# Sam Watson’s journal round-up for 9th July 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.

Evaluating the 2014 sugar-sweetened beverage tax in Chile: an observational study in urban areas. PLoS Medicine [PubMedPublished 3rd July 2018

Sugar taxes are one of the public health policy options currently in vogue. Countries including Mexico, the UK, South Africa, and Sri Lanka all have sugar taxes. The aim of such levies is to reduce demand for the most sugary drinks, or if the tax is absorbed on the supply side, which is rare, to encourage producers to reduce the sugar content of their drinks. One may also view it as a form of Pigouvian taxation to internalise the public health costs associated with obesity. Chile has long had an ad valorem tax on soft drinks fixed at 13%, but in 2014 decided to pursue a sugar tax approach. Drinks with more than 6.25g/100ml saw their tax rate rise to 18% and the tax on those below this threshold dropped to 10%. To understand what effect this change had, we would want to know three key things along the causal pathway from tax policy to sugar consumption: did people know about the tax change, did prices change, and did consumption behaviour change. On this latter point, we can consider both the overall volume of soft drinks and whether people substituted low sugar for high sugar beverages. Using the Kantar Worldpanel, a household panel survey of purchasing behaviour, this paper examines these questions.

Everyone in Chile was affected by the tax so there is no control group. We must rely on time series variation to identify the effect of the tax. Sometimes, looking at plots of the data reveals a clear step-change when an intervention is introduced (e.g. the plot in this post), not so in this paper. We therefore rely heavily on the results of the model for our inferences, and I have a couple of small gripes with it. First, the model captures household fixed effects, but no consideration is given to dynamic effects. Some households may be more or less likely to buy drinks, but their decisions are also likely to be affected by how much they’ve recently bought. Similarly, the errors may be correlated over time. Ignoring dynamic effects can lead to large biases. Second, the authors choose among different functional form specifications of time using Akaike Information Criterion (AIC). While AIC and the Bayesian Information Criterion (BIC) are often thought to be interchangeable, they are not; AIC estimates predictive performance on future data, while BIC estimates goodness of fit to the data. Thus, I would think BIC would be more appropriate. Additional results show the estimates are very sensitive to the choice of functional form by an order of magnitude and in sign. The authors estimate a fairly substantial decrease of around 22% in the volume of high sugar drinks purchased, but find evidence that the price paid changed very little (~1.5%) and there was little change in other drinks. While the analysis is generally careful and well thought out, I am not wholly convinced by the authors’ conclusions that “Our main estimates suggest a significant, sizeable reduction in the volume of high-tax soft drinks purchased.”

A Bayesian framework for health economic evaluation in studies with missing data. Health Economics [PubMedPublished 3rd July 2018

Missing data is a ubiquitous problem. I’ve never used a data set where no observations were missing and I doubt I’m alone. Despite its pervasiveness, it’s often only afforded an acknowledgement in the discussion or perhaps, in more complete analyses, something like multiple imputation will be used. Indeed, the majority of trials in the top medical journals don’t handle it correctly, if at all. The majority of the methods used for missing data in practice assume the data are ‘missing at random’ (MAR). One interpretation is that this means that, conditional on the observable variables, the probability of data being missing is independent of unobserved factors influencing the outcome. Another interpretation is that the distribution of the potentially missing data does not depend on whether they are actually missing. This interpretation comes from factorising the joint distribution of the outcome $Y$ and an indicator of whether the datum is observed $R$, along with some covariates $X$, into a conditional and marginal model: $f(Y,R|X) = f(Y|R,X)f(R|X)$, a so-called pattern mixture model. This contrasts with the ‘selection model’ approach: $f(Y,R|X) = f(R|Y,X)f(Y|X)$.

This paper considers a Bayesian approach using the pattern mixture model for missing data for health economic evaluation. Specifically, the authors specify a multivariate normal model for the data with an additional term in the mean if it is missing, i.e. the model of $f(Y|R,X)$. A model is not specified for $f(R|X)$. If it were then you would typically allow for correlation between the errors in this model and the main outcomes model. But, one could view the additional term in the outcomes model as some function of the error from the observation model somewhat akin to a control function. Instead, this article uses expert elicitation methods to generate a prior distribution for the unobserved terms in the outcomes model. While this is certainly a legitimate way forward in my eyes, I do wonder how specification of a full observation model would affect the results. The approach of this article is useful and they show that it works, and I don’t want to detract from that but, given the lack of literature on missing data in this area, I am curious to compare approaches including selection models. You could even add shared parameter models as an alternative, all of which are feasible. Perhaps an idea for a follow-up study. As a final point, the models run in WinBUGS, but regular readers will know I think Stan is the future for estimating Bayesian models, especially in light of the problems with MCMC we’ve discussed previously. So equivalent Stan code would have been a bonus.

This is an economics blog. But focusing solely on economics papers in these round-ups would mean missing out on some papers from related fields that may provide insight into our own work. Thus I present to you a politics and sociology paper. It is not my field and I can’t give a reliable appraisal of the methods, but the results are of interest. In the global fight against non-communicable diseases, there is a range of policy tools available to governments, including the sugar tax of the paper at the top. The WHO recommends a large number. However, there is ongoing debate about whether trade rules and agreements are used to undermine this public health legislation. One agreement, the Technical Barriers to Trade (TBT) Agreement that World Trade Organization (WTO) members all sign, states that members may not impose ‘unnecessary trade costs’ or barriers to trade, especially if the intended aim of the measure can be achieved without doing so. For example, Philip Morris cited a bilateral trade agreement when it sued the Australian government for introducing plain packaging claiming it violated the terms of trade. Philip Morris eventually lost but not after substantial costs were incurred. In another example, the Thai government were deterred from introducing a traffic light warning system for food after threats of a trade dispute from the US, which cited WTO rules. However, there was no clear evidence on the extent to which trade disputes have undermined public health measures.

This article presents results from a new database of all TBT WTO challenges. Between 1995 and 2016, 93 challenges were raised concerning food, beverage, and tobacco products, the number per year growing over time. The most frequent challenges were over labelling products and then restricted ingredients. The paper presents four case studies, including Indonesia delaying food labelling of fat, sugar, and salt after challenge by several members including the EU, and many members including the EU again and the US objecting to the size and colour of a red STOP sign that Chile wanted to put on products containing high sugar, fat, and salt.

We have previously discussed the politics and political economy around public health policy relating to e-cigarettes, among other things. Understanding the political economy of public health and phenomena like government failure can be as important as understanding markets and market failure in designing effective interventions.

Credits

# Lazaros Andronis’s journal round-up for 4th September 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.

The effect of spending cuts on teen pregnancy. Journal of Health Economics [PubMed] Published July 2017

High teenage pregnancy rates are an important concern that features high in many countries’ social policy agendas. In the UK, a country which has one of the highest teen pregnancy rates in the world, efforts to tackle the issue have been spearheaded by the Teenage Pregnancy Strategy, an initiative aiming to halve under-18 pregnancy rates by offering access to sex education and contraception. However, the recent spending cuts have led to reductions in grants to local authorities, many of which have, in turn, limited or cut a number of teenage pregnancy-related programmes. This has led to vocal opposition by politicians and organisations, who argue that cuts jeopardise the reductions in teenage pregnancy rates seen in previous years. In this paper, Paton and Wright set out to examine whether this is the case; that is, whether cuts to Teenage Pregnancy Strategy-related services have had an impact on teenage pregnancy rates. To do so, the authors used panel data from 149 local authorities in England collected between 2009 and 2014. To capture changes in teenage pregnancy rates across local authorities over the specified period, the authors used a fixed effects model which assumed that under-18 conception rates are a function of annual expenditure on teenage pregnancy services per 13-17 year female in the local authority, and a set of other socioeconomic variables acting as controls. Area and year dummies were also included in the model to account for unobservable effects that relate to particular years and localities and a number of additional analysis were run to get around spurious correlations between expenditure and pregnancy rates. Overall, findings showed that areas which implemented bigger cuts to teenage pregnancy-targeting programmes have, on average, seen larger drops in teenage pregnancy rates. However, these drops are, in absolute terms, small (e.g. a 10% reduction in expenditure is associated with a 0.25% decrease in teenage conception rates). Various explanations can be put forward to interpret these findings, one of which is that cuts might have trimmed off superfluous or underperforming elements of the programme. If this is the case, Paton and Wright’s findings offer some support to arguments that spending cuts may not always be bad for the public.

Young adults’ experiences of neighbourhood smoking-related norms and practices: a qualitative study exploring place-based social inequalities in smoking. Social Science & Medicine [PubMed] Published September 2017

Smoking is a universal problem affecting millions of people around the world and Canada’s young adults are no exception. As in most countries, smoking prevalence and initiation is highest amongst young groups, which is bad news, as many people who start smoking at a young age continue to smoke throughout adulthood. Evidence suggests that there is a strong socioeconomic gradient in smoking, which can be seen in the fact that smoking prevalence is unequally distributed according to education and neighbourhood-level deprivation, being a greater problem in more deprived areas. This offers an opportunity for local-level interventions that may be more effective than national strategies. Though, to come up with such interventions, policy makers need to understand how neighbourhoods might shape, encourage or tolerate certain attitudes towards smoking. To understand this, Glenn and colleagues saw smoking as a practice that is closely related to local smoking norms and social structures, and sought to get young adult smokers’ views on how their neighbourhood affects their attitudes towards smoking. Within this context, the authors carried out a number of focus groups with young adult smokers who lived in four different neighbourhoods, during which they asked questions such as “do you think your neighbourhood might be encouraging or discouraging people to smoke?” Findings showed that some social norms, attitudes and practices were common among neighbourhoods of the same SES. Participants from low-SES neighbourhoods reported more tolerant and permissive local smoking norms, whereas in more affluent neighbourhoods, participants felt that smoking was more contained and regulated. While young smokers from high SES neighbourhoods expressed some degree of alignment and agency with local smoking norms and practices, smokers in low SES described smoking as inevitable in their neighbourhood. Of interest is how individuals living in different SES areas saw anti-smoking regulations: while young smokers in affluent areas advocate social responsibility (and downplay the role of regulations), their counterparts in poorer areas called for more protection and spoke in favour of greater government intervention and smoking restrictions. Glenn and colleagues’ findings serve to highlight the importance of context in designing public health measures, especially when such measures affect different groups in entirely different ways.

Cigarette taxes, smoking—and exercise? Health Economics [PubMed] Published August 2017

Evidence suggests that rises in cigarette taxes have a positive effect on smoking reduction and/or cessation. However, it is also plausible that the effect of tax hikes extends beyond smoking, to decisions about exercise. To explore whether this proposition is supported by empirical evidence, Conway and Niles put together a simple conceptual framework, which assumes that individuals aim to maximise the utility they get from exercise, smoking, health (or weight management) and other goods subject to market inputs (e.g. medical care, diet aids) and time and budget constraints. Much of the data for this analysis came from the Behavioral Risk Factor Surveillance System (BRFSS) in the US, which includes survey participants’ demographic characteristics (age, gender), as well as answers to questions about physical activities and exercise (e.g. intensity and time per week spent on activities) and smoking behaviour (e.g. current smoking status, number of cigarettes smoked per day). Survey data were subsequently combined with changes in cigarette taxes and other state-level variables. Conway and Niles’s results suggest that increased cigarette costs reduce both smoking and exercise, with the decline in exercise being more pronounced among heavy and regular smokers. However, the direction of the effect varied according to one’s age and smoking experience (e.g. higher cigarette cost increased physical activity among recent quitters), which highlights the need for caution in drawing conclusions about the exact mechanism that underpins this relationship. Encouraging smoking cessation and promoting physical exercise are important and desirable public health objectives, but, as Conway and Niles’s findings suggest, pursuing both of them at the same time may not always be plausible.

Credits

# Incentives and social preferences

Incentives are widely and frequently used to influence preferences among people with the aim of achieving some socially beneficial end. These incentives include fines, rewards, and taxes. From the domain of health, Pigouvian taxes on foods deemed unhealthy and pricing schemes for alcohol are examples of such incentives. But, evidence often reveals that these incentives are not having the effect that would be expected from a rational homo economicus; often the effect is smaller than expected and in some cases is the opposite of what is expected. In one well-cited experiment, the imposition of fines on parents arriving late to pick up their children in Haifa, Israel, resulted in double the number of late pick-ups (Gneezy and Rustichini, 2000). Social preferences must be playing a role. In an extensive review of economic experiments, Bowles and Polania-Reyes (2012) examine whether economic incentives and social preferences are substitutes or complements. For policy-makers, the non-separability of incentives and social preferences is important; if incentives act as a substitute for preferences for some socially beneficial end, then their imposition will lead to lower than expected or opposite effects.

To examine how social preferences and incentives interact, Bowles and Polania-Reyes identify two types of preference: state-dependent and endogenous. They distinguish them as follows:

As Italian residents, your authors now eat a lot more pasta than we did in our countries of origin. Abstracting from possible international price differences, this could be another case of “when in Rome, do as the Romans.” Or it might be that we have newly come to enjoy the taste of pasta, perhaps through extensive exposure to it while in Italy. Which case it is—state-dependent or endogenous preferences—would be revealed by what we will eat back in Bogotá or Santa Fe. If we go back to arepas or potatoes, then our taste for pasta was state-dependent. If we remain pastaphiles, then our preferences have endogenously changed.

Extending from this, the authors define mechanisms by which incentives and preferences interact. Under a state-dependent mechanism, the situation, environment, or way (e.g. the state) in which an incentive is administered can alter preferences in three ways: