from The Academic Health Economists’ Blo… at http://bit.ly/2DmxPmw on March 12, 2018 at 12:00PM
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.
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How does household income affect child personality traits and behaviors? American Economic Review [RePEc] Published February 2018
The intergenerational transmission of poor social and health outcomes and its remediation has long been of concern to policy makers and economists alike. A popular hypothesis to explain this phenomenon is that of fetal origins: the nine months in the womb are perhaps the most important in determining a person’s health over their lifetime. We have featured numerous papers on this blog looking at the impact of in utero conditions on infant, child, and adult outcomes. This hypothesis though leaves a sense of pessimism since if this generational link is rooted in biology then it is not likely to be modifiable by any intervention. Studies of institutional interventions in schools and the health care system have shown that the health of children from impoverished households can be improved. But what about the effects of simply improving the material conditions of those households? Would this have an effect? This study uses a longitudinal dataset of children in North Carolina, USA which oversampled children from Native American families who, in the middle of the period of observation, began to receive an unconditional cash transfer from the tribal government funded by casino revenues. A difference-in-difference-in-differences model is used with the relevant differences being: before v. after, younger cohorts v. older cohorts (older children’s households did not receive the cash while they were children), and Native American v non-Native American. An ‘event study analysis’ is also used, which takes into account time from the intervention. (This is the exact same method as another recently featured paper on this blog – perhaps sign of the growing popularity of such techniques). Average annual income increased by around $3,500 per year. Quite clear improvements in a range of psychological traits are estimated from the models including increases in conscientiousness and agreeableness, and declines in emotional and behavioural disorders. Potential mediating mechanisms for these changes are explored and uncertain evidence is shown indicating improved parental supervision and interaction and a reduction in parental mental health care seeking (they plot 90% confidence intervals which appear ‘statistically significant’ where 95% confidence intervals clearly would not be – however, the lack of significance stars and p-values is refreshing). Such evidence should weigh heavily on policy makers’ minds when implementing reductions to social assistance programs and household income.
Adaptation or recovery after health shocks? Evidence using subjective and objective health measures. Health Economics. [PubMed] Published March 2018.
Hedonic adaptation is a well evidenced phenomenon in health economics and related fields. Individuals can get used to health conditions and adverse circumstances, such as amputation or blindness, and recover much of their pre-illness quality of life. This makes it hard for healthy people to judge the quality of life of these conditions and is one of the reasons for the divergence in preferences over health states depending on who you ask. This paper takes an interesting approach to looking at adaptation by asking whether the improvement in someone’s subjective assessment of their own life expectancy after a serious illness is reflective of actual recovery or is in fact due to the optimism brought on by adaptation. Typically, beliefs about life expectancy are found to accord well with actuarial assessments of life expectancy, but little is known about how this relates to serious illness. This study suggests that subjective assessments of mortality risk do drop with cancer, stroke, and myocardial infarction in line with changes to objective risk of death. However, these subjective assessments generally return to their pre-illness levels, which doesn’t reflect the continued increase in risk actually faced by these people. An explanation for this is hedonic adaptation – people perhaps end up feeling as well as they did before even if they are not. It’s hard to say though if there’s a survivorship bias in favour of the optimists.
The local influence of pioneer investigators on technology adoption: Evidence from new cancer drugs. Review of Economics and Statistics. [RePEc] Published March 2018.
Technology diffusion typically shows a strong spatial pattern. If you know someone who has adopted a new technology, you are more likely to do the same yourself. But what about in medicine – do doctors also adopt similar patterns of prescribing new drugs? In the UK, we might think such patterns are unlikely as doctors are not free to prescribe what they like since they are restricted generally to what the NHS will reimburse. New technologies have to be first approved on the basis of being demonstrably cost-effective. But in the United States doctors are freer to prescribe what they like. While this has benefits, it also leads to adoption of cost-ineffective interventions or persistence in prescribing sub-optimal treatments. If the diffusion of new treatments is based upon social and professional spatial networks then one might expect the epicentre to be where the drug was trialled, the PI may well also be the loudest cheerleader for the new drug should it be shown to be effective. Indeed if a ‘superstar’ researcher is involved with the development of a drug this may attract more attention to it still. The key finding from this study in the US is that patients treated in the hospital market where the first author of the paper reporting the results of the main clinical trial of a drug were 36% more likely to receive the drug than elsewhere in the first two years. This is generally beneficial to patients in those areas, particularly since the average survival benefit to those patients is larger than is attributable to the drug itself, which may suggest that doctors with local information are better at selecting which patients will benefit from a treatment. However, with some of the problems arising from reporting bias, p-values, and the like patients may also be getting a worse deal should the drug not be as good as claimed.