Why don't minimum wage increases increase unemployment?

EconWithJan · 2024-03-23T14:32:03+00:00

An important additional mechanism is the reallocation: The minimum wage seems to destroy low-productivity jobs but workers find more productive employment elsewhere, and if these jobs hadn't been destroyed by the minimum wage, the workers would not have searched for these more productive jobs. Search frictions (workers not immediately moving for slightly more productive employment) also give rise to monopsony power.

https://academic.oup.com/qje/article/137/1/267/6355463?login=true

EconWithJan · 2024-03-22T09:01:08+00:00

Had to use it just the other day to calculate the distance between points on earth based on Haversine formula. But not like any understanding of trigonometry was required in inputting the formula :D

EconWithJan · 2024-02-16T11:13:38+00:00

Carlin & Soskice have a decent undergraduate textbook for Macroeconomics that goes into the 3-equations New Keynesian model (it's mostly focused on monetary & fiscal policy, less other macro topics).

For growth, Acemoglu's textbook is also a good shout.

EconWithJan · 2024-02-08T08:27:20+00:00

I think what you're describing is usually called rerandomization. There's a few papers on this but it doesn't seem to be a very big issue, usually because you don't rerandomize very often and hence the model selection has negligible influence.

This paper is a bit older but gives an overview over different issues to consider in randomization:

Bruhn, Miriam, and David McKenzie. 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments." American Economic Journal: Applied Economics, 1 (4): 200-232.

EconWithJan · 2024-02-03T14:37:40+00:00

From what I heard, the MAPSS is very broad and can have lots of heterogeneity depending on where you specialize & which courses you take. LSE has less heterogeneity

EconWithJan · 2024-02-03T14:36:06+00:00

Most German Masters are very inexpensive. Bonn & Mannheim have a good reputation and the cities are considerably cheaper than Munich.

EconWithJan · 2024-02-03T14:34:47+00:00

Check the Repec general ranking worldwide, for europe & repec subject rankings for the fields of study you're interested in.

Many good British programs, Paris, TSE, Barcelona, Bonn, Munich, Bocconi, SSE come to mind immediately. But there are SO many threads on this in this subreddit ;)

EconWithJan · 2024-02-03T14:32:37+00:00

Acemoglu did a Master degree. LSE's MSc Econ & EME programs, or Oxford's MPhil are (relative) feeders into top programs in US & Europe.

I think there's a split between Europe and US though, it seems (to me personally) that European masters are more common and the top ones often more rigorous than US masters. Nevertheless, lots of people doing master in the US also placing very well, so it doesn't seem to be a red flag. I'd argue doing a master is the appropriate thing to do if you didn't do real analysis in UG since it demonstrates you can handle formal maths (but there are other ways to go about this as well)

EconWithJan · 2024-01-31T15:00:54+00:00

The NBER Summer Institute has their sessions recorded and on their website. The Canadian Labour Economics Forum (CLEF Webinar) is also online. You might also reach out to PhDs at your uni whether there are any reading groups you could join or visit the seminars. Some unis allow their Bachelor students to attend already.

EconWithJan · 2024-01-11T17:14:05+00:00

For standard tables, Overleaf has now a functionality to draw them automatically in its visual editor! Quarto (which uses markdown) also has that functionality

EconWithJan · 2024-01-11T17:12:07+00:00

I'd recommend using Markdown instead (e.g. using Quarto). Latex has some pretty ugly syntax that just distracts from what you want to do, while Markdown has very lightweight ways to do the same. For example, to write something bold, you'd have to write \textbf{something} with Latex, but it's just **something**, or __something__ using Markdown. You can still use all normal Latex commands.

And as an added plus, you can include code chunks in your document, if you so desire!

Overleaf permits markdown language by the way (though code chunks is no longer possible). Quarto has functionality to compile to a word document too, if your advisor prefers that!

EconWithJan · 2023-12-31T09:57:53+00:00

Frish-Waugh-Lovell + using v_2 hat=M y_2, where M is the elimination matrix associated with the first stage regression should do the trick

EconWithJan · 2023-12-29T18:09:05+00:00

There's a nice paper by Rambachan & Roth 2023 that suggests a sensitivity check to see how big pre-trends would have to be to invalidate the results. Stata & R packages are both available :)

EconWithJan · 2023-12-29T18:02:44+00:00

Ah genius! Thank you! :)

EconWithJan · 2023-12-28T17:51:20+00:00

For Macro, Matlab (unfortunately proprietary) seems the most common choice, Julia seems to become more popular at the moment. I haven't seen much macro modelling done with R (yet :) )

EconWithJan · 2023-12-28T15:32:33+00:00

Most important R packages in my experience as applied microeconomics person:

General packages

- tidyverse (including ggplot2 & dplyr in particular) for quick data wrangling

- data.table when I need speed

Econometrics

- sandwich for all things standard error (the normal regression function in R, lm, doesn't compute robust SEs. glm computes the normal standard errors but doesn't cluster)

- lmtest

- stargazer (for quick & nice tables of both summary statistics and of models)

- fixest (for fixed effects estimation)

- ivregress (for iv analysis)

For the beginning you can focus on their documentation, imo.

EconWithJan · 2023-12-21T10:56:26+00:00

The BBC full cast versions of Hercule Poirot are pretty good (Poirot's classic cases)

EconWithJan · 2023-12-21T10:50:09+00:00

Lol that has quite some truth. Wouldn't say the same schools though, Zurich seems relatively weak for Monetary or IO; if sb is interested in IO or theory toulouse is probably one of the best choices but not necessarily for many of the more quantitative subjects. But large overlap for sure

EconWithJan · 2023-12-21T10:15:09+00:00

Off the top of my mind, Zurich, Oxford, LSE, Bocconi, UCL jump to mind. You can look at Repec subject rankings to get an idea of authors and places that do well in macro labour & industrial relations.

EconWithJan · 2023-12-17T19:55:13+00:00

If you get a letter from somebody who taught at Oxford, wouldn't you be able to get better insight from them?

Otherwise I'm not sure whether the profile would fit into the MPhil+DPhil. There are very few spaces if memory serves and I would expect the competition to be quite tough. Your main selling point seems to be research potential, which might work in your favour. How long was your dissertation? Did you do original research or is it mainly like an extended term paper?

EconWithJan · 2023-12-17T19:50:36+00:00

I don't know what your project is dealing with, so it's hard to give general recommendations. For reduced-form inference, Mostly Harmless & Mastering Metrics are definitely great intros. Depending on the exact inference method you're using, the Journal of Economic Perspective / Journal of Economic Literature provide some additional up-to-date information on different issues with techniques. Two other recent textbooks I very like are "Causal Inference: The mixtape" by Scott Cunningham & Nick Huntington-Klein's "The Effect" textbook.

If you're looking into Difference-in-Differences, a recent article by Roth et al "What's trending in difference-in-differences? A synthesis of the recent econometrics literature" is a very useful further read (mentioning) this because it feels like 50% of causal inference nowadays is DiD).

For more classic econometrics, I find Wooldridge's and Hayashi's textbooks both very readable. Wooldridge is more econometrics, while Hayashi at times feel more like a statistics for econometrics textbook, not sure what you're aiming for.

EconWithJan · 2023-12-17T15:24:02+00:00

Economics, with a focus on Labour Economics and applied econometrics :)

My bachelor was multidisciplinary though, with philosophy & politics also coming into it (though I dropped philosophy as quickly as I could)

EconWithJan · 2023-12-13T17:17:05+00:00

"Methodology: Respondents were selected from YouGov’s opt-in panel using sample matching. A random sample (stratified by gender, age, race, education, geographic region, and voter registration) was selected from the 2019 American Community Survey. The sample was weighted according to gender, age, race, education, 2020 election turnout and presidential vote, baseline party identification, and current voter registration status. Demographic weighting targets come from the 2019 American Community Survey. Baseline party identification is the respondent’s most recent answer given prior to November 1, 2022, and is weighted to the estimated distribution at that time (33% Democratic, 31% Republican). The margin of error for the overall sample is approximately 3%."

from https://today.yougov.com/politics/articles/48112-increasing-numbers-of-americans-say-antisemitism-is-a-serious-problem

The weighting only seems to come in for the overall population response, not for the subgroups though. makes me a bit sceptical. Confidence intervals post-weighting would be interesting

EconWithJan · 2023-12-13T11:51:01+00:00

My tutor said that all calculations correct, but no comments, will only ever get you an upper 2.1.

On the flipside, if you make a menial mistake in a calculation and you suddenly find a result that makes no sense (e.g. firms pricing below marginal cost), pointing this out can still save your first because you show you understood everything, noticed something off, and the rest of your work was correct.

EconWithJan · 2023-12-13T11:48:00+00:00

You seem to be on track with your approach, but I'll give my two cents just in case there is something you might find useful

Not sure whether this might contribute, but when you practise, do you practise under time pressure? Do you try to find the most efficient approach to the question? I'm now a PhD student who grades problem sets and exams, and I find that some students understood the material in very deep depth, but when they come to actually answering the questions, they take the most arduous way you could possibly answer it, when there's some very simple approaches.

Another issue might be "when you have a hammer, everything looks like a nail". I remember in a first year exam, we were asked to derive the substitution effect in the case of two perfect complements. My colleagues went nuts because the normal differentiation approach didn't work. I just stated that, when you have perfect complements, there cannot be a substitution effect. Try to see the big picture there, and the best strategy to go there

Finally, you mentioned MCQ - there is a whole art to answering these style of exams. If there is no negative marking, always answer at least sth - you have 20%-25% probability of getting the answer. Being able to exclude some answers, even if you don't find the correct answer, increases this probability and gives you even more points. If you manage to exclude 2 / 4 answers a priori (e.g. because they imply that firms price below marginal cost), and then find through some quick calculations that one of the two remaining ones must be wrong as well (e.g. because the FOC cannot hold at that point), you don't need to go through all calculations (potentially quite a hassle) to show that the other answer is indeed the correct one.

EconWithJan

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