When I was reviewing Manpower Group Inc.’s 2012 10K filing recently, a couple of risk factors in the report caught my attention — risk factors that are applicable to the staffing industry at large:
- A continuation or worsening of the global economic downturn could result in our clients using fewer workforce solutions and services or becoming unable to pay us for our services on a timely basis or at all, which would materially adversely affect our business.
- We may lack the speed and agility to respond to the needs of our clients.
So what should staffing companies do to address these risks? Would they be better equipped if they knew how these risks affected their revenue and operations?
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To address these questions, over the past few years, I have been researching the link between the broader U.S. economy and the performance of staffing firms.
The growth in demand for contingent workforce in the U.S. will be based on the expectations of growth within the industries in which clients of staffing firms operate. Because most of the companies in the S&P 500 are clients of staffing firms, I started with the hypothesis that the S&P 500 Index is a good leading indicator of anticipated demand for contingent labor.
While there are a number of measures for the labor market like BLS jobs report, unemployment rate etc. that are typically broad and retrospective, the American Staffing Association’s ASA Staffing Index comes closest to an indicator of current demand for contingent labor.
Studying the relationship between these two indices provides some interesting insights. The basic time series plot of weekly S&P500 and ASA Staffing Index (see image below), going back to Jan 2008, shows that the demand for staffing services is positively correlated (+75 percent) to the S&P500. (There are some obvious outliers like staffing demand in second half of December and January which are traditionally low demand seasons.)
Click image to enlarge.
But in order to validate the hypothesis and quantify the window of time available to staffing firms to act on a change in S&P500 performance, we need to understand the effect of a lag in ASA Staffing Index to the S&P500. Further analysis revealed a 7 week lag — if the S&P500 index showed a change one week, the ASA Staffing index showed a corresponding change seven weeks later.
The next logical question is how do we forecast the ASA Staffing Index 7 weeks out given the state of markets? To answer this question a simple regression between the two indices shows the required factors and weights to forecast the Staffing Index. The regression analysis yielded the following equation:
ASA Staffing Index (7 weeks out) = 0.0445 x [S&P 500 Index value] + 32.617
For the statistically inclined, the t-statistic for the intercept and the coefficient were significant at 18 and 28 respectively and R Square = 95.75. In layman’s terms, this model explains 95.75 percent of the variation in the ASA Staffing Index. Is it perfect? No. Is it useful? Maybe, but it’s a far cry from having nothing!
So my fellow staffing executives, now that you are able to quantify the effect of the economy on staffing demand; here are some questions to ponder to align your business.
- How will you plan for the demand over the next 7 weeks? Which skills? Which regions?
- What impact does this 7 week window have on your cost and price structure?
- How will you align sourcing, recruitment and operations capacity to this demand?
- What type of technology enablers do you need to address the 7-week window?
- What is the impact of this demand cycle on your organization’s culture?
But these are for topics for future discussion … stay tuned.
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