In a rebuttal to gainsaying that there is no connection between ObamaCare’s employer mandate and part-time job creation, Duke University researcher Chris Conover has discovered an astounding statistic.
Thus far in 2013, part-time job creation is more than quadrupling full-time job creation (defined by BLS data as 35 or more hours per week, even higher than the 30 hours or more per week that the PPACA stipulates). How extraordinary is that?
What should immediately be obvious to even someone without a shred of statistical training is how deviant the 2013 experience is compared to the past. For every new FT job added to the economy, there were 4.3 PT jobs added! In most (non-negative) years, the ratio is the reverse: that is, there are typically 5 FT jobs added for every new PT job. Even in 2004—the year with the second-highest ratio during this time-frame–there were 2 FT jobs for every PT job, yielding a ratio of 0.5. Even if growth in PT vs. FT workers reverted to its historic pattern for the balance of 2013, the year’s average monthly ratio still would be four times as large as the 2nd highest ratio from 2004.
In the average non-negative year, there is exactly the reverse trend — 5 full-time jobs for every part-time job created! (These are scaled as ratios, and not direct mathematical relationships. Thus, the average yearly non-negative ratio for part-time to full-time jobs created would be 0.2.)
An academic researcher at the Center for Health Policy and Inequalities Research, Conover developed a method of clearing out statistical noise by comparing the ratio of part-time to full-time jobs created in a given year (based on monthly data) and then comparing that data year-on-year. Briefly, how did Conover compile this data?
For the most part, an examination of metrics measured in millions (e.g., involuntary PT workers or total PT workers) masks what is really going on. A much better sense is given by comparing the changes in PT employment to the changes in FT employment. Because the monthly Current Population Survey are so volatile, it is easier to see what is going on by calculating an average monthly figure for each calendar year to get a sense of whether the number of PT or FT is rising or falling.
We only have six months of data for 2013, but this method allows us to compare the average monthly count for the year to date with the average monthly count from prior years on an apples-to-apples basis. We can then calculate the ratio of new PT workers in an average month to new FT workers in an average month. Obviously this ratio will turn negative in years that either FT or PT workers have declined on average. So over the past decade, there’s only 4 other years with which to compare the 2013 experience.