Monday, February 24, 2020

What’s Good for Me is Good for You

There have been variations of this theme over the last hundred years or so with regard to corporations: “...What was good for our country was good for GM, and vice versa.” If you go further back, you get one of the French king’s statements regarding that what’s good for the king is good for the country—“L’etat c’est moi” (The state is me)—only to be recently echoed by Alan Dershowitz during President Trump’s impeachment trial in the Senate. Trickle-down economics is another form that what’s good for the upper class will be good for all classes: “a rising tide raises all boats”.

On a microeconomics level, we see this sentiment between executives, management and labor. Boards of directors have rationalized excess executive pay, bonuses, stock options, etc. If the executive team is encouraged to fight for their own benefit, they will also create financial success for the shareholders and create financial security for employees—or a booming economy that will be good for them to avoid layoffs. Friedman convinced many that the board’s and executive team’s sole responsibility was to drive up share prices. Federal (e.g. SEC) laws and corporate governance rules have been built on this theory. In the last twenty years, it’s been questioned as corporate social responsibility (CSR), B-Corporations legal/tax structures and investment firms (e.g. Blackstone and others) and large investment houses have promoted environmentalism, community welfare, etc.

It seems a lot of economic advice is based on unproven theory, anecdotes, personal preferences and very little data...until recently. We can forgive those from before the 20th century because the data wasn’t really available. Adam Smith, Karl Marx and others could only opine based on their own experience, personal or prevalent political philosophies and relatively few data points. Until the 20th century, the study of capital was called political economics until Alfred Marshall published the Principles of Economics in 1890 eliminating the word ‘political’ from the study. Into the 1970s, 1980s and 1990s, there were few databases in which to do rigorous analysis of fiscal policies and determine their effectiveness. Instead there was but a small group of data from which economists extrapolated all kinds of theories to explain causal relations. Keynes, Friedman, Stiglitz, Solow, Krugman, Shiller, and others had to manually calculate any algorithms, graphs. Based on a paucity of data and ability to determine if we’re confusing correlation with causality, it’s amazing that many of the theories have survived.

Many haven’t as newer economists—some of the Behavioral Economism branch—show that people as individuals, groups, nations often operate not as perfectly rational beings. National and corporate leaders, not to mention most of us on Main Street and Elm Street, can’t know all of the data and can’t analyze it all into effective information. Many assumptions form the foundation of our analyses. Even one quick example from the disciplined field of corporate valuation: the extensive methodology behind discounted cash flow assumes: 1) a level of growth; 2) a reasonable cost of equity. Vary those two factors even a small bit and the valuation of a company can radically change. If you want a larger example, you could review a range of expectations on the US government debt for the range of GDP forecasts and tax revenue forecasts. Again, it seems many have acknowledged the social connections of our world through the latest promotions of CSR, B-corps, etc.

A few economists recognize that there’s a tension between accepted theory and data and institutional knowledge/tradition. Data is supposed to be the most objective of the three but the selection of data is very subjective. Many, including the author of Weapons of Math Destruction, argue that even algorithms have the same biases as the creators of the algorithms. Testing those algorithms may tempt some to omit/disregard any adverse results and outliers. Think about the call for the hard sciences—biology, chemistry, physics—for publishing even the failed experiments i.e. the ones that cannot reject a hypothesis under review and therefore coming to a firm conclusion. Experiments might be repeatable and reproducible—1out of some number of times—but may not be reproducible and repeatable enough to be ‘truth’. Those sciences have realized that everyone would benefit from knowing a lot more results. Likewise, economics and corporate strategies might benefit from similar publishing of the failed experiments, failures. In business press, we only know about the few successes of any methodology and strategies; we don’t know about the hundreds of other companies that attempted the same and failed miserably.

With what are we left when we don’t have this perfect knowledge? Our own experience and our corporate traditions about how to operate and drive success. We are stuck with “What’s good for me is good for you.” Lately, we know that doesn’t sound totally correct. So let’s go looking for the better economic theories that have done a thorough analysis of all the data available in this Internet age.

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