He estado siguendo los datos de #COVID19 en Chile hace un rato, y encontré algunos patrones interesantes q pensé seria útil incluir en algo mas coherente.
Tuvieron las cuarentenas efectos heterogeneos en cuanto al progreso de nuevos casos en Chile? Un hilo 1/n Lo q mas me interesaba era ver si es que habia diferencias en efectividad de estas politicas segun nivel de ingreso, y potencial% analizar a que se debian estas diferencias. Spoiler: Encuentro diferencias entre ambos grupos. 2/n

I’ve been tracking #COVID19 data in Chile for a while now, and some interesting patterns arose that I thought would be useful to put together in sth more coherent.

Did small-area lockdowns have heterogeneous effects on the daily progression of new cases in Chile? A thread 1/n What I was most interested in was seeing whether there were differences in the effectiveness of these policies by income level, and potentially analyzing why these differences happened. Spoiler: I do find differences 2/n

Ok, rant time about #matching. I’ve seen a lot of econ people diss matching, and it was something somewhat repeated on my different methods classes in different institutions. So I’ve got stuff to say: 1) Stop confusing matching w/ an identification strategy. 1/n It’s an adjustment method. Of course it’s a huge stretch to think you can recover causal effects *just* by matching. Just as it’s ridiculous to think you can recover those effects by using OLS. However, if you layer it on top of an actual sound identification strategy 2/n