Toll Index December 2016 – strong finish

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On the mutation of the labor market – fitness

In the 1980’s we started seeing more and more neighborhood gyms. They were typically all about body building, owned by someone who was a body builder themselves and they were frequented mostly by young men. Arnold Schwarzenegger’s career was just beginning. The appearance of these gyms itself is a result of the industrial revolution: having freed ourselves from having to work with our bodies we turned to stressing our muscles in controlled settings as a sport.

In the last couple of decades we witnessed the disappearance of these body building gyms and saw them being replaced by large fitness club chains. These are more than just about body building and are joined by young and old as well as by men and women. If the old body building gym turned a body builder into a small business owner and had young men as a clientele these fitness clubs are now a non-negligible job creator: fitness and nutrition experts, personal trainers and the like. Moreover they contribute to the physical health of their clientele who are now mostly holding clerical jobs.

In the last couple of years strolling around in Cologne you will now see an increasing number of boutique fitness clubs. After fitness left the neighborhood for the chain store fitness club it now returns to the neighborhood revisiting and refining the concept. You can have 20 minutes sessions with a maximum of 4-5 other people under the supervision of an (almost) personal trainer. You can almost go from your living room to the gym in under one minute.

In reference to the discussion of whether or not there will be anything for humans to do when “machines or robots take over” this vignette says that the answer is emphatically that the market for humans will continue to exist even if we end up giving massages to each other.

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Python vs Stata

Here and there I am asked “what I think about python for science” and I must say I sometimes gave a biased answer by saying that python is for children. I could never get over python’s indentation requirement. I am more reluctant with judgement these days, so when I recently wrote a perl script to a young research assistant asking him to complete it and harvest some data for me, went away, talked to his peers and came back with a broken python script instead I did the adult thing and sat down with him to learn enough python to fix it and get him started.

The young research assistant made progress in python and added it to his personal toolkit, wrote a script for me which almost worked but I had to troubleshoot it when I started doing some plausibility checks on the data which created data quality suspicions in my mind. By that time I almost had taken the python learning curve but decided I would be faster to learn python and rewrite the script than learn python and read the assistant’s script. So I did. It went well and I got to appreciate some of the features of python: it paints great pictures, it draws on php, mysql, object orientation, R etc and hides A LOT of the more complex things one usually has to master before they start programming for a living. For example you don’t need to learn regular expressions to remove html tags you just sip a beautifulsoup.
So now that I learned the kids’ language I thought I’d do use it to paint some network graphs for a paper I am doing and then I thought of doing some arithmetic as well. I got in trouble right away. I used numpy, and drew 1000 numbers from N(0,1). When I computed the mean of the created data points it wasn’t zero! So I did this 1000 times and took the mean of all the experiments. The mean I desire to be zero can often be -3 or -9 on the average after 1000 experiments! Here is the code, try it. Maybe your machine does better:

import numpy as np
np.set_printoptions(precision=64)
mu, sigma = 0, 1
ss = []for i in range(1,1000):
s = np.random.normal(mu, sigma, 1000)
ss.extend([np.mean(s)])

ssmu = np.mean(ss)
sssigma = np.std(ss, ddof=1)

ssmu, sssigma

I run this on a Macbook Air with Mas OS Sierra 10.12.2 and the python which comes with Anaconda. In contrast I couldn’t get Stata 14 on the same machine to make as grave errors as python does. Try it yourself:

clear
set more off
set obs 1000
gen mu = .

forval i =1(1)1000{

keep mu
gen t = rnormal(0, 1)
sum t
replace mu = r(mean) in `i’

}

sum mu

If precision matters python ain’t the right thing to be doing, at least on my kind of hardware. If you do use python rerun your paper several times and take the mean of the results before you claim you’ve found something that matters.

If you run these on your machine send me the results of your experiment.

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Toll Index November 2016 – gluhwein season

Based on fresh data by the Bundesamt für Güterverkehr the Toll Index for the month of November 2016 shows that the volume of outbound, border crossing, heavy freight vehicles is at an all time November high on a per working day basis. The Toll Index retreated by 6.3% compared to (a particularly high) October 2016 while it is up by 2.4% compared to November 2015 and up by 18.6% compared to the average November between 2007 and 2015.

The Index like much else in the country is in the gravitational field of the Gluhwein and is headed towards its seasonal December low. The month-on-month rate of descent of 6.3% is larger than the average October-November descent of 3.1% in the time interval 2007-2015.

I expect that German manufacturing will have an accordingly strong showing when the first estimates of the  Production Index for the month of November is published by the BMWi in about four weeks time. The Toll Index for the months of October and November show a strong fourth quarter for German manufacturing for 2016 in accordance with the Markit German composite PMI.


 

The data is available at the Data Repository of the IDSC – Research Data Center of IZA. The paper we first studied the properties of these data is published at the Journal of Forecasting.

The Toll Index has been widely covered in national and international press (selection):

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