The final post of this series documents my conclusion and analysis of the available data described in Part 1 and Part 2. As reported in my earlier post, some recruiters and organizations claim a decrease in applications per opening in December. This series examines and analyzes the available data behind this assumption.
To analyze these trends I created a database containing monthly data from January 2004 until December 2012. This series contained 108 time observations of 15 variables. This post is a little delayed as I had to wait until a very recent data release from the Department of Labor Bureau of Labor Statistics Job Openings and Labor Turnover Survey (JOLTS) and from other DOL data series. Oregon specific data comes from the Oregon Labor Market Information System (OLMIS). The central variable I used as the dependent variable in my models is a data set I created from weekly Google Search Index of web searches in the United States for jobs under the jobs and education sub group. To be more specific this was a quantified version of weekly searches over time provided by Google that I averaged to get monthly data series. I used a specific search category to avoid cross chatter from search for topics like the late Steve Jobs of Apple. Most of the searches involved Indeed, Craigslist, Google and Yahoo (both job posting sites and search tool sites).
I created several econometric models to investigate the effect of the holiday season on the labor market and job applications. I used the Gretl program, a cross-platform software package for econometric analysis, written in the C programming language. I used an OLS regression analysis to check correlation and connections between the data points. These correlations could then be analyzed to be good indicators of holiday hiring and job applications. All data is not seasonably adjusted to better look at the actual holiday months, rather than massaged data to account for cycles. In this study I examined the cycles themselves, thereby necessitating unadjusted data.
My first regression used the Google Search data as the dependent variable. In other words I wanted to see how it correlated with the other data. For the independent variables I used new jobs added, number of hires, the unemployment rate, job openings, layoffs and discharges, other separations and the number of voluntary exits or quits(all monthly for the United States). The data for job openings, layoffs and quits was not connected to the Google search index at any significant level. I also ran the results with Oregon specific statistics and got similar results. In other words, the number of layoffs, job openings and quits does not seems to directly correlate with Google job searches. The variables for the unemployment rate (both locally and national) did however have a strong correlation and coefficient with the Google search index. We can interpret this simply that historically, when the unemployment rate goes up 1%, the Google search index also goes up about 6%. The empirical observation is that a higher employment rate increase the number of job seekers thereby increasing the number of job searches online. The Search data also had a statistically significant correlation with “other separations” and new jobs and hires. The full regression results are below for thew more wonkish audience. If you are not interested, you can skip ahead.
Some stray observations:
- Some data is skewed by the Great Recession in the middle of the time series. See the chart below for an example.
- Very strong correlation between the unemployment rate and the jobs search index. The coefficient is 6.2 with a very small p-value. If the model is correct, then the coefficient implies that a 1% increase in the unemployment rate would generally correlate with a 6.2% increase in web searches for jobs. The small p-value means that it is unlikely that the data would look like this randomly.
- I added a late variable of unemployed per opening. This is the number of job openings divided by the total unemployed population. It shows exactly how competitive the job market is.
- Really interesting that the unemployed per hire has an inverse relationship with the Google job searches. The only way I can explain that is that as a failure of the model or that the Great Recession affected the searches in unexpected ways (see point 4 below for more details). Another explanation is that there are far fewer new jobs in December, making the ratio of new jobs to unemployed seem normal or higher in December.
- Between 2004 and 2012, on average there are 1 million fewer unemployed people during December, compared to the average for the rest of the year. Therefore we can convulsively say there is a smaller labor pool during the holiday season. The number of unemployed per opening in December is almost the same as the number of unemployed per job opening for the entire year (3.2).
- Search Index on average declines 9% in December.
- Job openings and new jobs significantly down due to hiring in ramp up to the holiday season (e.g. October and November).
- The Unemployment rate is typically down about .1% on average in December.
- Quits were always way down in December, about 450,000 less on average. Another strong indicator that the available labor force shrinks during December, particularly passive candidates, see #5 below.
- For a graphical example of job creation and the following decrease in job searches, see the chart below. The outliers to the left are time series during the worst of the 2008-2009 recession. During normal economic conditions, increased numbers of jobs correlates with a decrease in the job search index.
We conclude that businesses do indeed receive fewer applications in December for a variety of reasons.
- Fewer job openings occur during the month of December. This is because Holiday hiring occurs in the months going into the season, not during the busy peak period.
- There are fewer unemployed people in December, therefore there are fewer applicants in the active labor supply pool.
- Currently employed individuals who look for new jobs typically slow or stop their search during the holiday season. However the volume of job searches is more closely related to the unemployment rate than the number of employees leaving their jobs.
- Google searches for jobs tends to follow conventional economic wisdom. Meaning that job searches increase during periods of high unemployment, however the month to month change in December is counter intuitive. The smallest decline in job searches occurred during December 2007-2010, the only way to explain that is to accept that passive candidates stuck with their jobs and avoided searching due to the terrible economic conditions. On the bright side here, in 2012 we saw the lowest decline in holiday job searches since 2004, meaning that people feel comfortable enough to search for jobs they may have held during the recession.
- The unemployment rate is down and passive job seekers do not search for jobs as often in December. Therefore the labor market supply and total applications decrease during the holiday season. The data backs up the empirical and anecdotal evidence.
The fundamental takeaway is twofold. For job applicants, December is a good time to apply for jobs due to the fact that much your competition may have taken a break for the job search. More importantly, the most qualified and experienced candidates (i.e. your strongest competition) seem to search for jobs less in December. For employers, you are likely to get fewer applicants in general and of the highly qualified sort in December. You might want to increase your December recruitment advertising budget or extend your search into January for a larger labor pool.