Excellent (and vibrant) discussion today hosted by us to brainstorm on our new findings on the Food Reserve Agency’s impact on agriculture market in Zambia.
We’re glad to be joined with colleagues from the Ministry of Agriculture, FRA, and the @ZambiaPDU.
New paper: We develop a supply chain model of staple crops in Zambia, showing transportation infrastructure can improve food security for smallholders.
Led by my student @JunrenW w/ @kathy_baylis@protensia@kcaylor
Open access: https://t.co/tJKSvGq5AP
@SamsungMobileUS You guys are the worst. I bought a Samsung s22 and it had a problem. I returned it and it took a month only to receive a faulty locked device. I was asked to send it back and they assured me it will be sorted. They still sent back a faulty and locked device. Very insensitive.
A 🧵 to keep track of my @Stata#dataviz packages 👇
1) 𝘀𝗰𝗵𝗲𝗺𝗲𝗽𝗮𝗰𝗸: Has a large collection of ready-to-use #Stata#schemes.
Two I personally use all the time are white_tableau (clean white) and neon (black background).
https://t.co/Ef8pAWHY44
All of these chapters look great, but I want to draw attention to "Chapter 83 - Machine learning in agricultural economics" by Baylis, Heckelai, and Storm. Exhaustive, up-to-date list & discussion of ML applications in enviro, ag, devo, related fields https://t.co/UWLH2CipO1
One of the somewhat hidden benefits of clustering standard errors is that, usually, they are robust to any other kind of misspecification. So for something like probit, the standard errors account for the possibility that the probit model is misspecified.
#metricstotheface
I botched this. Should read:
a) Using fitted values from a nonlinear first stage as IVs in a linear second stage.
(b) Finding your high school sweetheart on Facebook.
(c) Inserting fitted values from a first stage into a nonlinear second stage.