I was prompted to write this post after reading the NYT’s In France, New Review of 35-Hour Workweek. For those not familiar with the 35-hour workweek, France adopted it in February 2000 with the suppport of then Prime Minister Lionel Jospin and the Minister of Labour Martine Aubry. Simply stated, the goal was to increase quality of life by reducing the work hour per worker ratio by requiring corporations to hire more workers to maintain the same work output as before. This in theory would also reduce the historic 10% unemployment rate.
I mostly write about ML, but I’ve been meaning to write about Pandas’ latest features and tight integration with SciPy such as data imputation and statistical modeling, and the actual working hours of EU countries will serve as fun source of data for my examples. I found data on the annual average hours worked per EU country from 1998 to 2013 on The Economic Observation and Research Center for the Development of the Economy and Enterprise Development website. This notebook won’t serve as in-depth research on the efficacy of this policy, but more of a tutorial on data exploration, although a follow-up post exploring the interactions of commonly tracked economic factors and externalities of the policy might be fun.
In this IPython notebook we’ll work through generating descriptive statistics on our hours worked dataset then work through the process of interpolation to and extrapolation as defined below:
Interpolation is an estimation of a value within two known values in a sequence of values. For this data this might mean replacing missing average hour values in given date positions between the min and max observations.
Extrapolation is an estimation of a value based on extending a known sequence of values or facts beyond the area that is certainly known. Extrapolation is subject to greater uncertainty and a higher risk of producing meaningless data. For this data where the max observed date is 2013, we might want to extrapolate what the data points might be out to 2015.