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Has Traditional Economic Theory Run Its Course?

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This is an excerpt from our most recent Economic Outlook report. To access the full PDF, please click here.

The last few years have been characterized by a plethora of attempts at reconciling traditional macro-economic models such as the Philips Curve (which postulates a strong relationship between the household unemployment rate and consumer inflation) or the relationship between the money supply and consumer inflation (aptly summarized by Milton Friedman: “…inflation is always and everywhere a monetary phenomenon”).

New empirical realities appear to contradict these models. An inverse relationship between unemployment rates and consumer inflation has been missing in action for several years. In addition, the effect of monetary policy on growth/inflation seems limited. We all serve as witnesses. After a decade of quantitative easing (aka “QE”), money pulses appear to produce muted results. Central banks around the world struggle to better understand the complex transmission channels between lower costs of lending -- but limited growth in business loans.

We want to highlight a number of factors. These contribute to the broken relationships, in our view:

1)    Money growth that leads nowhere. Rapid expansion of the Federal Reserve’s balance sheet over the past decade has barely affected consumer inflation. In particular, the aftermath of the 2008 financial crisis was characterized by an unprecedented increase in the highly liquid money supply in the United States but also around the globe -- in Europe and in Japan. Annual “QE” pulses exceeded the real or nominal growth of their underlying economies by far.

In years past, former Fed Chair Janet Yellen used to blame pricing wars among mobile phone service providers and fixity in prescription drug prices. She is not alone in suspecting. One-off corporate and administrative pricing events could temporarily suppress consumer inflation rates. This, however, led us to believe traditional measures of consumer inflation might need to be overhauled, in order to provide a better measure of pricing dynamics.

A number of recently developed measures can provide more transparency. One of them is a newly developed measure of consumer inflation developed by researchers at MIT. This is based on prices found on the worldwide web, scraped from thousands of online retailers. These provide a much more comprehensive measure of changes to final point-of-sale consumer prices. The researchers showed in a number of studies. Collection of online prices strongly enhances traditional data collection methods. They provide a much more frequent and transparent measure.

2)    The Household Unemployment Rate is probably second to only GDP Growth and Accounting frameworks when it comes to widely-followed gauges on the health of a country’s economy. Yet, computing this Household Unemployment Rate can be highly misleading.    

As measured by the Bureau of Labor Statistics, the ratio uses the percentage of unemployed individuals who are currently in the labor force. While their denominator appears to be a sensible assessment of the U.S. labor force pool currently available, it suffers from a number of shortcomings. The U.S. Labor Force Participation Rate is depicted below.


It’s obvious. The Labor Force Participation Rate fluctuates dramatically, due to structural changes in the U.S. economy (starting with an increasing number of women in the workforce after the second World War). This became the subject of current debate. Workers discouraged by the aftermath of the 2008 financial crisis, and Baby Boomers who keep reaching retirement ages, have caused a massive decline in the pool counted across the last decade.

As a result, economists from the Federal Reserve Bank of Richmond proposed an alternative measure. Theirs is the so-called HKL Non-Employment Index (it was developed by Hornstein-Kudlyak-Lange). They not only count the unemployed, but also those out of the labor force for various reasons (e.g. those who are marginally attached but willing to work. Those who stopped searching. And those who do not want a job). They weight the new groups on how likely it is that they might transition back into employment.

This is depicted below. The red line includes part-time workers to the HKL pool. The blue line is the new HKL non-employment index. The green line is the traditional U.S. household unemployment rate:


The new measures address some shortcomings with the traditional unemployment measure. These take account of structural changes in the U.S. economy, such as a massive increase of part time jobs. Think on individuals working as drivers for Ride Sharing services temporarily.

The above graph depicts the traditional unemployment rate and the Non-Employment Index (including an alternative version showing people working part time for economic reasons). It becomes pretty clear. While all of the lines move in the same direction, the traditional unemployment line shows a much steeper decline after the 2008 crisis (since it would count underemployed part time workers as fully employed). The traditional measure painted a much rosier picture of the economy than the Non-Employment index has.

Underlying causes for this type of different behavior in the respective underemployment measures will be different -- depending on each time period. Structural changes to the U.S. economy, and its intertwined sister the global economy, are relentlessly ongoing.

In addition to these HKL measures, researchers at Cornell University recently developed the U.S. Private Sector Job Quality Index (JQI), a metric tracking the ratio between high- and low-quality jobs in the U.S. This was released for the first time in December 2019. Since the inception of the historical data in 1990, their ratio has declined -14.4%. Thus, another confirmation. An impression -- of a negative quality-adjusted structural change -- affecting the U.S. economy’s current labor force is seen again.

We conclude: it is crucial for large scale U.S. economic models to account for this type of meaningful change seen in the empirical data. In order to provide more robust macro policy and financial market guidance across longer time periods.


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