Productivity: Experts’ View

While talking about the economic output per hour and hours worked in different countries, I had a question about estimating productivity in the IT industry:

I also have a question for anyone who knows economics more than I do. Everyone I know in the I.T. and other hitech fields works 50-60 hours a week but doesn’t get any overtime. Do they count as 40-hour work-week for calculation of productivity numbers?

Stephen Roach of Morgan Stanley provides some analysis:

In the four quarters ending 2Q03, the increase in manufacturing productivity (3.5%) was below the gain in overall nonfarm business productivity (4.1%). A strong increase in services sector productivity is the only way to reconcile these numbers; my rough guesstimate points to nonmanufacturing productivity growth of 4.5% on a year-over-year basis —- about 30% faster than measured gains in manufacturing.

This is where the productivity miracle falls apart, in my view. I honestly don’t think we have a clue as to how to measure productivity in the white-collar services sector. The problems lie both in the numerator (output) and the denominator (labor input) of the productivity equation. The production of the proverbial ‘widget’ makes measurement of tangible output in the manufacturing sector relatively easy by comparison. The intangible output of services is a different matter altogether. Measuring quality-adjusted value-added in knowledge-based activities is tough in theory and virtually impossible in practice. Yet that’s exactly what the productivity metric requires us to do. Is it correct to measure the output of a software programmer, for instance, by the lines of code that he or she writes? Or the number of words that an analyst produces? Or is less more? To me, the efficient software program and the insightful piece of analysis wins, hands down. Try measuring managerial output —- hardly a trivial consideration given that managers account for fully 25% of America’s total white-collar workforce.

As tough as it is to measure the numerator in the white-collar productivity calculus, I have long been equally critical of efforts to capture the denominator —- labor input. The official data on labor input comes from the establishment surveys of the US Bureau of Labor Statistics; at the crux of this gauge is an estimate of the length of average work schedules. For white-collar knowledge workers, these numbers simply don’t make any sense to me. Take financial services —- an industry in which I have spent my entire career. According to the BLS, the average workweek in the financial activities sector was 35.4 hours in July 2003 —- essentially unchanged from the level a decade earlier (35.6 hours in July 1993). I find that most difficult to fathom. Over the past decade, the IT-enabled knowledge worker has seen a radical transformation of work schedules. Courtesy of miniaturized and portable information appliances, together with near-ubiquitous connectivity, workdays have been extended as never before. Yet in this increasingly ‘24 × 7’ mindset, the official data speak of unbelievably short and unchanged work weeks. What a disconnect!

To me, this smacks of a classic measurement problem. The official data seem to underestimate woefully actual hours worked in America’s increasingly knowledge-based, white-collar economy. We are guilty of confusing extended work schedules with productivity growth. I’ve said it before: Productivity is not about working longer. It’s all about generating more value added per unit of labor input. To the extent that government statisticians are undercounting work time, it follows they are guilty of overstating productivity. With America’s newfound productivity gains skewed increasingly toward the white-collar services sector, this statistical conundrum takes on even greater meaning for the economy as a whole.

Brad DeLong disagrees with his analysis.

The first criticism is only half-right because the bulk of white-collar service-sector work—including virtually all of managerial work—are themselves inputs into further stages of the production process. The management of Daimler-Chrysler helps the rest of Daimler-Chrysler make cars. The management of Nike helps the rest of Nike make shoes. We know what a car is. We know what a shoe is. To the extent that we overestimate white-collar productivity in Daimler-Chrysler’s and Nike’s value chains, we automatically underestimate blue-collar productivity because the combined output of both—quality-adjusted cars or shoes—is something we know about. It is very possible that we are overestimating white-collar and underestimating blue-collar productivity, but such errors should cancel each other out for the economy as a whole. And yet the statistics for the economy as a whole are very impressive.

The second criticism is also only half-right. Because people are easier to reach, they are spending less time hanging around the office twiddling their thumbs waiting just in case somebody needs to reach them and learn something they know. Because people are easier to reach, they are being bugged more and made to work more outside formal normal working hours. Which effect dominates? I don’t know. I do know that people seem to prefer the wired to the hanging-around-the-office lifestyle, and prefer it by quite a bit. But I live near the center of where the most action is. It’s not clear to me whether Stephen Roach’s point is quantitatively important, indeed, it’s not clear to me that it cuts in the direction he thinks it does.

Moreover, there is a third potential criticism of the productivity numbers that Stephen Roach doesn’t make, and that I wish he would: the speed-up criticism. More and more, blue-collar and lower-level white-collar workers can be watched, monitored, and assessed. The pace at which they are expected to work can be ratcheted up with much more ease than in past ages. Is this factor—either the reduction in paid on-the-job leisure, or the breaking of the wage-effort social contract in the interest of extracting more value for each wage and salary dollar, depending on your viewpoint—important? I do not know. I wish I did.

No, I don’t have any intelligent comment myself.

Author: Zack

Dad, gadget guy, bookworm, political animal, global nomad, cyclist, hiker, tennis player, photographer