Self-Fulfilling Job Reports: a Better Approach to Monthly Employment Data
Most recently, for example, David Stockman, former Director of the Office of Management and Budget in the Republican Reagan administration, in a Wall Street Examiner-published lengthy email commentary on the reported January Labor Force Participation rate, set against a backdrop of prop-up feel-good good-news interpretations of the data by former Clinton Secretary of the Treasury Larry Summers, said, “In short, if you spend a little time with these numbers you will know that they are being made up.”
After endless counterclaims and contentious debates like this, about the veracity or accuracy of job reports, the mind wearies and longs for something more clear-cut, something less suspect. Given this backdrop, it would be a huge improvement to have job data that are
- More transparent
- Computationally simpler
- Predictively more useful
- Less open to dispute
- Less susceptible to devious manipulation
Such an overhaul of the way the job numbers and growth/shrinkage rates are reported is indeed possible—a method of computing job growth and loss data that, in addition to being utterly simple, generates statistics that are easily confirmed or disconfirmed. That format is what I shall call “self-fulfilling job reports”. Here’s how it would work.
The “Self-Fulfilling Job Report”—a Proposal
At the end of each month, the government would report job growth, stagnation or decline (in percentage terms or absolute numbers), but not for the previous month—instead, for the coming month, as a prediction, but not a conventional prediction. Rather than being a prediction of how the economy will perform per se, it is a prediction of how many jobs the prediction itself will create.
In other words, the jobs report will actually be a prediction of the extent to which the prediction of growth (or contraction, if needed to cool an overheated economy) will be self-fulfilling, creating precisely the growth rate that the prediction specifies. For example, a reported decrease in the unemployment rate, to, say, 7% would be interpreted as the prediction that this specific prediction of a decrease in unemployment to 7% would generate sufficient optimism in the economy for hiring and rehiring to take off and hit precisely that target rate.
Suppose, given the rate of inflation, unemployment and other parameters, such as the need for election-year feel-good news, the government decides that it wants to have a reduction of the unemployment rate to an attainable 7%. The conventional method, which an incumbent administration will predictably be accused by its critics of utilizing, is to cook the numbers, e.g., by reporting huge reductions in unemployment, while omitting to mention whether the gains were due to increases to unacknowledged, but normal seasonal variations or in the numbers of people without jobs who have simply given up or used up their unemployment benefits and are therefore no longer counted among the unemployed.
Such a contrived cooked-book approach is like reporting that the worst of the Black Death would have been over sooner if all the able-bodied inhabitants of a plague-ravaged medieval town had merely been asked to report to local health authorities to prove they were uninfected. Plague free rate in that population: 100%! Happy days are here again, or would have returned earlier!
The “self-fulfilling job report” (SFJR) approach would be free of such duplicity and jugglery. Unambiguous, consistent and honest demarcation of the employed from the unemployed and of jobs from pseudo-jobs (such as ad hoc, temporary and unproductive sinecures created by local, state or federal government fiat) would provide essential transparency essential to assessing the predictive accuracy of the self-fulfilling jobs report, at the end of the coming month covered by the prediction.
How an SFJR Scheme Could Work
Such an SFJR scheme just might work. Think about it: What drives the economy? What stalls it? Optimism and pessimism, respectively. So, after careful deliberation about what job numbers and employment participation rates the economy needs and can accommodate in the short or near term, various levels of government can collaborate to set the target numbers and rates—much like the old Soviet-style quota system, but with a huge difference. Instead of demanding or forcing the attainment of the target, as the Soviet quota systems did, the SFJR would, without being at all heavy-handed, inspire it.
“Today, the government announced the SFJR for April and is predicting a robust increase in employment, with 500,000 jobs predicted by the end of the month.” The good news causes not only the predictable spike in the stock market, consumer spending and borrowing, etc., which is what is happening now, as the numbers improve in an election year, but also in hiring, re-hiring, investment and other forms of economic growth-promoting activity.
Failed Prediction? No Problem
If the self-fulfilling prophecy effect of the SFJR turns out to be weaker than the prediction predicted, the number can easily be revised retroactively, with absolutely no adverse implications for the next month’s prediction. Since virtually no one stops listening to TV weathermen when they are wrong (which empirical studies show has been about half the time—at least in Vancouver), no one will dismiss government SFJRs merely because they are wrong occasionally, or even often.
There is a precedent or president for this kind of flexible jobs-report revisionism: In a February 9, 2012 news story titled “White House Reveals Jobless Forecast—But Disavows It”, Reuters reported that, “President Barack Obama will forecast a U.S. unemployment rate averaging 8.9 percent in 2012 in his annual budget on Monday—but before the document was even released a top aide called the projection ‘stale’ and said it should be lower.”
“We would certainly lower our forecast of the unemployment rate from the figures that will appear in Monday’s Budget if we were to do another forecast today,” chairman of the White House Council of Economic Advisors Alan Krueger said in a recent e-mail, cited in the Reuters report.
Given this kind of retro-flexibility, making self-fulfilling predictions of labor force participation rates, unemployment rates and the like becomes even more attractive—especially if they are longer-term, e.g., bi-monthly, quarterly or even semi-annually, which would allow the predictions sufficient time to take effect and translate into increased hiring, investment and spending.
Choosing the Right Time Frame
The key feature of the SFJR that must be retained by all means is the self-fulfilling nature of the SFJR predictions: What they predict must be participation or employment rates based on an informed estimate of the impact of the very same prediction on the sentiments of the market, employers, investors and anyone else in a position to push the rates up, down or to leave them unchanged when necessary. To maximize the likelihood of a successful self-fulfilling prediction, the right time frame has to be selected.
The spike in the Dow Jones just after the latest January figures were released illustrates the best-case immediate-impact conventional prediction scenario—an effect highly dependent on the relative liquidity of stocks. Prediction as stimulus, market uptick as immediate Pavlovian response. However, other economic processes and wealth that are not so rapid or liquid, e.g., construction of new infrastructure or term deposits, are likely to require a longer time frame for manifestations of their job-rate and labor participation-rate impacts. Bi-monthly quarterly or semi-annual reports provide and allow for that extra time.
A predicted impact of this kind would look something like this: “Our best estimate is that if we announce the January numbers in advance, the Dow Jones will rise ____points within ___days.” Now, this is not a pure self-fulfilling prediction, since it is using one prediction (about labor participation or unemployment rates) to make another, different prediction (about stock-market performance). But if a job-rate prediction-induced spike in the Dow Jones could, in turn, correctly be predicted to lead to the future job participation rate changes caused by the very same predicted participation-rate changes, the participation-rate predictions would then be part of a genuine SFJR—a true Self-Fulfilling Job Report.
Two Kinds of SFJR-Induced Boosts
There is, in addition to the economic lift a SFJR can create, a substantial morale boost: Given the very positive intentions and frequent positive outcomes associated with an SFJR, those whose job it is to make such predictions would find their jobs and projections professionally very fulfilling.
Even when the predictions turn out to be wrong and not very self-fulfilling….
…except emotionally.