Observational studies

Excess mortality in the Netherlands in 2020

Published by demetriodor on January 25, 2021

What has been the impact of COVID-19 on mortality in the Netherlands? Using the methods described here, I estimated excess mortality in the country during 2020. The results are not pretty: around 15,000 additional deaths, 10% increase over the expected mortality for the year, 25% of the excess not captured by records of official COVID-19-related deaths. The analysis features comparisons of excess mortality over the past 10 years, as well as an exploration of 2020 excess mortality across age and gender. Read it here. You can also check the data and code (in R).

Modeling mortality

Published by demetriodor on January 20, 2021

To grasp the true impact of COVID-19 on our societies, we need to know the effect of the pandemic on mortality. In other words, we need to know how many deaths can be attributed to the virus, directly and indirectly. It is already popular to visualize mortality in order to gauge the impact of the pandemic in different countries. You might have seen at least some of these graphs and websites: FT, Economist, Our World in Data, CBS, EFTA, CDC, EUROSTAT, and EUROMOMO. But estimating the impact of COVID-19 on mortality is also controversial, with people either misunderstanding or distrusting the way in which the impact is measured and assessed. That’s why, I put together a step-by-step guide about how we can go about estimating the impact of COVID-19 on mortality. In the guide, I build a large number of statistical models that we can use to predict expected mortality in 2020. The complexity of the models ranges from the simplest, based only on weekly averages from past years, to what is currently the state of the art. But this is not all. What I also do is review the predictive performance of all of these models, so that we know which ones work best. I run the models on publicly available data from the Netherlands, I use only the open software R, and I share the code, so anyone can check, replicate and extend the exercise. The guide is available here: http://dimiter.eu/Visualizations_files/nlmortality/Modeling-Mortality.html I hope this guide will provide some transparency about how expected mortality is and can be estimated…

Interest groups and the making of legislation

Published by demetriodor on March 6, 2013

How are the activities of interest groups related to the making of legislation? Does mobilization of interest groups lead to more legislation in the future? Alternatively, does the adoption of new policies motivate interest groups to get active? Together with Dave Lowery, Brendan Carroll and Joost Berkhout, we tackle these questions in the case of the European Union. What we find is that there is no discernible signal in the data indicating that the mobilization of interest groups and the volume of legislative production over time are significantly related. Of course, absence of evidence is the same as the evidence of absence, so a link might still exist, as suggested by theory, common wisdom and existing studies of the US (e.g. here). But using quite a comprehensive set of model specifications we can’t find any link in our time-series sample. The abstract of the paper is below and as always you can find at my website the data, the analysis scripts, and the pre-print full text. One a side-note – I am very pleased that we managed to publish what is essentially a negative finding. As everyone seems to agree, discovering which phenomena are not related might be as important as discovering which phenomena are. Still, there are few journals that would apply this principle in their editorial policy. So cudos for the journal of Interest Groups and Advocacy. Abstract Different perspectives on the role of organized interests in democratic politics imply different temporal sequences in the relationship between legislative activity and the influence activities of…

Correlation does not imply causation. Then what does it imply?

Published by demetriodor on October 9, 2012

‘Correlation does not imply causation’ is an adage students from all social sciences are made to recite from a very early age. What is less often systematically discussed is what could be actually going on so that two phenomena are correlated but not causally related. Let’s try to make a list: 1) The correlation might be due to chance. T-tests and p-values are generally used to guard against this possibility. 1a) The correlation might be due to coincidence. This is essentially a variant of the previous point but with focus on time series. It is especially easy to mistake pure noise (randomness) for patterns (relationships) when one looks at two variables over time. If you look at the numerous ‘correlation is not causation’ jokes and cartoons on the internet, you will note that most concern the spurious correlation between two variables over time (e.g. number of pirates and global warming): it is just easier to find such examples in time series than in cross-sectional data. 1b) Another reason to distrust correlations is the so-called ‘ecological inference‘ problem. The problem arises when data is available at several levels of observation (e.g. people nested in municipalities nested in states). Correlation of two variables aggregated at a higher level (e.g. states) cannot be used to imply correlation of these variables at the lower (e.g. people). Hence, the higher-level correlation is a statistical artifact, although not necessarily due to mistaking ‘noise’ for ‘signal’. 2) The correlation might be due to a third variable being causally related to the two correlated variables we observe. This is the well-known omitted…

Facebook does randomized experiments to study social interactions

Published by demetriodor on June 15, 2012

Facebook has a Data Science Team. And here is what they do: Eytan Bakshy […] wanted to learn whether our actions on Facebook are mainly influenced by those of our close friends, who are likely to have similar tastes. […] So he messed with how Facebook operated for a quarter of a billion users. Over a seven-week period, the 76 million links that those users shared with each other were logged. Then, on 219 million randomly chosen occasions, Facebook prevented someone from seeing a link shared by a friend. Hiding links this way created a control group so that Bakshy could assess how often people end up promoting the same links because they have similar information sources and interests [link to source at Technology Review]. It must be great (and a great challenge) to have access to all the data Facebook and use it to answer questions that are relevant not only for the immediate business objectives of the company. In the words of the Data Science Team leader: “The biggest challenges Facebook has to solve are the same challenges that social science has.” Those challenges include understanding why some ideas or fashions spread from a few individuals to become universal and others don’t, or to what extent a person’s future actions are a product of past communication with friends. Cool! These statements might make for a good discussion about the ethics of doing social science research inside and outside academica as well.

Protestants, Missionaries and the Diffusion of Liberal Democracy

Published by demetriodor on June 4, 2012

A new APSR article [ungated] argues for the crucial role of Protestant missionaries in the global spread of liberal democracy. The statistical analyses tease out the effect of missionaries from the influence of the characteristics of colonizers (Britain, the Netherlands, France, etc.) and pre-existing geographic, economic and cultural characteristics of the states. Interestingly, Protestant missionary influence not only remains a significant predictor of democracy outside the Western world once these factors are controlled for, but it renders them obsolete (which is a big deal because the same institutional, geographic, economic and cultural characteristics have been the usual explanations of democracy diffusion). On the other hand, the patterns in the data are consistent with the plausible mechanisms through which the effect of Protestant missionaries is exercised – the spread of newspapers, education, and civil society. I am sure this article is not going to be the last word on democracy diffusion, but it certainly puts the influence of Protestantism center stage. The major issue, I suspect, is not going to be methodological (since the article already considers a plethora of potential methodological complications in the appendix), but conceptual – to what extent the effect of Protestant missionaries can be conceptually separated from the improvements in education and the growth of the public sphere. In other words, do (did) you need the religious component at all, or education, newspapers and civil society would have worked on their own to make liberal democracy more likely (even if fostered by other channels than Protestant missionaries) . In terms of methodology, it might be interesting…

Category: Observational studies