Brent Gibbons’s journal round-up for 9th April 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

The effect of Medicaid on management of depression: evidence from the Oregon Health Insurance Experiment. The Milbank Quarterly [PubMed] Published 5th March 2018

For the first journal article of this week’s AHE round-up, I selected a follow-up study on the Oregon health insurance experiment. The Oregon Health Insurance Experiment (OHIE) used a lottery system to expand Medicaid to low-income uninsured adults (and their associated households) who were previously ineligible for coverage. Those interested in being part of the study had to sign up. Individuals were then randomly selected through the lottery, after which individuals needed to take further action to complete enrollment in Medicaid, which included showing that enrollment criteria were satisfied (e.g. income below 100% of poverty line). These details are important because many who were selected for the lottery did not complete enrollment in Medicaid, though being selected through the lottery was associated with a 25 percentage point increase in the probability of having insurance (which the authors confirm was overwhelmingly due to Medicaid and not other insurance). More details on the study and data are publicly available. The OHIE is a seminal study in that it allows researchers to study the effects of having insurance in an experimental design – albeit in the U.S. health care system’s context. The other study that comes to mind is of course the famous RAND health insurance experiment that allowed researchers to study the effects of different levels of health insurance coverage. For the OHIE, the authors importantly point out that it is not necessarily obvious what the impact of having insurance is. While we would expect increases in health care utilization, it is possible that increases in primary care utilization could result in offsetting reductions in other settings (e.g. hospital or emergency department use). Also, while we would expect increases in health as a result of increases in health care use, it is possible that by reducing adverse financial consequences (e.g. of unhealthy behavior), health insurance could discourage investments in health. Medicaid has also been criticized by some as not very good insurance – though there are strong arguments to the contrary. First-year outcomes were detailed in another paper. These included increased health care utilization (across all settings), decreased out-of-pocket medical expenditures, decreased medical debt, improvements in self-reported physical and mental health, and decreased probability of screening positive for depression. In the follow-up paper on management of depression, the authors further explore the causal effect and causal pathway of having Medicaid on depression diagnosis, treatment, and symptoms. Outcomes of interest are the effect of having Medicaid on the prevalence of undiagnosed and untreated depression, the use of depression treatments including medication, and on self-reported depressive symptoms. Where possible, outcomes are examined for those with a prior depression diagnosis and those without. In order to examine the effect of Medicaid insurance (vs. being uninsured), the authors needed to control for the selection bias introduced from uncompleted enrollment into Medicaid. Instrumental variable 2SLS was used with lottery selection as the sole instrument. Local average treatment effects were reported with clustered standard errors on the household. The effect of Medicaid on the management of depression was overwhelmingly positive. For those with no prior depression diagnosis, it increased the chance of receiving a diagnosis and decreased the prevalence of undiagnosed depression (those who scored high on study survey depression instrument but with no official diagnosis). As far as treatment, Medicaid reduced the share of the population with untreated depression, virtually eliminating untreated depression among those with pre-lottery depression. There was a large reduction in unmet need for mental health treatment and an increased share who received specific mental health treatments (i.e. prescription drugs and talk therapy). For self-reported symptoms, Medicaid reduced the overall rate screened for depression symptoms in the post-lottery period. All effects were relatively strong in magnitude, giving an overall convincing picture that Medicaid increased access to treatment, which improved depression symptoms. The biggest limitation of this study is its generalizability. Much of the results were focused on the city of Portland, which may not represent more rural parts of the state. More importantly, this was limited to the state of Oregon for low-income adults who not only expressed interest in signing up, but who were able to follow through to complete enrollment. Other limitations were that the study only looked at the first two years of outcomes and that there was limited information on the types of treatments received.

Tobacco regulation and cost-benefit analysis: how should we value foregone consumer surplus? American Journal of Health Economics [PubMed] [RePEcPublished 23rd January 2018

This second article addresses a very interesting theoretical question in cost-benefit analysis, that has emerged in the context of tobacco regulation. The general question is how should foregone consumer surplus, in the form of reduced smoking, be valued? The history of this particular question in the context of recent FDA efforts to regulate smoking is quite fascinating. I highly recommend reading the article just for this background. In brief, the FDA issued proposed regulations to implement graphic warning labels on cigarettes in 2010 and more recently proposed that cigars and e-cigarettes should also be subject to FDA regulation. In both cases, an economic impact analysis was required and debates ensued on if, and how, foregone consumer surplus should be valued. Economists on both sides weighed-in, some arguing that the FDA should not consider foregone consumer surplus because smoking behavior is irrational, others arguing consumers are perfectly rational and informed and the full consumer surplus should be valued, and still others arguing that some consumer surplus should be counted but there is likely bounded rationality and that it is methodologically unclear how to perform a valuation in such a case. The authors helpfully break down the debate into the following questions: 1) if we assume consumers are fully informed and rational, what is the right approach? 2) are consumers fully informed and rational? and 3) if consumers are not fully informed and rational, what is the right approach? The reason the first question is important is that the FDA was conducting the economic impact analysis by examining health gains and foregone consumer surplus separately. However, if consumers are perfectly rational and informed, their preferences already account for health impacts, meaning that only changes in consumer surplus should be counted. On the second question, the authors explore the literature on smoking behavior to understand “whether consumers are rational in the sense of reflecting stable preferences that fully take into account the available information on current and expected future consequences of current choices.” In general, the literature shows that consumers are pretty well aware of the risks, though they may underestimate the difficulty of quitting. On whether consumers are rational is a much harder question. The authors explore different rational addiction models, including quasi-rational addiction models that take into account more recent developments in behavioral economics, but declare that the literature at this point provides no clear answer and that no empirical test exists to distinguish between rational and quasi-rational models. Without answering whether consumers are fully informed and rational, the authors suggest that welfare analysis – even in the face of bounded rationality – can still use a similar valuation approach to consumer surplus as was recommended for when consumers are fully informed and rational. A series of simple supply and demand curves are presented where there is a biased demand curve (demand under bounded rationality) and an unbiased demand curve (demand where fully informed and rational) and different regulations are illustrated. The implication is that rather than trying to estimate health gains as a result of regulations, what is needed is to understand the amount of demand bias as result of bounded rationality. Foregone consumer surplus can then be appropriately measured. Of course, more research is needed to estimate if, and how much, ‘demand bias’ or bounded rationality exists. The framework of the paper is extremely useful and it pushes health economists to consider advances that have been made in environmental economics to account for bounded rationality in cost-benefit analysis.

2SLS versus 2SRI: appropriate methods for rare outcomes and/or rare exposures. Health Economics [PubMed] Published 26th March 2018

This third paper I will touch on only briefly, but I wanted to include it as it addresses an important methodological topic. The paper explores several alternative instrumental variable estimation techniques for situations when the treatment (exposure) variable is binary, compared to the common 2SLS (two-stage least squares) estimation technique which was developed for a linear setting with continuous endogenous treatments and outcome measures. A more flexible approach, referred to as 2SRI (two-stage residual inclusion) allows for non-linear estimation methods in the first stage (and second stage), including logit or probit estimation methods. As the title suggests, these alternative estimation methods may be particularly useful when treatment (exposure) and/or outcomes are rare (e.g below 5%). Monte Carlo simulations are performed on what the authors term ‘the simplest case’ where the outcome, treatment, and instrument are binary variables and a range of results are considered as the treatment and/or outcome become rarer. Model bias and consistency are assessed in the ability to produce average treatment effects (ATEs) and local average treatment effects (LATEs), comparing the 2SLS, several forms of probit-probit 2SRI models, and a bivariate probit model. Results are that the 2SLS produced biased estimates of the ATE, especially as treatment and outcomes become rarer. The 2SRI models had substantially higher bias than the bivariate probit in producing ATEs (though the bivariate probit requires the assumption of bivariate normality). For LATE, 2SLS always produces consistent estimates, even if the linear probability model produces out of range predictions. Estimates for 2SRI models and the bivariate probit model were biased in producing LATEs. An empirical example was also tested with data on the impact of long-term care insurance on long-term care use. Conclusions are that 2SRI models do not dependably produce unbiased estimates of ATEs. Among the 2SRI models though, there were varying levels of bias and the 2SRI model with generalized residuals appeared to produce the least ATE bias. For more rare treatments and outcomes, the 2SRI model with Anscombe residuals generated the least ATE bias. Results were similar to another simulation study by Chapman and Brooks. The study enhances our understanding of how different instrumental variable estimation methods may function under conditions where treatment and outcome variables have nonlinear distributions and where those same treatments and outcomes are rare. In general, the authors give a cautionary note to say that there is not one perfect estimation method in these types of conditions and that researchers should be aware of the potential pitfalls of different estimation methods.



Sam Watson’s journal round-up for 12th February 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Tuskegee and the health of black men. The Quarterly Journal of Economics [RePEc] Published February 2018

In 1932, a study often considered the most infamous and potentially most unethical in U.S. medical history began. Researchers in Alabama enrolled impoverished black men in a research program designed to examine the effects of syphilis under the guise of receiving government-funded health care. The study was known as the Tuskegee syphilis experiment. For 40 years the research subjects were not informed they had syphilis nor were they treated, even after penicillin was shown to be effective. The study was terminated in 1972 after its details were leaked to the press; numerous men died, 40 wives contracted syphilis, and a number of children were born with congenital syphilis. It is no surprise then that there is distrust among African Americans in the medical system. The aim of this article is to examine whether the distrust engendered by the Tuskegee study could have contributed to the significant differences in health outcomes between black males and other groups. To derive a causal estimate the study makes use of a number of differences: black vs non-black, for obvious reasons; male vs female, since the study targeted males, and also since women were more likely to have had contact with and hence higher trust in the medical system; before vs after; and geographic differences, since proximity to the location of the study may be informative about trust in the local health care facilities. A wide variety of further checks reinforce the conclusions that the study led to a reduction in health care utilisation among black men of around 20%. The effect is particularly pronounced in those with low education and income. Beyond elucidating the indirect harms caused by this most heinous of studies, it illustrates the importance of trust in mediating the effectiveness of public institutions. Poor reputations caused by negligence and malpractice can spread far and wide – the mid-Staffordshire hospital scandal may be just such an example.

The economic consequences of hospital admissions. American Economic Review [RePEcPublished February 2018

That this paper’s title recalls that of Keynes’s book The Economic Consequences of the Peace is to my mind no mistake. Keynes argued that a generous and equitable post-war settlement was required to ensure peace and economic well-being in Europe. The slow ‘economic privation’ driven by the punitive measures and imposed austerity of the Treaty of Versailles would lead to crisis. Keynes was evidently highly critical of the conference that led to the Treaty and resigned in protest before its end. But what does this have to do with hospital admissions? Using an ‘event study’ approach – in essence regressing the outcome of interest on covariates including indicators of time relative to an event – the paper examines the impact hospital admissions have on a range of economic outcomes. The authors find that for insured non-elderly adults “hospital admissions increase out-of-pocket medical spending, unpaid medical bills, and bankruptcy, and reduce earnings, income, access to credit, and consumer borrowing.” Similarly, they estimate that hospital admissions among this same group are responsible for around 4% of bankruptcies annually. These losses are often not insured, but they note that in a number of European countries the social welfare system does provide assistance for lost wages in the event of hospital admission. Certainly, this could be construed as economic privation brought about by a lack of generosity of the state. Nevertheless, it also reinforces the fact that negative health shocks can have adverse consequences through a person’s life beyond those directly caused by the need for medical care.

Is health care infected by Baumol’s cost disease? Test of a new model. Health Economics [PubMed] [RePEcPublished 9th February 2018

A few years ago we discussed Baumol’s theory of the ‘cost disease’ and an empirical study trying to identify it. In brief, the theory supposes that spending on health care (and other labour-intensive or creative industries) as a proportion of GDP increases, at least in part, because these sectors experience the least productivity growth. Productivity increases the fastest in sectors like manufacturing and remuneration increases as a result. However, this would lead to wages in the most productive sectors outstripping those in the ‘stagnant’ sectors. For example, salaries for doctors would end up being less than those for low-skilled factory work. Wages, therefore, increase in the stagnant sectors despite a lack of productivity growth. The consequence of all this is that as GDP grows, the proportion spent on stagnant sectors increases, but importantly the absolute amount spent on the productive sectors does not decrease. The share of the pie gets bigger but the pie is growing at least as fast, as it were. To test this, this article starts with a theoretic two-sector model to develop some testable predictions. In particular, the authors posit that the cost disease implies: (i) productivity is related to the share of labour in the health sector, and (ii) productivity is related to the ratio of prices in the health and non-health sectors. Using data from 28 OECD countries between 1995 and 2016 as well as further data on US industry group, they find no evidence to support these predictions, nor others generated by their model. One reason for this could be that wages in the last ten years or more have not risen in line with productivity in manufacturing or other ‘productive’ sectors, or that productivity has indeed increased as fast as the rest of the economy in the health care sector. Indeed, we have discussed productivity growth in the health sector in England and Wales previously. The cost disease may well then not be a cause of rising health care costs – nevertheless, health care need is rising and we should still expect costs to rise concordantly.


Thesis Thursday: Mathilde Péron

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Mathilde Péron who graduated with a PhD from Université Paris Dauphine. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Three essays on supplementary health insurance
Brigitte Dormont
Repository link

How important is supplementary health insurance in France, compared with other countries?

In France in 2016, Supplementary Health Insurance (SHI) financed 13.3% of total health care expenditure. SHI supplements a partial mandatory coverage by covering co-payments as well as medical goods and services outside the public benefit package, such as dental and optical care or balance billing. SHI is not a French singularity. Canada, Austria, Switzerland, the US (with Medicare / Medigap) or the UK do offer voluntary SHI contracts. A remarkable fact, however, is that 95% of the French population is covered by a SHI contract. In comparison, although the extent of public coverage is very similar in France and in the UK, the percentage of British patients enrolled in a private medical insurance is below 15%.

The large SHI enrolment and the subsequent limited out-of-pocket payments – €230 per year on average, the lowest among EU countries – should not hide important inequalities in the extent of coverage and premiums paid. SHI coverage is now mandatory for employees of the private sector. They benefit from subsidized contracts and uniform premiums. Individuals with an annual income below €8,700 benefit from free basic SHI coverage which covers copayments, essentially. However, the rest of the population (students, temporary workers, unemployed, retirees, independent, and civil servants) buy SHI in a competitive market where premiums generally increase with age.

Can supplementary health insurance markets lead to an adverse selection death spiral?

Competitive health insurance markets are subject to asymmetric information that prevent the existence of pooling contracts (Rothschild and Stiglitz, 1976Cutler and Zeckhauser, 1998). The US market is a good example; in the 1950s not-for-profit insurance companies (Blue Cross, Blue Shields) – which offered pooled contracts – almost all disappeared (Thomasson, 2002). And, despite a notably higher public coverage that could limit adverse selection effects, the French SHI market is not an exception.

Historically, SHI coverage was provided by not-for-profit insurers, the Mutuelles, who relied on solidarity principles. But as the competition becomes more intense, the Mutuelles experience the adverse selection death spiral; they lose their low-risk clients attracted by lower premiums. To survive, they have to give up on uniform premiums and standardized coverage. Today 90% of SHI contracts in the individual market have premiums that increase with age. It is worth noting that in France insurers have strong fiscal incentives to avoid medical underwriting, so age remains the only predictor for individual risk. Still, premiums can vary with a ratio of 1 to 3, which raises legitimate concerns about the affordability of insurance and access to health care for patients with increasing medical needs.

How does supplementary health insurance influence prices in health care, and how did you measure this in your research?

A real policy concern is that SHI might have an inflationary effect by allowing patients to consume more at higher prices. Access to specialists who balance bill (i.e. charge more than the regulated fee) – a signal for higher quality and reduced waiting times – is a good example (Dormont and Peron, 2016).

To measure the causal impact of SHI on balance billing consumption we use original individual-level data, collected from the administrative claims of a French insurer. We observe balance billing consumption and both mandatory and SHI reimbursements for 43,111 individuals from 2010 to 2012. In 2010, the whole sample was covered by the same SHI contract, which does not cover balance billing. We observe the sample again in 2012 after that 3,819 among them decided to switch to other supplementary insurers, which we assume covers balance billing. We deal with the endogeneity of the decision to switch by introducing individual effects into the specifications and by using instrumental variables for the estimation.

We find that individuals respond to better coverage by increasing their proportion of visits to a specialist who balance bills by 9%, resulting in a 32% increase in the amount of balance billing per visit. This substitution to more expensive care is likely to encourage the rise in medical prices.

Does the effect of supplementary insurance on health care consumption differ according to people’s characteristics?

An important result is that the magnitude of the impact of SHI on balance billing strongly depends on the availability of specialists. We find no evidence of moral hazard in areas where specialists who do not charge balance billing are readily accessible. On the contrary, in areas where they are scarce, better coverage is associated with a 47% increase in the average amount of balance billing per consultation. This result suggests that the most appropriate policy to contain medical prices is not necessarily to limit SHI coverage but to monitor the supply of care in order to guarantee patients a genuine choice of their physicians.

We further investigate the heterogeneous impact of SHI in a model where we specify individual heterogeneity in moral hazard and consider its possible correlation with coverage choices (Peron and Dormont, 2017 [PDF]). We find evidence of selection on moral hazard: individuals with unobserved characteristics that make them more likely to ask for comprehensive SHI show a larger increase in balance billing per visit. This selection effect is likely to worsen the inflationary impact of SHI. On the other hand, we also find that the impact of a better coverage is larger for low-income people, suggesting that SHI plays a role in access to care.

Have the findings from your PhD research influenced your own decision to buy supplementary health insurance?

As an economist, it’s interesting to reflect on your own decisions, isn’t it? Well, I master cost-benefit analysis, I have a good understanding of expected utility and definitely more information than the average consumer in the health insurance market. Still, my choice of SHI might appear quite irrational. I’m (reasonably) young and healthy, I could have easily switched to a contract with lower premiums and higher benefits, but I did not. I stayed with a contract where premiums mainly depend on income and benefits are standardized, an increasingly rare feature in the market. I guess that stresses out the importance of other factors in my decision to buy SHI, my inertia as a consumer, probably, but also my willingness to pay for solidarity.