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.

Brent Gibbons’s journal round-up for 22nd January 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.

Is retirement good for men’s health? Evidence using a change in the retirement age in Israel. Journal of Health Economics [PubMed] Published January 2018

This article is a tour de force from one chapter of a recently completed dissertation from the Hebrew University of Jerusalem. The article focuses on answering the question of what are the health implications of extending working years for older adults. As many countries are faced with critical decisions on how to adjust labor policies to solve rising pension costs (or in the case of the U.S., Social Security insolvency) in the face of aging populations, one obvious potential solution is to change the retirement age. Most OECD countries appear to have retirement ages in the mid-60’s with a number of countries on track to increase that threshold. Israel is one of these countries, having changed their retirement age for men from age 65 to age 67 in 2004. The author capitalizes on this exogenous change in retirement incentives, as workers will be incentivized to keep working to receive full pension benefits, to measure the causal effect of working in these later years, compared to retiring. As the relationship between employment and health is complicated by the endogenous nature of the decision to work, there is a growing literature that has attempted to deal with this endogeneity in different ways. Shai details the conflicting findings in this literature and describes various shortcomings of methods used. He helpfully categorizes studies into those that compare health between retirees and non-retirees (does not deal with selection problem), those that use variation in retirement age across countries (retirement ages could be correlated with individual health across countries), those that exploit variation in specific sector retirement ages (problem of generalizing to population), and those that use age-specific retirement eligibility (health may deteriorate at specific age regardless of eligibility for retirement). As this empirical question has amounted conflicting evidence, the author suggests that his methodology is an improvement on prior papers. He uses a difference-in-difference model that estimates the impact on various health outcomes, before and after the law change, comparing those aged 65-66 years after 2004 with both older and younger cohorts unaffected by the law. The assumption is that any differences in measured health between the age 65-66 group and the comparison group are a result of the extended work in later years. There are several different datasets used in the study and quite a number of analyses that attempt to assuage threats to a causal interpretation of results. Overall, results are that delaying the retirement age has a negative effect on individual health. The size of the effect found is in the ballpark of 1 standard deviation; outcome measures included a severe morbidity index, a poor health index, and the number of physician visits. In addition, these impacts were stronger for individuals with lower levels of education, which the author relates to more physically demanding jobs. Counterfactuals, for example number of dentist visits, which are not expected to be related to employment, are not found to be statistically different. Furthermore, there are non-trivial estimated effects on health care expenditures that are positive for the delayed retirement group. The author suggests that all of these findings are important pieces of evidence in retirement age policy decisions. The implication is that health, at least for men, and especially for those with lower education, may be negatively impacted by delaying retirement and that, furthermore, savings as a result of such policies may be tempered by increased health care expenditures.

Evaluating community-based health improvement programs. Health Affairs [PubMed] Published January 2018

For article 2, I see that the lead author is a doctoral student in health policy at Harvard, working with colleagues at Vanderbilt. Without intention, this round-up is highlighting two very impressive studies from extremely promising young investigators. This study takes on the challenge of evaluating community-based health improvement programs, which I will call CBHIPs. CBHIPs take a population-based approach to public health for their communities and often focus on issues of prevention and health promotion. Investment in CBHIPs has increased in recent years, emphasizing collaboration between the community and public and private sectors. At the heart of CBHIPs are the ideas of empowering communities to self-assess and make needed changes from within (in collaboration with outside partners) and that CBHIPs allow for more flexibility in creating programs that target a community’s unique needs. Evaluations of CBHIPs, however, suffer from limited resources and investment, and often use “easily-collectable data and pre-post designs without comparison or control communities.” Current overall evidence on the effectiveness of CBHIPs remains limited as a result. In this study, the authors attempt to evaluate a large set of CBHIPs across the United States using inverse propensity score weighting and a difference-in-difference analysis. Health outcomes on poor or fair health, smoking status, and obesity status were used at the county level from the BRFSS (Behavioral Risk Factor Surveillance System) SMART (Selected Metropolitan/Micropolitan Area Risk Trends) data. Information on counties implementing CBHIPs was compiled through a series of systematic web searches and through interviews with leaders in population health efforts in the public and private sector. With information on the exact years of implementation of CBHIPs in each county, a pre-post design was used that identified county treatment and control groups. With additional census data, untreated counties were weighted to achieve better balance on pre-implementation covariates. Importantly, treated counties were limited to those with CBHIPs that implemented programs related to smoking and obesity. Results showed little to no evidence that CBHIPs improved population health outcomes. For example, CBHIPs focusing on tobacco prevention were associated with a 0.2 percentage point reduction in the rate of smoking, which was not statistically significant. Several important limitations of the study were noted by the authors, such as limited information on the intensity of programs and resources available. It is recognized that it is difficult to improve population-level health outcomes and that perhaps the study period of 5-years post-implementation may not have been long enough. The researchers encourage future CBHIPs to utilize more rigorous evaluation methods, while acknowledging the uphill battle CBHIPs face to do this.

Through the looking glass: estimating effects of medical homes for people with severe mental illness. Health Services Research [PubMed] Published October 2017

The third article in this round-up comes from a publication from October of last year, however, it is from the latest issue of Health Services Research so I deem it fair play. The article uses the topic of medical homes for individuals with severe mental illness to critically examine the topic of heterogeneous treatment effects. While specifically looking to answer whether there are heterogeneous treatment effects of medical homes on different portions of the population with a severe mental illness, the authors make a strong case for the need to examine heterogeneous treatment effects as a more general practice in observational studies research, as well as to be more precise in interpretations of results and statements of generalizability when presenting estimated effects. Adults with a severe mental illness were identified as good candidates for medical homes because of complex health care needs (including high physical health care needs) and because barriers to care have been found to exist for these individuals. Medicaid medical homes establish primary care physicians and their teams as the managers of the individual’s overall health care treatment. The authors are particularly concerned with the reasons individuals choose to participate in medical homes, whether because of expected improvements in quality of care, regional availability of medical homes, or symptomatology. Very clever differences in estimation methods allow the authors to estimate treatment effects associated with these different enrollment reasons. As an example, an instrumental variables analysis, using measures of regional availability as instruments, estimated local average treatment effects that were much smaller than the fixed effects estimates or the generalized estimating equation model’s effects. This implies that differences in county-level medical home availability are a smaller portion of the overall measured effects from other models. Overall results were that medical homes were positively associated with access to primary care, access to specialty mental health care, medication adherence, and measures of routine health care (e.g. screenings); there was also a slightly negative association with emergency room use. Since unmeasured stable attributes (e.g. patient preferences) do not seem to affect outcomes, results should be generalizable to the larger patient population. Finally, medical homes do not appear to be a good strategy for cost-savings but do promise to increase access to appropriate levels of health care treatment.

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Chris Sampson’s journal round-up for 4th December 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.

Funding breakthrough therapies: a systematic review and recommendation. Health Policy Published 2nd December 2017

One of the (numerous) financial pressures on health care funders in the West is the introduction of innovative (and generally very expensive) new therapies. Some of these can be considered curative, which isn’t necessarily the best way for manufacturers to create a steady income. New funding arrangements have been proposed to facilitate patient access while maintaining financial sustainability. This article focuses on a specific group of innovative therapies known as ‘Advanced Therapy Medicinal Products’ (ATMPs), which includes gene therapies. The authors conducted a systematic review of papers proposing funding models and considered their appropriateness for ATMPs. There were 48 papers included in the review that proposed payment mechanisms for high-cost therapies. Three top-level groups were identified: i) financial agreements, ii) performance-based agreements, and iii) healthcoin (a tradable currency representing the value of outcomes). The different mechanisms are compared in terms of their feasibility, acceptability, burden, ‘financial attractiveness’ and their appeal to payers and manufacturers. Annuity payments are identified as relatively attractive compared to other options, but each mechanism is summarily shown to be imperfect in the ATMP context. So, instead, the authors propose an ATMP-specific fund. For UK readers, this will likely smell a bit too much like the disastrous Cancer Drugs Fund. It isn’t clear why such a programme would be superior to annuity payments or more inventive mechanisms, or even whether it would be theoretically sound. Thus, the proposal is not convincing.

Supply-side effects from public insurance expansions: evidence from physician labor markets. Health Economics [PubMed] Published 1st December 2017

Crazy though American health care may be, its inconsistency in coverage can make for good research fodder. The Child Health Insurance Program (CHIP) was set up in 1997 and then, when the initial money ran out 10 years later, the program was (eventually) expanded. In this study, the authors use the changes in CHIP to examine the impact of expanded public coverage on provider behaviour, namely; subspecialty training (which could become more attractive with a well-insured customer base), practice setting and prevailing wage offers. The data for the study relate to the physician labour market for New York state for 2002-2013, as collected in the Graduate Medical Education survey. A simple difference-in-differences analysis is conducted with reference to the 2009 CHIP expansion, controlling for physician demographics. Paediatricians are the treatment group and the control group is adult physician generalists (mostly internal medicine). 2009 seems to be associated with a step-change in the proportion of paediatricians choosing to subspecialise – an increased probability of about 8 percentage points. There is also an upward shift in the proportion of paediatricians entering private practice, with some (weak) evidence that there is an increased preference for rural areas. These changes don’t seem to be driven by relative wage increases, with no major change in trends. So it seems that the expanded coverage did have important supply-side effects. But the waters are muddy here. In particular, we have the Great Recession and Obamacare as possible alternative explanations. Though it’s difficult to come up with good reasons for why these might better explain the observed changes.

Reflections on the NICE decision to reject patient production losses. International Journal of Technology Assessment in Health Care [PubMedPublished 20th November 2017

When people conduct economic evaluations ‘from a societal perspective’, this often just means a health service perspective with productivity losses added. NICE explicitly exclude the inclusion of these production losses in health technology appraisals. This paper reviews the issues at play, focussing on the normative question of why they should (or should not) be included. Findings from a literature review are summarised with reference to the ethical, theoretical and policy questions. Unethical discrimination potentially occurs if people are denied health care on the basis of non-health-related characteristics, such as the ability to work. All else equal, should health care for men be prioritised over health care for women because men have higher wages? Are the unemployed less of a priority because they’re unemployed? The only basis on which to defend the efficiency of an approach that includes productivity losses seems to be a neoclassical welfarist one, which is hardly tenable in the context of health care. If we adopt the extra-welfarist understanding of opportunity cost as foregone health then there is really no place for production losses. The authors also argue that including production losses may be at odds with policy objectives, at least in the context of the NHS in the UK. Health systems based on privately-funded care or social insurance may have different priorities. The article concludes that taking account of production losses is at odds with the goal of health maximisation and therefore the purpose of the NHS in the UK. Personally, I think priority setting in health care should take a narrow health perspective. So I agree with the authors that production losses shouldn’t be included. I’m not sure this article will convince those who disagree, but it’s good to have a reference to vindicate NICE’s position.

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