Notes on Piketty, capital and labor, theory and data

Notes on Piketty, capital and labor, theory and data

Piketty’s (2014) book Capital and associated articles contains a lot of fascinating stuff and has inspired a lot of interesting debate. Here are some summary notes. Please comment on errors. If you want to change the world, you need to know how it works.

The critique: Data and theory don’t support the r > g story, Piketty’s 2nd law, that low growth and high returns on capital will make K/Y increase in the future.

  • Net savings rate does not seem to be stable in the long run and does not seem to correlate negatively with growth.
  • Adjusted for valuation, K/Y doesn’t seem to have increased in the last decades, contrary to some of Piketty’s main findings.
  • Piketty means that the elasticity of substitution between K and L (\sigma) is above 1, based on, among other things, data on K/Y. But if K/Y hasn’t increased, Piketty might also be wrong about \sigma. A lot of earlier empirical analysis indicates \sigma< 1. Also, for Piketty’s theory to hold, \sigma might need to be much higher than Piketty suggests.

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Will Homo Economicus work less in the future?

Will Homo Economicus work less in the future?

Timo Boppart and Per Krussell (BK) did a really interesting paper last year on the past and future of work hours. More people should read it and I want to understand it, so here is some simple summary notes.

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Two Keynesians

Here are two presentations from 2013 with Laurance Ball and Engelbert Stockhammer on unemployment. An introduction & illustration of some theoretical differences. Ball argues from a New Keynesian view that hysteresis may cause a long run increase in unemployment, and stresses the importance of monetary policy (more text here and here). Stockhammer argues that a long run NAIRU is probably a bad explanation for the development of unemployment in the OECD countries, based on a Post Keynesian view, instead focusing on capital accumulation (more text here and here).

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Academic writing software. Combine text, graphs & statistical analysis

I use the following:

  1. LyX – very easy to use document processor that generates LaTeX code. Like MS Word but for academic publications and such. Creates academic layouted pdfs. Great for writing math stuff. You can connect statistical results, such as graphs and regression tables from your LyX-document. If you update your data or results, the graphs and tables are also updated in the text automatically. Statistical software (Stata, RSstudio etc.) can create regression tables in formats that LyX can handle. Example: I usually export regression tables from Stata in file format .tex, and import them as “Child document” in LyX (Stata commands: .eststo .esttab). Extremely useful.
  2. Zotero – to manage references and sources. Automatically creates a list of publications. Useful for writing in LyX, MS Word etc.
  3. LyZ – to combine LyX and Zotero, through Firefox (and here’s a plugin for Chrome). Example: Connect reference list to LyX; add reference from Zotero directly in the text in LyX; and the reference list in your LyX-document is updated automatically. Extremely useful.

There is loads of other similiar software for academic writing and such. Depending on your data and methods, there might be other programs that is better suited for your needs. Some comparisons of

  • academic writing software here
  • LaTeX editors here
  • reference manager tools here

I use Spotify and Soundcloud for music.