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If we take this methodology seriously, we should also apply it to TV/movies/streaming time.
This yields the big claims that TV and streaming technology *decreased* significantly from 2004 to 2017, and that this change led young adults to work 4% *more* hours at a given wage. https://twitter.com/ChicagoJournals/status/1357418936657195009
This yields the big claims that TV and streaming technology *decreased* significantly from 2004 to 2017, and that this change led young adults to work 4% *more* hours at a given wage. https://twitter.com/ChicagoJournals/status/1357418936657195009
Some key claims by Aguiar, Bils, Charles and Hurst in this paper, now in JPE:
(1) we can "infer quality changes" in leisure from time use data (specifically aggregated ATUS data), and
(2) innovations in recreational computing lowered 21-30 year old men's work hours by 2%.
(1) we can "infer quality changes" in leisure from time use data (specifically aggregated ATUS data), and
(2) innovations in recreational computing lowered 21-30 year old men's work hours by 2%.
First, a note about the key time use category ABCH use: “recreational computing” includes both gaming and things like checking email, goofing off on the computer, or the time I spend tweeting. It’s something of a mixed bag here. But they use this category instead of just gaming.
ABCH propose a methodology to calculate rates of technological change for demographic groups using aggregated time use data, which I have replicated using their data and code. First they calculate β for each leisure category (in row 1 here); β>1 is a "leisure luxury."
From this calculated β, we calculate a change in technology for this leisure type, in row 2. Next, use this to calculate a shift in the marginal value of leisure, in row 3. And finally, a change in labor supply at a given wage (row 4), a reduction of ~2% for 21-30 year old men.
I apply the ABCH methodology to time spent watching television, movies and streaming video. This category is also a "leisure luxury" (beta>1 in row 1), but shows *negative* technological change for most groups and would predict *increasing* labor supply of ~4% for those 21-30.
If we take the model in "Leisure Luxuries and the Labor Supply of Young Men" seriously, we should believe conclusions from it for leisure types other than recreational computing.
ABCH report a predicted LS drop from computing, but not the more-than-offsetting increase from TV.
ABCH report a predicted LS drop from computing, but not the more-than-offsetting increase from TV.
