Thesis Thursday: Luke Wilson

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 Luke Wilson who has a PhD from Lancaster University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Essays on the economics of alcohol and risky behaviours
Supervisors
Colin P. Green, Bruce Hollingsworth, Céu Caixeiro Mateus
Repository link
https://doi.org/10.17635/lancaster/thesis/636

What inspired your research and how did ‘attractiveness’ enter the picture?

Without trying to sound like I have a problem, I find the subject of alcohol fascinating. The history of it, how it is perceived in society, how our behaviours around it have changed over time, not to mention it tastes pretty damn good!

Our attitude to alcohol is fascinating and diverse. Over 6.5 million people have visited Munich in the last month alone to attend the world’s largest beer festival Oktoberfest, drinking more than 7.3 million litres of beer. However, 2020 will be the 100-year anniversary of the introduction of prohibition in the United States. Throughout history, alcohol consumption has been portrayed as both a positive and negative commodity in society.

For my thesis, I wanted to understand individuals’ current attitudes to drinking alcohol; whether they are affected by legal restrictions such as being constrained by the minimum legal drinking age of 18 in the UK, whether their attitudes have changed over their life course, and how alcohol fits among a wider variety of risky behaviours such as smoking and illicit drug use.

As for how did ‘attractiveness’ enter the picture, I was searching for datasets that allow for longitudinal analysis, as well as contain information on risky behaviours, and I stumbled upon the data that asked the interviewers to rate the attractiveness of the respondent. My first thought was what a barbaric question to ask, but I quickly realised that the question is used a lot in determining the ‘beauty premia’ in the labour market. However, nobody has examined how these ‘beauty premia’ might come into effect while still at school.

Are people perceived to be more attractive at an advantage or a disadvantage in this context?

The current literature provides a compelling view that there are sizeable labour market returns to attractiveness in the United States (Fletcher, 2009; Stinebrickener et al., 2019). What is not well understood, and where our research fits in, is how physical attractiveness influences earlier, consequential, decisions. The previous literature seeks to provide, in essence, the effect of attractiveness on labour market outcomes conditional on individual characteristics, both demographic and ‘pre-market’. However, attractiveness is also likely to change both the opportunities and costs of a variety of behaviours during adolescence.

Exploiting the interviewer variations in ratings of attractiveness, we found that attractiveness of adolescents has marked effects on a range of risky behaviours. For instance, more attractive teens are less likely to smoke than teens of average or than lower attractiveness teens. However, attractiveness is associated with higher teen alcohol consumption. Attractive females, in particular, are substantially more likely to have consumed alcohol in the past twelve months, than those of or below average attractiveness.

How did you model the role of the minimum legal drinking age in the UK?

I was highly unoriginal and estimated the effect of the minimum legal drinking age in the UK using a regression discontinuity design approach, like that of Carpenter and Dobkin (2009). I jest but it is one of the most effective ways to estimate a causal effect of a particular law/policy that is triggered by age, especially for the UK which has not changed its legal drinking age.

Where our research deviates is that we focus on the law itself and analyse how an individual’s consumption of alcohol in a particular school year may differ at the cut-off (aged 18). For example, do those born in September purchase alcohol for themselves and their younger friends or do we all adhere to the laws that govern us and wait patiently…

Are younger people drinking less, nowadays?

The short answer is yes! Evidence from multiple British surveys shows a consistent pattern over 10-15 years of reduced participation in drinking, reduced consumption levels among drinkers, reduced prevalence of drunkenness, and less positive attitudes towards alcohol in young adults aged 16 to 24.

Friends of mine at the University of Sheffield (Oldham et al., 2018) have sought to unravel the decline in youth drinking further and find evidence that younger drinkers are consuming alcohol less often and in smaller quantities. They find that, among those who were drinkers, the percentage of 16-24 year-olds who drank in the last week fell from 76% to 60% between 2002 and 2016, while for 11-15 year-olds it fell from 35% to 19%. Additionally, alongside declines in youth drinking, the proportion of young adults who had ever tried smoking fell from 43% in 1998 to 17% in 2016.

While we are witnessing this decline, the jury is still out as to why it is happening. Explanations so far include that increases in internet use (social media) and online gaming are changing the way young people spend their leisure time. Additionally, economic factors may play a role, such as the increase in the cost of alcohol, as well as the increase in tuition fees and housing costs meaning that young adults have less disposable income.

What were some of the key methodological challenges you faced in your research?

The largest methodological problem I faced throughout my PhD was finding suitable data to examine the effect of the minimum legal drinking age in the setting of the UK. One of the key underlying components in a regression discontinuity design is the running variable. The running variable I use is age in months of the respondents, which is calculated using the date in which the survey interview took place as well as the month and year of birth of the respondent. Unfortunately, due to issues with data being disclosive, it is very difficult to obtain data that have these variables as well as suitable questions regarding alcohol consumption. Luckily, the General Household Survey (Special Licence version) had the variables I needed to conduct the analysis, albeit only between 1998 and 2007.

How might your research inform policymakers seeking to discourage risky behaviours?

Definitely a difficult question to answer, especially given that one of my chapters uses interviewer variations in ratings of attractiveness of the respondents, so I have stayed well clear from drawing individual policy recommendations from that chapter. That said, these results are important for a number of interrelated reasons. Previous labour market research demonstrates marked effects of attractiveness. My results suggest that important pre-market effects of attractiveness on individual behaviour are likely to be consequential for both labour market performance and important pre-market investments. In this sense, the findings suggest that physical attractiveness provides another avenue for understanding non-cognitive traits that are important in child and adolescent development and carry lifetime consequences.

The chapter on the minimum legal drinking age provides intriguing results regarding the effectiveness of policies that impose limits on ‘consumption’ through age-restrictive policies; whether they are enough on their own or merely delay consumption. This is especially relevant given that there is currently a discussion about increasing the minimum legal tobacco purchasing age to 21 and increasing the age in which you can buy a national lottery ticket from age 16 to 18 in the UK.

Sam Watson’s journal round-up for 26th November 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.

Alcohol and self-control: a field experiment in India. American Economic Review Forthcoming

Addiction is complex. For many people it is characterised by a need or compulsion to take something, often to prevent withdrawal, often in conflict with a desire to not take it. This conflicts with Gary Becker’s much-maligned rational theory of addiction, which views the addiction as a choice to maximise utility in the long term. Under Becker’s model, one could use market-based mechanisms to end repeated, long-term drug or alcohol use. By making the cost of continuing to use higher then people would choose to stop. This has led to the development of interventions like conditional payment or cost mechanisms: a user would receive a payment on condition of sobriety. Previous studies, however, have found little evidence people would be willing to pay for such sobriety contracts. This article reports a randomised trial among rickshaw drivers in Chennai, India, a group of people with a high prevalence of high alcohol use and dependency. The three trial arms consisted of a control arm who received an unconditional daily payment, a treatment arm who received a small payment plus extra if they passed a breathalyser test, and a third arm who had the choice between either of the two payment mechanisms. Two findings are of much interest. First, the incentive payments significantly increased daytime sobriety, and second, over half the participants preferred the conditional sobriety payments over the unconditional payments when they were weakly dominated, and a third still preferred them even when the unconditional payments were higher than the maximum possible conditional payment. This conflicts with a market-based conception of addiction and its treatment. Indeed, the nature of addiction means it can override all intrinsic motivation to stop, or do anything else frankly. So it makes sense that individuals are willing to pay for extrinsic motivation, which in this case did make a difference.

Heterogeneity in long term health outcomes of migrants within Italy. Journal of Health Economics [PubMed] [RePEc] Published 2nd November 2018

We’ve discussed neighbourhood effects a number of times on this blog (here and here, for example). In the absence of a randomised allocation to different neighbourhoods or areas, it is very difficult to discern why people living there or who have moved there might be better or worse off than elsewhere. This article is another neighbourhood effects analysis, this time framed through the lens of immigration. It looks at those who migrated within Italy in the 1970s during a period of large northward population movements. The authors, in essence, identify the average health and mental health of people who moved to different regions conditional on duration spent in origin destinations and a range of other factors. The analysis is conceptually similar to that of two papers we discussed at length on internal migration in the US and labour market outcomes in that it accounts for the duration of ‘exposure’ to poorer areas and differences between destinations. In the case of the labour market outcomes papers, the analysis couldn’t really differentiate between a causal effect of a neighbourhood increasing human capital, differences in labour market conditions, and unobserved heterogeneity between migrating people and families. Now this article examining Italian migration looks at health outcomes, such as the SF-12, which limit the explanations since one cannot ‘earn’ more health by moving elsewhere. Nevertheless, the labour market can still impact upon health strongly.

The authors carefully discuss the difficulties in identifying causal effects here. A number of model extensions are also estimated to try to deal with some issues discussed. This includes a type of propensity score weighting approach, although I would emphasize that this categorically does not deal with issues of unobserved heterogeneity. A finite mixture model is also estimated. Generally a well-thought-through analysis. However, there is a reliance on statistical significance here. I know I do bang on about statistical significance a lot, but it is widely used inappropriately. A rule of thumb I’ve adopted for reviewing papers for journals is that if the conclusions would change if you changed the statistical significance threshold then there’s probably an issue. This article would fail that test. They use a threshold of p<0.10 which seems inappropriate for an analysis with a sample size in the tens of thousands and they build a concluding narrative around what is and isn’t statistically significant. This is not to detract from the analysis, merely its interpretation. In future, this could be helped by banning asterisks in tables, like the AER has done, or better yet developing submission guidelines around its use.

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Sam Watson’s journal round-up for 16th 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 impact of NHS expenditure on health outcomes in England: alternative approaches to identification in all‐cause and disease specific models of mortality. Health Economics [PubMedPublished 2nd April 2018

Studies looking at the relationship between health care expenditure and patient outcomes have exploded in popularity. A recent systematic review identified 65 studies by 2014 on the topic – and recent experience from these journal round-ups suggests this number has increased significantly since then. The relationship between national spending and health outcomes is important to inform policy and health care budgets, not least through the specification of a cost-effectiveness threshold. Karl Claxton and colleagues released a big study looking at all the programmes of care in the NHS in 2015 purporting to estimate exactly this. I wrote at the time that: (i) these estimates are only truly an opportunity cost if the health service is allocatively efficient, which it isn’t; and (ii) their statistical identification method, in which they used a range of socio-economic variables as instruments for expenditure, was flawed as the instruments were neither strong determinants of expenditure nor (conditionally) independent of population health. I also noted that their tests would be unlikely to be any good to detect this problem. In response to the first, Tony O’Hagan commented to say that that they did not assume NHS efficiency, nor even that it was assumed that the NHS is trying to maximise health. This may well have been the case, but I would still, perhaps pedantically, argue then that this is therefore not an opportunity cost. For the question of instrumental variables, an alternative method was proposed by Martyn Andrews and co-authors, using information that feeds into the budget allocation formula as instruments for expenditure. In this new article, Claxton, Lomas, and Martin adopt Andrews’s approach and apply it across four key programs of care in the NHS to try to derive cost-per-QALY thresholds. First off, many of my original criticisms I would also apply to this paper, to which I’d also add one: (Statistical significance being used inappropriately complaint alert!!!) The authors use what seems to be some form of stepwise regression by including and excluding regressors on the basis of statistical significance – this is a big no-no and just introduces large biases (see this article for a list of reasons why). Beyond that, the instruments issue – I think – is still a problem, as it’s hard to justify, for example, an input price index (which translates to larger budgets) as an instrument here. It is certainly correlated with higher expenditure – inputs are more expensive in higher price areas after all – but this instrument won’t be correlated with greater inputs for this same reason. Thus, it’s the ‘wrong kind’ of correlation for this study. Needless to say, perhaps I am letting the perfect be the enemy of the good. Is this evidence strong enough to warrant a change in a cost-effectiveness threshold? My inclination would be that it is not, but that is not to deny it’s relevance to the debate.

Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. The Lancet Published 14th April 2018

“Moderate drinkers live longer” is the adage of the casual drinker as if to justify a hedonistic pursuit as purely pragmatic. But where does this idea come from? Studies that have compared risk of cardiovascular disease to level of alcohol consumption have shown that disease risk is lower in those that drink moderately compared to those that don’t drink. But correlation does not imply causation – non-drinkers might differ from those that drink. They may be abstinent after experiencing health issues related to alcohol, or be otherwise advised to not drink to protect their health. If we truly believed moderate alcohol consumption was better for your health than no alcohol consumption we’d advise people who don’t drink to drink. Moreover, if this relationship were true then there would be an ‘optimal’ level of consumption where any protective effect were maximised before being outweighed by the adverse effects. This new study pools data from three large consortia each containing data from multiple studies or centres on individual alcohol consumption, cardiovascular disease (CVD), and all-cause mortality to look at these outcomes among drinkers, excluding non-drinkers for the aforementioned reasons. Reading the methods section, it’s not wholly clear, if replicability were the standard, what was done. I believe that for each different database a hazard ratio or odds ratio for the risk of CVD or mortality for eight groups of alcohol consumption was estimated, these ratios were then subsequently pooled in a random-effects meta-analysis. However, it’s not clear to me why you would need to do this in two steps when you could just estimate a hierarchical model that achieves the same thing while also propagating any uncertainty through all the levels. Anyway, a polynomial was then fitted through the pooled ratios – again, why not just do this in the main stage and estimate some kind of hierarchical semi-parametric model instead of a three-stage model to get the curve of interest? I don’t know. The key finding is that risk generally increases above around 100g/week alcohol (around 5-6 UK glasses of wine per week), below which it is fairly flat (although whether it is different to non-drinkers we don’t know). However, the picture the article paints is complicated, risk of stroke and heart failure go up with increased alcohol consumption, but myocardial infarction goes down. This would suggest some kind of competing risk: the mechanism by which alcohol works increases your overall risk of CVD and your proportional risk of non-myocardial infarction CVD given CVD.

Family ruptures, stress, and the mental health of the next generation [comment] [reply]. American Economic Review [RePEc] Published April 2018

I’m not sure I will write out the full blurb again about studies of in utero exposure to difficult or stressful conditions and later life outcomes. There are a lot of them and they continue to make the top journals. Admittedly, I continue to cover them in these round-ups – so much so that we could write a literature review on the topic on the basis of the content of this blog. Needless to say, exposure in the womb to stressors likely increases the risk of low birth weight birth, neonatal and childhood disease, poor educational outcomes, and worse labour market outcomes. So what does this new study (and the comments) contribute? Firstly, it uses a new type of stressor – maternal stress caused by a death in the family and apparently this has a dose-response as stronger ties to the deceased are more stressful, and secondly, it looks at mental health outcomes of the child, which are less common in these sorts of studies. The identification strategy compares the effect of the death on infants who are in the womb to those infants who experience it shortly after birth. Herein lies the interesting discussion raised in the above linked comment and reply papers: in this paper the sample contains all births up to one year post birth and to be in the ‘treatment’ group the death had to have occurred between conception and the expected date of birth, so those babies born preterm were less likely to end up in the control group than those born after the expected date. This spurious correlation could potentially lead to bias. In the authors’ reply, they re-estimate their models by redefining the control group on the basis of expected date of birth rather than actual. They find that their estimates for the effect of their stressor on physical outcomes, like low birth weight, are much smaller in magnitude, and I’m not sure they’re clinically significant. For mental health outcomes, again the estimates are qualitatively small in magnitude, but remain similar to the original paper but this choice phrase pops up (Statistical significance being used inappropriately complaint alert!!!): “We cannot reject the null hypothesis that the mental health coefficients presented in panel C of Table 3 are statistically the same as the corresponding coefficients in our original paper.” Statistically the same! I can see they’re different! Anyway, given all the other evidence on the topic I don’t need to explain the results in detail – the methods discussion is far more interesting.

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