Skip to main content

Japan Stagnation Myth?

I came across this piece on The Atlantic website by Eamonn Fingleton arguing that Japan's two decade long stagnation is largely a myth. I'm not willing, just yet, to completely go along with his thesis that this myth has been stoked by the Japanese Government to ease political pressure on their export-oriented economic model. (It would certainly be interesting if it were true. It is definitely the case that Japanese companies have become adept at dealing with potential US political pressure, e.g. assembling cars in the US.)

However, as a semi-regular visitor to Japan I do find it hard to completely believe the stagnation story. The place just seems so clean and vibrant. If Japan is stagnating, then American cities like Detroit would do well to 'stagnate' also.

Anyways, beyond the interesting stats on Japanese trade growth and the improvement in living standards since the 1980s in Eamonn's piece, I've been sceptical of the stagnation story based on official GDP numbers (he actually thinks these are underestimates, but let's leave that issue aside for the moment). If we control for stagnate Japanese population growth by looking at GDP growth per capita as opposed to overall GDP growth we can see that Japan's growth numbers aren't that different from the US's (the usual country comparison in the stagnation stories).

Look at this graph using data from the World Bank:

It shows the difference between Japanese and US annual overall GDP growth and per capita growth. Negative number indicate that the US grew more than Japan and vice versa. 0 means that GDP growth is the same for both countries.

The key stagnation period to look at is from around 2000 to the present. Japan was supposedly stagnating while the US was booming. If we just look at overall GDP growth Japan did grow more slowly than the US. But if we look at growth on a per capita basis, there is basically no difference. Apart from 2009, Japan has basically done at least as well as the US since the end of the Asian financial crisis.

Here is the R code to reproduce the graph:

Comments

Popular posts from this blog

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines' ). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models , especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values. So, I’ve been putting together th

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name