In a research project I’ve been working on for several years now, we’re interested in the effect of anti-NGO legal crackdowns on various foreign aid-related outcomes: the amount of foreign aid a country receives and the proportion of that aid dedicated to contentious vs. non-contentious causes or issues. These outcome variables are easily measurable thanks to the AidData project, but they post a tricky methodological issue.

The LSE Impact blog has a post from May 2021 raising some reservations about the idea of ‘Slow Science’. The ‘Slow Science’ idea hasn’t really picked up in academia, as far as I can tell. The post presents some good thoughts about why the “slowness-idea” is problematic in general. I agree that slowness is not a value in itself. Sometimes, developments and events like a pandemic demand it to do research faster than one would do it otherwise.

In place of a generic blog post, I am reposting a short Twitter thread here. The thread is a response to an opinion piece on the Times Higher Education website titled Pay researchers for results, not plans. (Posts on the THE website require registration of an account that includes a couple of free reads.) I copy-paste thread into this post. If you prefer to read it on Threadreader, you find it here.

Read the previous post first! This post is a sequel to the previous one on Bayesian propensity scores and won’t make a lot of sense without reading that one first. Read that one first! In my previous post about how to create Bayesian propensity scores and how to legally use them in a second stage outcome model, I ended up using frequentist models for the outcome stage.

This post combines two of my long-standing interests: causal inference and Bayesian statistics. I’ve been teaching a course on program evaluation and causal inference for a couple years now and it has become one of my favorite classes ever.

In most of my research, I work with country-level panel data where each row is a country in a specific year (Afghanistan in 2010, Afghanistan in 2011, and so on), also known as time-series cross-sectional (TSCS) data.

At the end of my previous post on beta and zero-inflated-beta regression, I included an example of a multilevel model that predicted the proportion of women members of parliament based on whether a country implements gender-based quotas for their legislatures, along with a few different control variables. I also included random effects for year and region in order to capture time- and geography-specific trends.

In the data I work with, it’s really common to come across data that’s measured as proportions: the percent of women in the public sector workforce, the amount of foreign aid a country receives as a percent of its GDP, the percent of religious organizations in a state’s nonprofit sector, and so on. When working with this kind of data as an outcome variable (or dependent variable) in a model, analysis gets tricky if you use standard models like

I’ve been teaching a course on program evaluation since Fall 2019, and while part of the class is focused on logic models and the more managerial aspects of evaluation, the bulk of the class is focused on causal inference. Ever since reading Judea Pearl’s The Book of Why in 2019, I’ve thrown myself into the world of DAGs, econometrics, and general causal inference, and I’ve been both teaching it and using it in research ever since.

Update #1 An update to knitr has made it a ton easier to embed fonts in SVG files from R. Jump to the update to see how. Update #2 Also, it’s possible to change TikZ fonts and not use Computer Modern for everything! Jump to the second update to see how.