Have an ordinal dependent variable? Use this robustness test.

Ordinal variables are everywhere. Data providing information about happiness, levels of customer satisfaction, employees’ satisfaction, mental stress, psychological well-being, societal trust, and other important variables are now regularly collected and analyzed by national governments, large multinational companies, and researchers. However, because these data are not directly observable or quantitatively measurable, they are thus not measured on objective cardinal units. This presents a key challenge when performing standard quantitative analysis.

“How Much Does the Cardinal Treatment of Ordinal Variables Matter?”—Forthcoming

I am very excited to share that my paper, “How Much Does the Cardinal Treatment of Ordinal Variables Matter? An Empirical Investigation” is now (finally) forthcoming in the journal Political Analysis. I wrote the first draft of this paper in my 2nd-year paper class at the University of Minnesota. So, publishing this paper in the official methods journal of the American Political Science Association is particularly rewarding.

Testing and Correcting for Endogeneity with Discontinuities and No Exclusion Restriction

Applied microeconomists, like us, spend a lot of our time thinking (…erm… worrying) about the bias from endogeneity embedded in our empirical estimates. That is why the work of Carolina Caetano (and co-authors), in methodological papers published in Econometrica and the Journal of Econometrics seems so exciting to us.

Power Calculations for Experiments with Panel Data

Last week, I came across this paper by Fiona Burlig, Louis Preonas, and Matt Woerman recently published in the Journal of Development Economics. It is a paper that seems broadly applicable, so I’ll highlight the key points.

Mediation Analysis and the ‘Sequential Unconfoundedness’ Assumption

Students with the Centre for the Study of African Economies (CSAE) at the University of Oxford are creating a wonderful public good. The Coders’ Corner is a collection of tips and tricks for implementing useful statistical techniques in common statistical software (e.g., mostly Stata). This product represents a tremendous service to the broader research community. Almost anyone reading this blog should check out previous posts.

Binary Dependent Variable? … Just use OLS

Here is an excerpt from a recent paper published in Applied Economic Perspectives and Policy by John Gibson; entitled, “Are You Estimating the Right Thing? An Editor Reflects.”

How to Use the Front-Door Criterion — New Working Paper

If you follow Marc Bellemare’s blog or specifically his ‘Metrics Monday series, you will already be aware of our new working paper. The paper is titled: “The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion.” The number of people who are reading this post and who do not already read Marc’s blog is probably very small. So, with that in mind, I will offer a few additional thoughts based on the preliminary work writing this paper.

How to Assess “Economic Significance”

Back in 2015, I read a book by Morten Jerven, in which the author makes the point that over 145 variables have been found to be statistically significant explanatory variables for long-run economic growth. Morten’s point is more nuanced than this, but this suggests that when interpreting regression results we need to not only consider statistical significance, but also economic significance.

Lotteries and Life Satisfaction – A Comment on the Cardinal Treatment of Ordinal Variables

A long standing belief, held by many, is that winning the lottery actually makes people miserable. This belief is backed up by existing research in psychology finding that lottery winners were no more satisfied with their life than people who did not win the lottery. New research suggests this belief might be wrong.

How Much Does the Cardinal Treatment of Ordinal Variables Matter? – New Working Paper

Concepts such as subjective well-being, satisfaction, happiness, trust, measures of quality, and even standardized test scores are all measured using an ordinal variable. This means that we know the rank of the response categories (e.g. a respondent reporting being “very satisfied” indicates they are more satisfied than if they had reported being “satisfied”), but we do not know the interval between response categories (e.g. we don’t know how much more satisfied “very satisfied” is compared to “satisfied”). This is contrasted with cardinal variables, such as earnings, where we know $10 is more than $5 and represents twice as much money.