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.
It’s pretty clear that sleep is important to almost all aspects of our lives and our well-being. So it is perhaps surprising that economists have paid relatively little attention to the ways in which the quality of sleep influences the ‘economic’ aspects of our lives. Part of the explanation might be that almost anything that you can imagine having an effect on your sleep is also likely to be affected by your sleep. Identifying causality is a challenge. This paper shows us how it’s done.
The study is focussed on the relationship between sleep and labour market outcomes in new mothers. There’s good reason to care about new mothers’ sleep because many new mothers report that lack of sleep is a problem and many suffer from mental and physical health problems that might relate to this. But the major benefit to this study is that the context provides a very nice instrument to help identify causality – children’s sleep. The study uses data from the Avon Longitudinal Study of Parents and Children (ALSPAC), which seems like an impressive data set. The study recruited 14,541 pregnant women with due dates between 1991 and 1993, collecting data on mothers’ and children’s sleep quality and mothers’ labour market activity. The authors demonstrate that children’s sleep (in terms of duration and disturbances) affects the amount of sleep that mothers get. No surprise there. They then demonstrate that the amount of sleep that mothers get affects their labour market outcomes, in terms of their likelihood of being in employment, the number of hours they work, and household income. The authors also demonstrate that children’s sleep quality does not have a direct impact on mothers’ labour market outcomes except through its effect on mothers’ sleep. The causal mechanism seems difficult to refute.
Using a two-stage least squares model with a child’s sleep as an instrument for their mother’s sleep, the authors estimate the effect of mothers’ sleep on labour market outcomes. On average, a 30-minute increase in a mother’s sleep duration increases the number of hours she works by 8.3% and increases household income by 3.1%. But the study goes further (much further) by identifying the potential mechanisms for this effect, with numerous exploratory analyses. Less sleep makes mothers more likely to self-report having problems at work. It also makes mothers less likely to work full-time. Going even further, the authors test the impact of the UK Employment Rights Act 1996, which gave mothers the right to request flexible working. The effect of the Act was to reduce the impact of mothers’ sleep duration on labour market outcomes, with a 6 percentage points lower probability that mothers drop out of the labour force.
My only criticism of this paper is that the copy-editing is pretty poor! There are so many things in this study that are interesting in their own right but also signal need for further research. Unsurprisingly, the study identifies gender inequalities. No wonder men’s wages increase while women’s plateau. Personally, I don’t much care about labour market outcomes except insofar as they affect individuals’ well-being. Thanks to the impressive data set, the study can also show that the impact on women’s labour market outcomes is not simply a response to changing priorities with respect to work, implying that it is actually a problem. The study provides a lot of food for thought for policy-makers.
Health years in total: a new health objective function for cost-effectiveness analysis. Value in Health Published 23rd December 2019
It’s common for me to complain about papers on this blog, usually in relation to one of my (many) pet peeves. This paper is in a different category. It’s dangerous. I’m angry.
The authors introduce the concept of ‘health years in total’. It’s a simple idea that involves separating the QA and the LY parts of the QALY in order to make quality of life and life years additive instead of multiplicative. This creates the possibility of attaching value to life years over and above their value in terms of the quality of life that is experienced in them. ‘Health years’ can be generated at a rate of two per year because each life year is worth 1 and that 1 is added to what the authors call a ‘modified QALY’. This ‘modified QALY’ is based on the supposition that the number of life years in its estimation corresponds to the maximum number of life years available under any treatment scenario being considered. So, if treatment A provides 2 life years and treatment B provides 3 life years, you multiply the quality of life value of treatment A by 3 years and then add the number of actual life years (i.e. 2). On the face of it, this is as stupid as it sounds.
So why do it? Well, some people don’t like QALYs. A cabal of organisations, supposedly representing patients, has sought to undermine the use of cost-effectiveness analysis. For whatever reason, they have decided to pursue the argument that the QALY discriminates against people with disabilities, or anybody else who happens to be unwell. Depending on the scenario this is either untrue or patently desirable. But the authors of this paper seem happy to entertain the cabal. The foundation for the development of the ‘health years in total’ framework is explicitly based in the equity arguments forwarded by these groups. It’s designed to be a more meaningful alternative to the ‘equal value of life’ measure; a measure that has been used in the US context, which adds a value of 1 to life years regardless of their quality.
The paper does a nice job of illustrating the ‘health years in total’ approach compared with the QALY approach and the ‘equal value of life’ approach. There’s merit in considering alternatives to the QALY model, and there may be value in an ‘additive’ approach that in some way separates the valuation of life years from the valuation of health states. There may even be some ethical justification for the ‘health years in total’ framework. But, if there is, it isn’t provided by this paper. To frame the QALY as discriminatory in the way that the authors do, describing this feature as a ‘limitation’ of the QALY approach, and to present an alternative with no basis in ethics is, at best, foolish. In practice, the ‘health years in total’ calculation would favour life-extending treatments over those that improve health. There are some organisations with vested interests in this. Expect to see ‘health years in total’ obscuring decision-making in the United States in the near future.
The causal effect of education on chronic health conditions in the UK. Journal of Health Economics Published 23rd December 2019
Since the dawn of health economics, researchers have been interested in the ways in which education and health outcomes depend on one another. People with more education tend to be healthier. But identifying causal relationships in this context is almost impossible. Some studies have claimed that education has a positive (causal) effect on both general and specific health outcomes. But there are just as many studies that show no impact. This study attempts to solve the problem by throwing a lot of data at it.
The authors analyse the impact of two sets of reforms in the UK. First, the raising of the school leaving age in 1972, from 15 to 16 years. Second, the broader set of reforms that were implemented in the 1990s that resulted in a major increase in the number of people entering higher education. The study’s weapon is the Quarterly Labour Force Survey (QLFS), which includes over 5 million observations from 1.5 million people. Part of the challenge of identifying the impact of education on health outcomes is that the effects can be expected to be observed over the long-term and can therefore be obscured by other long-term trends. To address this, the authors limit their analyses to people in narrow age ranges in correspondence with the times of the reforms. Thanks to the size of the data set, they still have more than 350,000 observations for each reform. The QLFS asks people to self-report having any of a set of 17 different chronic health conditions. These can be grouped in a variety of ways, or looked at individually. The analysis uses a regression discontinuity framework to test the impact of raising the school leaving age, with birth date acting as an instrument for the number of years spent in education. The analysis of the second reform is less precise, as there is no single discontinuity, so the model identifies variation between the relevant cohorts over the period. The models are used to test a variety of combinations of the chronic condition indicators.
In short, the study finds that education does not seem to have a causal effect on health, in terms of the number of chronic conditions or the probability of having any chronic condition. But, even with their massive data set, the authors cannot exclude the possibility that education does have an effect on health (whether positive or negative). This non-finding is consistent across both reforms and is robust to various specifications. There is one potentially important exception to this. Diabetes. Looking at the school leaving age reform, an additional year of schooling reduces the likelihood of having diabetes by 3.6 percentage points. Given the potential for diabetes to depend heavily on an individual’s behaviour and choices, this seems to make sense. Kids, stay in school. Just don’t do it for the good of your health.