My friend and fishing buddy Brian Barnier is Director and Head of Analytics for ValueBridge Advisors (US) and Burnt Oak Capital (UK). He focuses on growing companies and investments through an emphasis on the application of technology to process improvement and the understanding of product, market, and economic trends. He has an analytical edge in risk management that comes from being a whiz at systems analysis and advanced data visualization. He is co-founder and editor of the economic and market risk site Fed Dashboard & Fundamentals (www.feddashboard.com) and the author of The Operational Risk Handbook and over 200 professional articles. He teaches a graduate seminar at the Colin Powell School at the City University of New York.
In the guest commentary that follows, Brian gives us a concise, practical seminar on root-cause analysis, a mindset and methodology that I learned from Brian and have found endlessly helpful.
Now, here’s Brian.
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Think about using your car navigation app to route around congestion. Have you used a fish finder to locate a great place to drop your line? Or did you repair the air pump for your live bait bucket because bait fish need air to stay alive? Better yet, have you repaired an outboard motor? Have you watched an outboard motor or anything being manufactured? All of these activities are about systems and involve root-cause analysis that was refined during World War II. Yet these tools are rarely used in monetary economics.
By contrast, monetary economics largely runs on a strain of math borrowed from physics before World War II and dedicated to searching for high-level equilibria. Monetary economics, then, relies on abstract calculations rather than on pragmatic investigations into how the mechanics of how an outboard motor actually work, including that filters can get clogged.
When central bankers say “data-dependent,” they mostly refer to aggregate averages, such as all the fish in lakes in Maine rather than the smallmouth bass in West Grand Lake. Or, central bankers might fret that fishermen are not taking enough fish – without realizing that fishermen might be shifting preferences to “catch and release” because having a model mounted on a wall, created from a photograph of a trophy fish, is just fine.
Overlooking systems and root-cause methods (from operations research and related fields) leads central bankers into two mistakes: not drilling down into deeper data and not updating variables in formulas (e.g., potential GDP) to reflect current dynamics.
This flawed methodology has led to missing the magnitude of change in our world, especially in two areas: tech (including management technique and technology) and trade.
The global tech and trade transformation has led to falling costs (goods other than food and energy have been falling in cost since the mid-1990s) and the global rise in financial assets (sparked by regulatory and technology changes in the late 1980s). This sea change resulted in a shift from scarcity to abundance.
Most all the systematic errors in central bank forecasts and actions can be traced back to overlooking this math, data, and change.
Within monetary models, these root causes are widely overlooked:
- * The long-run aggregate supply curve for goods, especially durables, hasn’t been upward-sloping since the mid-1990s. This means people tend to buy more, not less, when prices fall. And, it means that central bank attempts to increase prices are more likely to cause stagflation than growth in units purchased.
- * Potential GDP is much higher than assumed because excess capacity in labor hours is much higher than assumed (leisure – work preferences before 2009 are ignored, especially those of the early 1970s, when people worked more). Improvements in equipment (the capabilities of new equipment and the speed of putting it in place: oil fields, data centers), structures (high-density offices and smaller-footprint manufacturing), or a championship fishing lake by 5AM in the summer also augment potential GDP but aren’t sufficiently accounted for.
- * Economic growth is now less limited by labor hours simply because the labor hours needed to produce results have been falling with improvements in management technique and technology.
- * Wages won’t necessarily rise when workers become “service humans” providing support to robots, because of the falling cost and increasing capabilities of automation (whether for equity trading or for assembling cars).
- * Labor productivity is not widely weak, and weak labor productivity isn’t necessarily bad when businesses prioritize improving returns on assets (especially expensive ones) and returns to investors.
- * Multifactor productivity (MFP) is less and less a measure of “technological progress” now that technology inputs have been explicitly included, especially since a 2008 national accounting change. MFP is a residual of outputs compared to inputs. Practically, its value diminishes the more detached it becomes from business measures of production and returns.
- * Business fixed investment doesn’t necessarily rise with a lower effective federal funds rate because (1) the effective fed funds rate is a small part of the net present value equation used for investment decisions and (2) the cost of productive capacity (equipment and structures) for a given level of output has been falling.
- * Natural interest rates are lower than assumed because they are set more by global funds flows than by production levels; and, again, the cost of fixed investment is now less.
- * It is generally assumed that most of the weighted-average product price change (as reported in price indices and deflators) is due to monetary (money-supply and interest-rate) causes. That assumption might have been reasonable in the 1970s, but it has been less true since 1981. When the root cause of a product price change is technology, management technique, online shopping, or government policy (regulatory, trade, tax, or subsidy), central bank monetary policy tools have had little effect. Central banks need a method to separate monetary causes (that central banks can address) from nonmonetary causes (that they rightly punt to other agencies of government).
- * A central tendency is assumed in average price indexes or deflators, rather than the dramatic dispersion of price trends. With prices having multiple modes (starting with the simple split of goods and services), targeting “average” inflation makes no sense. Landing at the average location of two airports is a crash.
- * Looking at growing research on the prices of complex and changing products (from mobile devices to medical treatments), we find it likely that the measured average price level increase is significantly overstated. If so, then real interest rates have long been higher and natural rates have been lower than assumed.
These examples cascade through monetary models and decisions. Just as with the basics of baiting a hook and casting a line, monetary models might benefit from factory-floor-style root-cause analysis and continual improvement. The goal is to catch a great fish, not get injured by an error with a hook.
We thank Brian for sharing this with our readers and wish him “tight lines”. For readers who can make last minute changes, there is a little space left for the June 21-22-23-24 gathering at Leen’s Lodge. Go directly by phone to Scott Weeks, 207-796-2929. August is now full. Labor Day is nearly fully reserved with few spaces remaining.
David R Kotok
Chairman & Chief Investment Officer
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