As we prepare to attract, develop and retain skilled workers around the world, who works and who doesn’t work is interesting to me. So I thought I’d share the following charts that I ran across in a collection of statistics published by the International Labor Comparisons Division of the BLS. The first shows a comparison of the employment population ratios (proportion of the working-age population that is employed) by sex in 16 countries, adjusted to U.S. concepts.
According to the BLS definitions, employment includes all people who:
- worked at least 1 hour as paid employees, working in their own business, profession, or on their own farm, or worked at least 15 hours as unpaid workers in a family-operated enterprise, and
- all those who did not work but had jobs or businesses from which they were temporarily absent due to vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management disputes, job training, or other family or personal reasons, regardless of whether they were paid for the time off or were seeking other jobs.
(Actually, I don’t know which is more interesting, the definition of employment above or the chart that follows…)
It’s interesting to note the differences in employment percentages between men and women. Turkey (40.7), Mexico (33.4), the Republic of Korea (22.4) and Japan (22) all have differences of 20 points or more between the sexes’ employment rates, and Italy (19.5) is right there as well. Those are big gaps.
But add this to the mix: there doesn’t appear to be a strong correlation between these low employment rates of women and the overall national unemployment rates. See the chart below:
It intuitively makes sense that South Africa with the lowest percentage of women employed in the workforce would also have the highest overall unemployment rate. However the relationship between these two data points isn’t as consistent as we might assume across other countries.
Look at the data for Mexico, Japan and Korea. They all report low employment rates for women and low overall unemployment rates. Not so intuitive.
That’s what I enjoy about people related statistics. Just when you think you’ve figured it out, the data throw you curve ball.
What do you think the story is here? Is it fair to try to find a pattern in data like this? What conclusions can you draw from this?