Chris Sampson’s journal round-up for 6th January 2020

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.

Child sleep and mother labour market outcomes. Journal of Health Economics [PubMed] [RePEc] Published January 2020

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.


Thesis Thursday: Caroline Chuard

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 Caroline Chuard who has a PhD from the University of Zurich. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Three essays on the health effects of family policies
Hannes Schwandt, Josef Zweimüller
Repository link

Is there a strong health economics evidence base on family policies?

The literature on parental leave and family health is relatively young. This literature emphasises that the returns depend on several key features. First, the timing of measurement matters. Therefore, the effects differ according to whether they are measured in the short- versus long-run. Second, the initial level of parental leave and the extent to which parental leave is increased are both key influencing factors. As such, an introduction is more beneficial than an increase at an already generous level of parental leave. Third, the results depend on the targeted group.

But keep in mind that the effects of family policies on health outcomes are just one part of a large literature that studies the effect on other outcomes such as maternal labour market outcomes, fertility, and child cognitive and non-cognitive development (e.g. Ruhm (2000), Lalive and Zweimüller (2009), Baker and Milligan (2008), Dustmann and Schönberg (2012), Lalive et al. (2014), Carneiro et al. (2015), Dahl et al. (2016), Danzer and Lavy (2018), Butikofer et al. (2018) and many more which have recently been reviewed by Olivetti and Petrongolo (2017) and Rossin-Slater (2018)).

What policy changes were you able to evaluate in your research?

I exploit two types of family policy changes in two countries. On the one hand, I use three changes in parental leave duration in Austria and, on the other hand, I use cantonal variation in family allowances across Switzerland.

More specifically, Austria increased parental leave by 1 year to 2 years in July 1990. This was partially reversed again in July 1996, by exclusively reserving 6 months to fathers so that maternal leave was essentially reduced to 1.5 years. Finally, in July 2000, there was another large extension in paid parental leave by 1 year to 2.5 years. Enforcement of all these changes was very strict, changing from one day to another depending on giving birth in June or July. This sharp discontinuity allows me to employ a regression discontinuity design.

In the case of Switzerland, I analyse the impact of birth allowances (so-called baby bonuses) on fertility, newborn health and birth scheduling. I exploit a unique quasi-experimental setting of Switzerland’s family allowances system. In this system, cantons are free to choose whether they want to implement birth allowances and how much they want to pay. During the last 50 years, 11 cantons have introduced a baby bonus, all increase the amount paid thereafter, and two cantons even abolished the baby bonus after all. This gives rise to a lot of cantonal variation. Thus, I use a difference-in-differences setting where I can analyse both the introduction and the intensity of the treatment.

What were the key strengths of the data sets that you used?

For all my studies I rely on administrative data. Thus, I can use the universe of observations delivered with high quality, as both Austria and Switzerland have very reliable administrative data.

In the Austrian case, I can even combine several different data sets. Namely, I use the Austrian Social Security Database (ASSD), which covers the complete working history of every worker in Austria. The ASSD covers every birth of employed mothers and their actual duration of parental leave. I can link the ASSD to the Austrian Birth Register (ABR) recording newborn health outcomes and additional individual-level characteristics of the mother. Finally, for a part of Austria, I additionally merge the data to health outcomes recorded in the health insurance data. This data set records every outpatient doctor visit, prescribed medication, and hospital stays including diagnosis code.

All of this, together, gives a huge variety of different variables on an individual basis allowing me to study a broad set of outcomes (such as health outcomes next to the directly targeted labour market outcomes). Furthermore, the detailed level of information allows me to study the impact of labour market behaviour on two margins—the extensive margin of mothers who choose to work or not and the intensive margin of how much mothers choose to work. The richness of the data also makes it possible to analyse heterogeneous effects across mothers and by work environment.

Did the policies achieve what they were designed to achieve?

This is a little hard to tell from looking at my results only. For example, in Austria the initial increase of parental leave duration by 1 year was introduced so that fathers could take up to 6 months of the full duration. This policy reform was a result of parliamentary procedural requests which wanted to introduce paternal leave. Due to the flat benefit structure almost no fathers were taking up parental leave, which essentially resulted in an increase of maternal leave from 1 to 2 years and, ultimately, led to the second policy change by exclusively reserving 6 months out of the total 2 years for fathers.

However, what I want to mention here, note that I explicitly evaluated side effects. All three chapters of my dissertation highlight the importance of studying alternative and indirect outcome measures in addition to the direct measures targeted by policymakers.

For example, in the Swiss study, we only find little fertility effects, the directly targeted outcome measure of birth allowances, but a sizable and significant reduction in the stillbirth rate as well as a positive impact on birth weight. A policymaker, who would now only study fertility, would argue that birth allowances were expensive to implement with little to no result, which, however, does not capture the full story.

Is there heterogeneity in how family policy reforms affect families?

The answer depends on the person affected and the studied outcome. For example, the Austrian parental leave duration reform affects maternal work behaviour during pregnancy regardless of the mother’s socioeconomic background and the industry. This change in prenatal maternal work status doesn’t affect newborn health at all.

However, when I study the same reforms with respect to maternal health, there is substantial heterogeneity. The initial increase in leave length is especially good for low-wage and unmarried mothers. Reducing leave duration harms mothers with unhealthy babies, proxied by a preterm birth or low birth weight baby. Substantially increasing leave duration is, though, especially bad for maternal health of those mothers who already suffered from mental diseases pre-birth. Also, for the paper on the Swiss baby bonus, we find a more beneficial impact in the decline of stillbirths for low socioeconomic status mothers.

Based on your research, how would you design parental leave policies?

With my research, I tried to give a more complete picture on the impact of family policies by taking into account health outcomes which have vastly been neglected so far. Nevertheless, for a policy recommendation it is crucial to take the findings from the previous literature into account.

Firstly, introducing parental leave has generally been shown to be very beneficial for the cognitive development of children (Carneiro et al., 2015). Secondly, these returns are, however, quickly declining (Butikofer et al., 2018). In combination with my findings of no impact of working during pregnancy on child health and a negative impact of too long parental leave policies for maternal health (Chuard, 2018), I would clearly put the focus on mandatory leave in the first months of a newborn’s life. While this might seem obvious for many European countries, this is still not the case in the US. And even Europe might face the risk on the other end of the parental leave duration scale. Many European countries tend to expand leave rather generously both pre- and post-natal, which seems from my research not necessary (always keep in mind, these policies are extremely expensive) and could potentially even be harmful in the long-run.

Chris Sampson’s journal round-up for 19th August 2019

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.

Paying for kidneys? A randomized survey and choice experiment. American Economic Review [RePEc] Published August 2019

This paper starts with a quote from Alvin Roth about ‘repugnant transactions’, of which markets for organs provide a prime example. This idea of ‘repugnant transactions’ has been hijacked by some pop economists to represent the stupid opinions of non-economists. If you ask me, markets for organs aren’t repugnant, they just seem like a very bad idea in terms of both efficiency and equity. But it doesn’t matter what I think; it matters what the people of the United States think.

The authors of this study conducted an online survey with a representative sample of 2,666 Americans. Each respondent was randomised to evaluate one of eight systems compared with the current system. The eight systems differed with respect to i) cash or non-cash compensation of ii) different sizes ($30,000 or $100,000), iii) paid by either a public agency or the organ recipient. Participants made five binary choices that differed according to the gain – in transplants generated – associated with the new system. Half of the participants were also asked to express moral judgements.

Both the system features (e.g. who pays) and the outcomes of the new system influenced people’s choices. Broadly speaking, the results suggest that people aren’t opposed to donors being paid, but are opposed to patients paying. (Remember, we’re talking about the US here!). Around 21% of respondents opposed payment no matter what, 46% were in favour no matter what, and 18% were sensitive to the gain in the number of transplants. A 10% point increase in transplants resulted in a 2.6% point increase in support. Unsurprisingly, individuals’ moral judgements were predictive of the attitudes they expressed, particularly with respect to fairness. The authors describe their results as exhibiting ‘strong polarisation’, which is surely inevitable for questions that involve moral judgement.

Being in AER, this is a long meandering paper with extensive analyses and thoroughly reported results. There’s lots of information and findings that I can’t share here. It’s a valuable study with plenty of food for thought, but I can’t help but think that it is, methodologically, a bit weak. If we want to understand the different views in society, surely some Q methodology would be more useful than a basic online survey. And if we want to elicit stated preferences, surely a discrete choice experiment with a well-thought-out efficient design would give us more meaningful results.

Estimating local need for mental healthcare to inform fair resource allocation in the NHS in England: cross-sectional analysis of national administrative data linked at person level. The British Journal of Psychiatry [PubMed] Published 8th August 2019

The need to fairly (and efficiently) allocate NHS resources across the country played an important part in the birth of health economics in the UK, and resulted in resource allocation formulas. Since 1996 there has been a separate formula for mental health services, which is periodically updated. This study describes the work undertaken for the latest update.

The model is based on predicting service use and total mental health care costs observed in 2015 from predictors in the years 2013-2014, to inform allocations in 2019-2024. Various individual-level data sources available to the NHS were used for 43.7 million people registered with a GP practice and over the age of 20. The cost per patient who used mental health services ranged from £94 to over one million, averaging around £2,000. The predictor variables included individual indicators such as age, sex, ethnicity, physical diagnoses, and household type (e.g. number of adults and kids). The model also used variables observed at the local or GP practice level, such as the proportion of people receiving out-of-work benefits and the distance from the mental health trust. All of this got plugged into a good old OLS regression. From individual-level predictions, the researchers created aggregated indices of need for each clinical commission group (CCG).

A lot went into the model, which explained 99% of the variation in costs between CCGs. A key way in which this model differs from previous versions is that it relies on individual-level indicators rather than those observed at the level of GP practice or CCG. There was a lot of variation in the CCG need indices, ranging from 0.65 for Surrey Heath to 1.62 for Southwark, where 1.00 is the average. You’ll need to check the online appendices for your own CCG’s level of need (Lewisham: 1.52). As one might expect, the researchers observed a strong correlation between a CCG’s need index and the CCG’s area’s level of deprivation. Compared with previous models, this new model indicates a greater allocation of resources to more deprived and older populations.

Measuring, valuing and including forgone childhood education and leisure time costs in economic evaluation: methods, challenges and the way forward. Social Science & Medicine [PubMed] Published 7th August 2019

I’m a ‘societal perspective’ sceptic, not because I don’t care about non-health outcomes (though I do care less) but because I think it’s impossible to capture everything that is of value to society, and that capturing just a few things will introduce a lot of bias and noise. I would also deny that time has any intrinsic value. But I do think we need to do a better job of evaluating interventions for children. So I expected this paper to provide me with a good mix of satisfaction and exasperation.

Health care often involves a loss of leisure or work time, which can constitute an opportunity cost and is regularly included in economic evaluations – usually proxied by wages – for adults. The authors outline the rationale for considering ‘time-related’ opportunity costs in economic evaluations and describe the nature of lost time for children. For adults, the distinction is generally between paid or unpaid work and leisure time. Arguably, this distinction is not applicable to children. Two literature reviews are described. One looked at economic evaluations in the context of children’s health, to see how researchers have valued lost time. The other sought to identify ideas about the value of lost time for children from a broader literature.

The authors do a nice job of outlining how difficult it is to capture non-health-related costs and outcomes in the context of childhood. There is a handful of economic evaluations that have tried to measure and value children’s foregone time. The valuations generally focussed on the costs of childcare rather than the costs to the child, though one looked at the rate of return to education. There wasn’t a lot to go off in the non-health literature, which mostly relates to adults. From what there is, the recommendation is to capture absence from formal education and foregone leisure time. Of course, consideration needs to be given to the importance of lost time and thus the value of capturing it in research. We also need to think about the risk of double counting. When it comes to measurement, we can probably use similar methods as we would for adults, such as diaries. But we need very different approaches to valuation. On this, the authors found very little in the way of good examples to follow. More research needed.