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Sam Watson’s journal round-up for 27th March 2017

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 minimum legal drinking age and morbidity in the United States. Review of Economics and Statistics Published 23rd February 2017

Governments have tried multiple different policies to reduce the physical and social harms of alcohol consumption. In the United Kingdom, a minimum price per unit alcohol has been investigated recently, and in 2003 opening times for licensed premises were extended. Neither policy was overwhelmingly judged to be an effective way to reduce harms. In the United States, the legal minimum age for purchasing alcohol is 21, notably higher than other Western nations. This legal age resulted from the National Minimum Age Drinking Act of 1984, which threatened states with a reduction of 10% in their funding for federal highways if they did not raise the legal age to 21. The Act was ostensibly in response to evidence of increased traffic fatalities associated with a lower legal age. This study adds evidence to this ongoing debate. The legal cut-off provides a natural discontinuity for the authors to investigate. Regression discontinuity can be abused, with some researchers controlling inappropriately for high powers of the variable, ‘forcing’ a difference to appear. This paper takes a more sensible approach adopting a quadratic form. For some variables, such as ED admission for alcohol intoxication, the discontinuity is obvious, as you would expect. But for others, such as accidental injury or deliberate injury by another person, the difference is not so apparent if you ignore the fitted lines. One wonders then how much their effect size is driven by their functional form. The authors write that their model is to ‘determine if an increase in the morbidity rate visible in a figure is statistically significant’. Oh dear.  Theoretically, the effect makes sense, alcohol does lead to physical and social harms. But I’m not convinced by the magnitude of the effect they’ve estimated: some sensitivity analyses wouldn’t have gone amiss.

A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society: Series A Published 16th March 2017

Meta-analysis is the frequently used method to combine results from multiple studies. Evidence synthesis is frequently required in health economic analyses to estimate parameters for models. Practitioners typically either consider ‘fixed effects’ or ‘random effects’ meta-analysis. The latter is used when it is assumed the estimated effects differ between studies, leading many authors to shun fixed effects analyses if there’s any suspicion of heterogeneity. But, as this article argues, there are multiple interpretations of fixed effects analyses. They can provide useful results even in the presence of between study heterogeneity. There are three key assumptions about the parameters estimated in different studies. First, there could be the same common effect underlying all studies. Secondly, each study could have a its own separate fixed effect. Or thirdly, each estimate is a draw from an underlying sampling distribution, an exchangeable parameters assumption. This latter assumption is the basis of random effects meta-analysis. The fixed effects meta-analysis estimator is consistent for the common effect parameter. For the multiple fixed effects assumption the fixed effects meta-analysis is a consistent estimator for the parameter that would have been estimated if the samples in each study were amalgamated. The key point of the paper is that under both the common effect and fixed effects assumptions the fixed effects meta-analysis estimator is useful.

Insurer competition in health care markets. Econometrica. Published 21st March 2017.

Given the gestation length of an economics paper, it is perhaps somewhat fortuitous that this one should land just as major health care market legislation is being discussed in the US. Health care provision differs notably between the US and other high income countries. Health care is predominantly left up to the market with ‘consumers’ purchasing insurance or health care directly. This, despite it being long recognised that health care markets are likely to fail (see our recent piece on the late Kenneth Arrow). But a single payer system is politically unpalatable. The Affordable Care Act (ACA; Obamacare) aimed to ensure universal coverage of health care through a system of subsidies, regulations, and mandates. The ACA brought about changes to the insurance market with a number of providers merging and consolidating. The consequences of these mergers may be deliterious as increased monopoly power within states may lead to higher premiums, but equally increased monopsony power may mean lower prices negotiated with health care providers. This article attempts to simulate what will happen to premiums and health care prices when insurers of different sizes are removed from the market. I can’t give a fair review to the methods in the time I’ve had to read this paper as there is a lot going on including econometric models of household choice and game theoretic models of insurer bargaining. But I put it here as it appears at first glance to be a solid analysis of what is an incredibly large and complex market in the US and is likely worth more time to understand.



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