# 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.

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# Sam Watson’s journal round-up for 2nd October 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 path to longer and healthier lives for all Africans by 2030: the Lancet Commission on the future of health in sub-Saharan Africa. The Lancet [PubMedPublished 13th September 2017

The relationship of health insurance and mortality: is lack of insurance deadly? Annals of Internal Medicine [PubMedPublished 19th September 2017

Smoking, expectations, and health: a dynamic stochastic model of lifetime smoking behavior. Journal of Political Economy [RePEcPublished 24th August 2017

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# Does political reform really reduce child mortality?

Measuring causal effects is a tricky business. But, it’s necessary if we want to appropriately design effective policies and interventions. Many things are not amenable to manipulation in an experiment and so we rely upon a toolbox of statistical tools to try to identify the effect of interest. These methods are often ingenious, finding sophisticated ways of exploiting different types of variation, but they are essentially uninterpretable without an underlying causal theory. To illustrate this, let’s consider a paper that was featured a few weeks ago in the journal round-up: Effect of democratic reforms on child mortality: a synthetic control analysis.

A large number of countries have undergone democratic reform over the last 20 years. This article aimed to estimate how that reform has impacted upon child mortality. To do this a synthetic control method was used.

## Synthetic control

The synthetic control method was formalised by Alberto Abadie, Alexis Diamond, and Jens Hainmueller in an article in the Journal of the American Statistical Association. It’s particularly useful in the situation where there is one area or cluster or country that has undergone a change, and multiple potential countries or clusters to act as controls that did not undergo the change. The eponymous synthetic control is a weighted average of the potential control sites where the weights have been chosen to best replicate the pre-intervention trend in the intervention site. The example given by Abadie and colleagues is estimation of the impact of tobacco control reform in California on tobacco consumption. The other US states are all potential controls. Bayesian synthetic control methods have also been established (by a team at Google), which we will make use of later.

The synthetic control method therefore seems appropriate to analyse the impact of democratic reform in a given country. Measurement of democratic reform was based on a change in the Polity2 index that rates the degree of autocracy/democracy in countries; a switch in the index from negative to positive (the index runs from -10 to 10) was considered ‘democratic reform’. Of the 60 countries identified as having reformed, 33 met the inclusion criteria, and 24 counterfactuals were able to be constructed. The primary outcome is the relative reduction in child mortality after ten years; the results from the 24 countries are shown in the histogram below (Figure 1). It would seem that, on average, democratic reform seems to reduce child mortality.

Figure 1. Histogram of results from the 24 included countries from Pieters et al.

## Causes

Perhaps one of the factors that have limited research in the area of political economy and health is the complexity of the relationships between the various macro, micro, economic, and political effects. For example, on the basis of the evidence presented above, we would still not be able to say whether, for a given country, introducing democratic reform would have any impact on child mortality. Let’s consider a couple of examples to explore why.

Figure 2. Results from synthetic control analyses of the impact of democratic change on child mortality. Data from UN Inter-agency Group for Child Mortality Estimation.

### Mozambique

Mozambique was engaged in a civil war between 1977 and 1992 as the communist Frelimo battled the anti-communist Renamo for control of the country. At the cessation of hostilities in 1993, wide sweeping reforms were enacted by Joaquim Chissano, and an election was held. We can consider 1993 as the year of democratic reform and conduct our own synthetic control analysis (using the aforementioned Bayesian approach). The results are shown in the figure above (Figure 2). Clearly, there is a significant reduction, but is this due to democratic reform or simply the end of civil war? A counterfactual approach is used as the theory of causation behind much statistical inference. Had an autocratic regime followed the civil war would there have still been declines in child mortality? I would conjecture that there would have been. Democratic reform in this case is either not a cause or a redundant cause.

### Zambia

Kenneth Kuanda was removed as the president of Zambia in 1991. Following this, reforms for multiparty democracy were enacted. Figure 2 above reports the estimated impact upon child mortality. A decline is clearly evident, however, this decline does not start until 2003, when the copper price tripled (Figure 3 below; copper constitutes 88% of Zambia’s exports) and GDP per capita almost doubled. Again, whether democratic reform can be inferred as a cause in this instance is questionable, especially when reduced to questions of counterfactuals.

Figure 3. Copper price per metric ton (US\$). Data from IMF Cross Country Macroeconomic Statistics Database.

### South Africa

The apartheid regime was ousted in 1994 following election of the ANC in South Africa. This regime change opened up the political institutions to the majority of South Africans who had previously been excluded. However, as Figure 2 above shows, this reform appeared to have little impact on child mortality. Indeed, South African healthcare still faces significant challenges, and large structural inequalities in access to quality healthcare persist even today. Taken together, this illustrates that democratic reform is not a sufficient condition for improvement of population health.

### Zimbabwe

Finally, consider Zimbabwe, which became more autocratic in 1986 following a deal between Zanu and Zapu. If democratic reform improves child mortality then it seems reasonable that autocratic changes would worsen child mortality. Figure 2 above reports the synthetic control results for Zimbabwe. No change is evident.

## What can we conclude?

Democratic reform is neither a sufficient nor necessary condition for improvements in child mortality. We cannot understand the evidence without an underlying theory. The study discussed here is a good data analysis; decent analyses in this area should be encouraged. But, the theory should come before the data.

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