Continuous transformation or a transitional approach, which path do you prefer?

Janet Yellen Testifies House Fniancial Services Committee

Earlier this week, Janet Yellen told the House Financial Services Committee that no decision has been made, but shared the Federal Reserve Bank‘s expectations via the Wall Street Journal.   “The economy will continue to grow at a pace that is sufficient to generate further improvements in the labor market and to return inflation to our 2% target over the medium term, and if the incoming information supports that expectation, then…December would be a live possibility. ”

Wow, lot’s of room in those statements.  Both US Stocks and bonds slipped following her remarks. It’s become commonplace to link Fed Chairpersons’ remarks to the rise and fall of the markets.  I’m not so willing.

Maybe because my thoughts of late were influenced by a conversation hosted by the Becker Friedman Institute I attended last week.  Entitled The Role and Impact of Monetary Policy in an Uncertain Economy  and included Charles Plosser of the Philadelphia Federal Reserve  and nobel laureate Lars Peter Hansen.

First, I’m with Plosser in sensing that it’s foolish to expect that  the Federal Reserve’s control of the money supply and interest rates can be used to effect both inflation and domestic employment.   Second, we need to be cognizant, as Hansen advises, that models can be very helpful but are not exactly the same as direct relationships.  Politico made a similar point. “The Fed and its staff, like any good economists, rely on past patterns as a guide to future outcomes. And now, those patterns are no longer working…:

In fact, it’s the latter thought the differentiation between modeled certainty and certainty deserves more attention.  I’ve been unpacking and exploring this in a variety of ways .  Here’s one:

I know with certainty the relationship between the gas pedal and the degree of pressure applied by my foot and the acceleration of my car.  Janet Yellen and the Fed no matter how experienced and accurate the input data, the econometric models relationship to the economy remain uncertain.  It’s why changes in pressure they apply to expand or contract interest rates have a fuzzier relationship to the economy, and the measurable results more complicated and less consistently understood when compared to my car’s observed speed when I hit the or lighten up on the gas.

Modeled certainty when it fails produces uncertainty but doesn’t mean stop, or does it?

I only watch the dashboard in my car.  I used to have a ticker list of stocks I followed, but no more.  I also rely on the weather app on my phone even though it’s not very accurate. Why? Well it’s useful to be prepared for forecasted conditions, even though several are beyond my control. Yes the weather is uncertain inspite of models who do their best to assure us.

I’m not alone in my struggle to understand and interpret the signals around us, especially the indicators of the health of the American economy and the global economy.  For one, its more complicated than the working of my car, which I also don’t fully understand. The dashboard guides me, it reminds me that the gas tank needs refilling or that in a particular area I may need to reduce my speed, or if the other lights go on I should get a mechanic to take a look.

Today the growing interconnections between sensors, and communications technology make makes it possible to funnel more information to me in real time than ever before.  So, what value do additional indicator really offer? What does knowing more change? The answer is it changes everything, but not necessarily in a predictable way.

Experience, does affect how we process information. Our brain uses experience to filter out commonplace or the “usual” details in our midst. Organizationally, experience used to model and plan the allocation of resources and assure us with forecasts based on different decisions.  The bigger the organization, the more careful and challenging the coordination and planning activities.

When I was a kid, I heard the expression “As goes General Motors, so goes the country.” I didn’t know the first thing about economic indicators, or inflation rates.  My family bought GM cars, so when my grandpa bought a new Buick, things were going well. Conversely, things were going less well when my father continued to drive his Pontiac long after a small hole in the floor board  appeared spurting water when we’d hit a puddle.

GM of course was until recently not just one of the world’s automakers, it’s activities were deeply embedded into the economy.  A report by US Auto Alliance , quantified the importance of the automotive industry in the U.S. economy  claiming:

  • more than seven million private sector jobs and $500 billion in compensation,
  • drew foreign direct investment (FDI) currently valued at $74 billion—approximately 3 percent of all FDI in the United States.
  • And collective investments of almost $46 billion that expanded and retooled U.S.‐based facilities since 2010.

It take a reasonably long time to build a car, but people don’t buy them very often, so supply can generally keep up with demand. If we use GM as a litmus test for the economy there’s some wise and prudent parallels becasue there’s a lot of interdependencies between larger sentiments and people’s financial capabilities.  In contrast, fast food offers a set of alternative indicators to measure the pulse of the economy. In May of 2015, US news speculated about the inverse relationship between the two in an article entitled “McDonald’s earnings slide could be a function of economics. Besides, McDonald’s is the 2nd  largest employer in the country, trailing WalMart. Not surprising given its 14,300 restaurants –4.6 outlets per county.  (I plan to explore this idea more fully in a post I’ve drafted called  McDonald’s a truly American Story).McDonald's Workforce, 2005-2014

See http://fortune.com/2015/06/13/fortune-500-most-employees/

I only point to these two companies becasue I think it’s important to notice the difference between government actions and companies responses to changes in external conditions.

BCG put it this way:

“To compound matters, the diversity of the business environments—in terms of growth, rate of change, and harshness—that global companies face is expanding in a multispeed world. So it is not surprising that many companies find their strategies and business models increasingly out of step with their environments.

Many companies get caught in a “boiling-frog trap,” where they fail to recognize the problem and delay efforts to remedy it, thus necessitating a painful and risky step-change transformation.”

Is that what you want the Federal Reserve Governors to do?  I hope not.  It’s why I don’t envy them nor am I ready to second guess them.  In reality no one should let uncertainty about monetary policy and interest rate hikes hold up your planning, I would encourage you to take a harder look at the relationship between the micro as well as the macro trends in your industry. You don’t need a data scientist per se to create an elaborate model, but it can’t hurt.  The trick is to merely face the realities.    Try to imagine how your customers adjust and see if these factors are included in your own models, you might fill in a few more gaps..a sustainable path is up to you.

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