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Hoisington Investment Management - Quarterly Review

April 21, 2014 | About:

Optimism at the FOMC

The Federal Open Market Committee (FOMC) has continuously been overly optimistic regarding its expectations for economic growth in the United States since the last recession ended in 2009. If their annual forecasts had been realized over the past four years, then at the end of 2013 the U.S. economy should have been approximately $1 trillion, or 6%, larger. The preponderance of research suggests that the FOMC has been incorrect in its presumption of the effectiveness of quantitative easing (QE) on boosting economic growth. This faulty track record calls into question their latest prediction of 2.9% real GDP growth for 2014 and 3.4% for 2015.

A major reason for the FOMC’s overly optimistic forecast for economic growth and its incorrect view of the effectiveness of quantitative easing is the reliance on the so-called “wealth effect”, described as a change in consumer wealth which results in a change in consumer spending. In an opinion column for The Washington Post on November 5, 2010, then FOMC chairman Ben Bernanke wrote, “…higher stock prices will boost consumer wealth and help increase confidence, which can also spur . Increased spending will lead to higher incomes and profits that, in a virtuous circle, will further support economic expansion.” Former FOMC chairman Alan Greenspan in a CNBC interview on Feb. 15, 2013 said, “The stock market is the key player in the game of economic growth.” This year, in the January 20 issue of Time Magazine, the current FOMC chair, Janet Yellen said, “And part of the [economic stimulus] comes through higher house and stock prices, which causes people with homes and stocks to spend more, which causes jobs to be created throughout the economy and income to go up throughout the economy.”

FOMC leaders may feel justified in taking such a position based upon the FRB/US, a large-scale econometric model. In part of this model, employed by the FOMC in their decision making, household consumption behavior is expressed as a function of total wealth as well as other variables. The model predicts that an increase in wealth of one dollar will boost consumer spending by five to ten cents (see page 8-9 “Housing Wealth and Consumption” by Matteo Iacoviello, International Finance Discussion Papers, #1027, Board of Governors of the Federal Reserve System, August 2011). Even at the lower end of their model's range this wealth effect, if it were valid, would be a powerful factor in spurring economic growth.

After examining much of the latest scholarly research, and conducting in house research on the link between household wealth and spending, we found the wealth effect to be much weaker than the FOMC presumes. In fact, it is difficult to document any consistent impact with most of the research pointing to a spending increase of only one cent per one dollar rise in wealth at best. Some studies even indicate that the wealth effect is only an interesting theory and cannot be observed in practice.

The wealth effect has been both a justification for quantitative easing and a root cause of consistent overly optimistic growth expectations by the FOMC. The research cited below suggests that the concept of a wealth effect is in fact deeply flawed. It is unfortunate that the FOMC has relied on this flawed concept to experiment with over $3 trillion in asset purchases and continues to use it as the basis for what we believe are overly optimistic growth expectations.

Consumer Wealth and Consumer Spending

Many episodes of rising and falling financial and housing asset wealth have occurred throughout history. The question is whether these periods of wealth changes are associated in a consistent and reliable way with changes in consumer spending. We examined, separately, percent changes in real consumption expenditures per capita against percent changes in the real S&P 500 index (financial wealth) and against percent changes in Robert Shiller’s real home price index (housing wealth). If economic relationships are valid they should work for all time periods, regardless of highly different idiosyncratic conditions, as opposed to an isolated subset of historical experience. As such, we conducted our analysis from 1930 through 2013, the entire time period for which all variables were available.

Financial Wealth. Chart 1 is a scatter diagram of current percent changes in both real per capita personal consumption expenditures (PCE), the preferred measure of spending, and the real S&P 500 stock price index. It is made up of 84 dots, which constitutes a robust sample. Over our sample period, as with most extremely long periods, time will tend to link economic variables to each other; population is a key factor that can cause such an association. By expressing consumption in per capita terms, trending has been reduced, and in turn, an artificially overstated degree of correlation has been avoided.


If financial wealth drives consumer spending, an unambiguous positively sloped line should be evident on this scatter diagram. Larger gains in the S&P 500 would be associated with faster increases in spending; conversely, declines in the S&P 500 would be tied to lower spending. If there was a strong positive correlation, the large gains in stock prices would be associated with strong gains in spending, and they would fall in the upper right quadrant of the graph. In addition, sizeable declines in the S&P would be associated with large decreases in consumer spending, and the dots would fall in the lower left quadrant, resulting in an upward sloping line. For the relationship to be stable and dependable the dots should be packed in an around the trend line. This is clearly not the case. The trend line through the dots is positive, but the observations in the upper left quadrant of the graph and those in the lower right exhibit a negative rather than positive correlation. Furthermore, the dots are not clustered close to the trend line. The goodness of fit (coefficient of determination) of 0.27 is statistically significant; however, the slope of the line is minimally positive. This suggests that an approximate one dollar increase in wealth will boost real per capita PCE by less than one cent, far less than even the lower band of the effect in the Fed’s model.

Theoretically, lagged changes are preferred because when current or coincidental changes in economic variables are correlated the coefficients may be biased due to some other factor not covered by the empirical estimation. Also, lags give households time to adjust to their change in wealth. As such, we correlated the current percent change in real per capita PCE against current changes as well as one- and two-year lagged changes (expressed as a three-year moving average) in the S&P 500. The lags did not improve the goodness of fit as the coefficient of determination fell to 0.21. An increased dollar of wealth, however, still resulted in a one cent increase in consumption. We then correlated current percent change in real per capita PCE with only lagged changes in the real S&P 500 for the two prior years (expressed as a two-year moving average), and the relationship completely fell apart as the goodness of fit fell to a statistically insignificant 0.06.

Housing Wealth. Chart 2 is a second scatter diagram, relating current percent changes in real home prices to current percent changes in real per capita PCE. Once again, the trend line does have a small positive slope, but there are so many observations in the upper left quadrant that the coefficient of determination does not meet robust tests for statistical significance. The dots are even more dispersed from the trend line than in the prior scatter diagram.

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