Macro Roundup

*The KGR is their stationary component of inequality: “…“KGR” (capital gains ratio), which we define to be the difference between the top 1% income share with and without realized capital gains income, divided by the bottom 99% income share..”

“…But, does more inequality actually cause lower returns, and is KGR* actually reflecting inequality? To address causality, we use tax rate changes as an instrument. Since contemporaneous and lagged changes in top estate tax rates explain a substantial portion of the variation in KGR (Table 6), we estimate the effect of inequality on returns using generalized method of moments (GMM) with instrumental variables (Table 7). Including KGR, industrial production growth, and the log price-earnings ratio as endogenous explanatory variables and using lags of top estate tax rate changes and the log price-earnings ratio as instruments, top income shares are still significant in predicting excess returns. This finding addresses another concern, which is that part of the variation in KGR is not due to inequality but rather from the timing of realizing capital gains or other omitted variables. Including one-year-ahead changes in capital gains tax rates as an additional instrument, we separately identify how the timing and inequality components predict returns (Table 9). The coefficient on the inequality component is negative and significant, while the timing coefficient is insignificant. Further suggestive evidence that KGR reflects inequality comes from the portfolios of the rich. Using the wealth composition estimates from Saez and Zucman (2016), we show that rising KGR is associated with subsequent increases in the share of equities owned by the rich (Table 10). The share of bonds, however, does not significantly change, which is consistent with our theory that rising inequality increases relative demand for risky assets…. Our findings are also robust to the inclusion of macro control variables, such as GDP growth. Using five year excess returns or the top 0.1% or 10% income share also yields similar results, although the predictability is really due to the top 1%(Table 4)….”

Had the same though, though I don’t think they do a super convincing job of addressing the covariance issue with business cycle, for example they point to increased demand for equities over debt (as the rich can take more risk)

In this paper we built a general equilibrium model with agents that are heterogeneous in wealth, risk aversion, and belief. We showed that the concentration of wealth/income drives down the subsequent equity premium. Our model is a mathematical formulation of Irving Fisher’s narrative that booms and busts are caused by changes in the relative wealth of the rich (the “enterpriser-borrower”) and the poor (the “creditor, the salaried man, or the laborer”). Consistent with our theory, we found that in the U.S. the income/wealth distribution is closely connected with stock market returns. When the rich are richer than usual the stock market subsequently performs poorly, both in- and out-of-sample. The inverse relationship between returns and inequality is robust to controlling for standard return predictors and instrumenting with changes in estate taxes. It also holds outside of the U.S., although in relatively open economies with low home bias it is U.S. inequality that matters (as opposed to domestic inequality)…Consistent with our theory, we find that when the income share of the top 1% income earners in the U.S. rises, the subsequent one year excess stock market return falls on average. That is, current inequality appears to forecast the subsequent risk premium of the U.S. stock market… Could one exploit the predictive power of top income shares to beat the market on average? The answer is probably no since the top income share—which comes from tax return data—is calculated with a substantial lag. One would receive the inequality update too late to act on its asset pricing information. However, our analysis provides a novel positive explanation of excess market returns over time. We conclude, as decades of macro/finance theory have suggested, that stock market fluctuations are intimately tied to the distribution of wealth, income, and assets….”

Relationship btw income inequality and the ERP, paper argues that a rise in inequality drives down the ERP (rich can take more risk):

On page 28 they mention 3 studies that the rich get higher returns and later on page 30 that the rich are better at timing the market (I don’t believe it). Plz read the 3 studies.

It’s also interesting that (page 29)“ equity concentration is also inversely related with the current level of the stock market and rising capital gains taxes.” so rates drive the rich to take more risk!

Here is the first of the three paper you wanted to look at that support the idea that the rich get higher returns. Their SCF data they use in support their argument is bellow. Note when documenting the decline of retail investors they use 2000 as a baseline, that was of course the tech bubble, which likely skewed stock ownership more broadly

“…In the quantitative tests of the model in Figure 2, we show that sophisticated investors allocate their wealth first into assets with highest level of volatility and subsequently into assets with lower levels of volatility. Now, we provide additional evidence which suggests similar investors’ preferences. Our first piece of evidence is based on SCF data regarding households’ holdings in liquid wealth. The idea of this test is that unsophisticated investors should be more likely to invest in safe (liquid) assets. SCF provides detailed classification of wealth invested in such assets that include checking accounts, call accounts, money market accounts, coverdell accounts, and 529 educational state-sponsored plans. As before, in each period, we divide households into two groups: top 10% and bottom 50% of the wealth distribution. For each of the groups, we calculate the average ratio of liquid wealth to total financial wealth. Higher ratios would imply greater exposure to low-profit assets. We present the two time series in Figure 5. We find evidence that strongly supports predictions of our model. First, the average ratio of liquid wealth for sophisticated investors, equal to 15.3%, is significantly lower than that for unsophisticated investors, which in our sample equals 25%. In addition, while the exposure to liquid assets by sophisticated investors is generally non-monotonic (u-shaped), similar investment for unsophisticated investors exhibits a strong positive time trend, especially in the last 20 years: The average investment goes up from 16.7% in 1998 to 39% in 2010.This evidence strongly supports our economic mechanism in that differences in information capacity lead to retrenchment by unsophisticated investors from risky assets and relocation to safer assets. We further confirm this claim using evidence on institutional holdings from Thomson Reuters. To this end, we calculate average (equal-weighted) equity ownership of sophisticated investors (mutual funds and hedge funds) and unsophisticated (retail) investors. We report the respective time series quarterly averages of the ownership over the period 1989-2012 in Figure 6. The results paint a picture that is generally consistent with our model’s predictions. Although the average ownership level of unsophisticated investors is higher in an unconditional sample and equals 61%, the time-series evidence clearly indicates a very strong pattern: The average equity ownership for unsophisticated investors goes down while that for sophisticated investors significantly goes up…”

Marcin Kacperezyk, Jaromier Nosal and Luminita Stevens, "Investor Sophistication and Capital Income Inequality,"Journal Of Monetary Economics, September 2014, https://www.cmu.edu/tepper/faculty-and-research/assets/docs/Nosal_KNS_paper_sep2014.pdf

“…An important component of total income, capital income is by far the most polarized part of household income in the United States, and it exhibits a strong upward trend in polarization…we provide a micro-founded mechanism for the return differential and show that, when embedded in a general equilibrium framework, it can go a long way in explaining the growth in capital income inequality, qualitatively and quantitatively. The main friction in the model is heterogeneity in investor sophistication. Intuitively, when information about financial assets and its processing are costly, individuals with different access to financial resources differ in terms of their capacity to acquire and process information. Sophisticated investors have access to better information, which allows them to earn higher income on the assets they hold. As a result, their wealth diverges from that of the unsophisticated investors with relatively less information. In addition, unsophisticated investors perceive their information disadvantage through asset prices and allocate their investments away from the allocations of informed investors, resulting in further divergence….This basic intuition resonates well with robust empirical evidence that documents the growing presence of sophisticated, institutional investors in risky asset classes, over the last 20-30 years….. Specifically, the average institutional equity ownership has more than doubled over the last few decades, and it accounts now for more than 60% of the total stock ownership. Our hypothesis also fits well with a puzzling phenomenon of the last two decades of a growing retrenchment of retail investors from trading and stock market ownership in general…. even though direct transaction costs, if anything, have fallen significantly. We document such avoidance of risky assets both for direct stock ownership and ownership of intermediated products, such as actively managed equity mutual funds. Specifically, we find that direct stock ownership has been falling steadily over the last 30 years, while flows into equity mutual funds coming from less sophisticated, retail investors began to decline and turn negative starting from the early 2000s, implying a drop in cumulative flows by 2012 by an astounding 70% of their 2000 levels….First, we show that sophisticated ownership increases with aggregate sophistication, which can be interpreted as general progress in information processing technologies. This result holds even if we keep the relative sophistication of the two investor types constant. Intuitively, the more an investor knows, the easier it is for her to learn on the margin. This effect reinforces the general equilibrium effect that the same growth in sophisticated investors’ capacity raises prices more than that of unsophisticated investors’ and leads to unsophisticated investors being priced out of the risky asset market. Second, we show that sophisticated investors are more likely to invest in and learn about more volatile assets within a set of risky assets. Thus, the mechanism implies a robust, unique way in which investors expand their risky portfolio holdings as the total capacity in the economy expands - they keep moving down in the asset volatility dimension. At the same time, unsophisticated investors abandon risky assets and hold safer assets. Third, similar effects occur in terms of trading intensity. Sophisticated investors frequently trade their assets while unsophisticated investors turn over their risky assets much less. Finally, we show that the symmetric expansion in capacity leads to lower expected market returns. These results play an important role in that they cut against plausible alternative explanations, such as the model with heterogeneous risk aversion or differences in trading costs…..What contributes to the growing income inequality across households? This question has been of great economic and policy relevance for at least several decades starting with a seminal work by Kuznets. We approach this question from the perspective of capital income that is known to be highly unequally distributed across individuals. We propose a theoretical information-based framework that links capital income derived from financial assets to a level of investor sophistication. Our model implies the presence of income inequality between sophisticated and unsophisticated investors that is growing in the extent of total sophistication in the market and in relative sophistication across investors. Additional predictions on asset ownership, market returns, and turnover help us pin down the economic mechanism and rule out alternative explanations. The quantitative predictions of the model match qualitatively and quantitatively the observed data.Although our empirical findings are strictly based on the U.S. market, our model should have similar implications for other financial markets. For example, qualitatively, we know that income inequality in emerging markets tends to be even larger than the one documented for the U.S. To the extent that financial sophistication in such markets is much more skewed one could rationalize within our framework the differences in capital incomes. Similarly, the U.S. market is considered to be the most advanced in terms of its total sophistication, which is possibly why we find a greater dispersion in capital income compared to other developed markets, such as those in Europe or Asia. More generally, one could argue that although the overall growth of investment resources and competition across investors with different skill levels are generally considered as a positive aspect of a well-functioning financial market, our work suggests that one should assess any policy targeting overall information environment in financial markets as potentially exerting an offsetting and negative effect on socially relevant issues, such as distribution of income. We leave detailed evaluation of such policies for future research….”

“…We provide a systematic analysis of the properties of individual returns to wealth using twenty years of population data from Norway’s administrative tax records. We document a number of novel results. First, in a given cross-section, individuals earn markedly different returns on their assets, with a difference of 500 basis points between the 10th and the 90th percentile. Second, heterogeneity in returns does not arise merely from differences in the allocation of wealth between safe and risky assets:returns are heterogeneous even within asset classes.Third, returns are positively correlated with wealth. Fourth, returns have an individual permanent component that accounts for 60% of the explained variation. Fifth, for wealth below the 95th percentile, the individual permanent component accounts for the bulk of the correlation between returns and wealth; the correlation at the top reflects both compensation for risk and the correlation of wealth with the individual permanent component. Finally, the permanent component of the return to wealth is also (mildly) correlated across generations.…returns to wealth exhibit substantial heterogeneity. For example, in the last year of our sample (2013)‚ the (value-weighted) average return on overall wealth is 3.7%, but it varies considerably across households (standard deviation 6.1%). Furthermore, heterogeneity in returns is not simply a reflection of differences in portfolio allocations between risky and safe assets, and thereby compensation for risk-taking that mirrors heterogeneity in risk tolerance. Even conditioning on the share of risky assets in a portfolio, heterogeneity in returns is large and increases with the level of wealth. This result is confirmed even when looking at individuals with no private business wealth. Another remarkable finding is that asset returns increase with wealth. In 2013, the difference between the median return for people in the 90th and 10th percentiles of the wealth distribution is 180 basis points. The correlation between returns and wealth does not merely reflect risk-taking. We find that risk-adjusted measures of excess returns (the Sharpe ratio) increase with the level of wealth at the point of entry in the sample, before any investment decisions are taken…. We find that for wealth below the 95th percentile the correlation between average returns and wealth rank is largely due to a positive correlation between wealth and the fixed effects (individuals with permanently higher returns are wealthier). For wealth above the 95th percentile, it is largely driven by compensation for higher risk exposure among the wealthy, with a more limited contribution (one third) from the fixed effects….”

Andreas Fagereng, Luigi Guiso, Davide Malacrino and Luigi Pistaferri, “Heterogeneity In Returns To Wealth And The Measurement Of Wealth Inequality,” American Economic Review, May 2016, https://pubs.aeaweb.org/doi/pdf/10.1257/aer.p20161022

“…Individual returns average around 3 percent (2.43 percent at the median) and show substantial heterogeneity over the sample period. The standard deviation of individual returns ranges between 2.5 percent in 2009 and 6.1 percent in 2005. Similarly, the range (not shown) between the ninetieth and the tenth percentile varies, depending on the year, between 3.2 and 7.5 percentage points. Interestingly, the extent of heterogeneity first increases and then trends downward.5 Figure 2 shows that median returns increase with the household’s position in the wealth distribution. For example, households in the ninetieth percentile have median returns that are often twice as large as those of households in the tenth percentile, and the correlation with wealth also varies over time…”

Andreas Fagereng, Luigi Guiso, Davide Malacrino and Luigi Pistaferri, “Heterogeneity and Persistence In Returns To Wealth,” National Bureau Of Economic Research, November 2016, https://www.nber.org/papers/w22822.pdf

Shleifer (this is very much in the spirit of his last book) supports Kindleberger-Minsky view that that financial panics are a predictable function of the boom-bust credit cycle:

“….Using historical data on post-war financial crises around the world, we show that crises are substantially predictable. The combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is associated with about a 40% probability of entering a financial crisis within the next three years. This compares with a roughly 7% probability in normal times, when neither credit nor asset price growth has been elevated. Our evidence cuts against the view that financial crises are unpredictable “bolts from the sky” and points toward the Kindleberger-Minsky view that crises are the byproduct of predictable, boom-bust credit cycles. The predictability we document favors macro-financial policies that “lean against the wind” of credit market booms…. . First…crises can be predicted using past credit growth in simple linear forecasting regressions. We show that both nonfinancial business and household credit growth forecast the onset of a future crisis. However, the degree of predictability is modest, even at horizons of up to five years. Schularick and Taylor (2012) find that a one standard deviation rise in real 1-year credit growth leads to a 2.8 percentage point increase in the probability of a crisis over the next five years. Repeating their analysis on our sample with BVX’s crisis chronology, we obtain virtually the same result….Second, we show that the degree of predictability rises substantially when we focus on large credit expansions that are accompanied by asset price booms. Specifically, when nonfinancial business credit growth is high and stock market valuations have risen sharply, or when household credit growth is high and home prices have risen sharply, the probability of a subsequent crisis is substantially elevated. The combination of rapid credit growth and asset price growth in the same sector is a natural signal of an outward shift in the supply of credit, which then sows the seeds of its own destruction… Our results are robust to different classifications of financial crises such as those in Reinhart and Rogoff (2011) and Jordà, Schularick, and Taylor (2017). However, we do not use data on credit spreads in this paper, which would likely increase the predictability of crises, because the historical scarcity of these data would substantially reduce our sample. To demonstrate these results, we construct a simple indicator variable called the Redzone, or the “R-zone” for short, that identifies periods of potential credit-market overheating. Specifically, we say that a country is in the “business R-zone” if nonfinancial business credit growth over the past three years is in the top quintile of the historical distribution, and stock market returns over the same window are in the top tercile. The probability of a crisis at a 1- year horizon is 13% if a country is in the business R-Zone, a substantial increase over the unconditional probability of 4%. The comparable 1-year probability is 14% if a country is in the household R-zone—i.e., if household credit growth and home price growth are jointly elevated. Crucially, the degree of predictability increases dramatically with horizon. The probability of experiencing a financial crisis within the next three years is 45% for countries 3 that are in the business R-zone, and 37% for countries in the household R-zone. Put differently, even after entering the R-zone, crises are slow to develop, suggesting that policymakers have time to act based on early warning signs….Third, we show that overheating in the business and household credit markets are separate phenomena. Both independently predict the arrival of future crises, but they are particularly dangerous in the rare instances—e.g., Japan in 1988—when they occur in tandem. 63% of the crises in our sample were preceded by either a household or business R-zone event within the prior three years. Perhaps not surprisingly, the United States was in the household R-zone from 2002-2006 and a financial crisis arrived in 2007….Fourth, overheating in credit markets naturally has a global component and is correlated across countries. We construct global business R-zone and global household R-zone variables which measure the fraction of countries in our sample that are in the R-zone in each year. We find that including these global variables in our forecasting regressions substantially increases the predictability of crises. For example, while Germany was nowhere near the R-zone in 2007, 33% of countries were in the business R-zone and 36% were in the household R-zone at the time. As a result, the predicted probability of experiencing a crisis within 3 years was 37% for Germany in 2007 and, indeed, Germany experienced a crisis in 2008. Furthermore, when we account for these global variables, we estimate that the probability of a subsequent crisis in the U.S. rose from 31% in 2002 when U.S. first entered the household R-zone to 51% in 2006…..Fifth, we show that R-zone events predict future contractions in real gross domestic product (GDP)…show that periods of credit market overheating predict lower real GDP growth at a horizon of two years. Mian, Sufi, and Verner (2019) demonstrate that rapid credit growth—especially household credit growth—forecasts low real GDP growth over the medium run. Adrian, Grinberg, Liang, and Malik (2018) find that financial stability measures—which include credit growth—predict higher downside risks to GDP growth. We show that the business and household R-zones also reliably predict GDP contractions, which we define as a 2% decline in real GDP in a year.This result is only partially driven by the well-known fact that financial crises themselves are associated with GDP contractions (Reinhart and Rogoff 2009a)….”

Robin Greenwood, Samuel Hanson, Andrei Shleifer and Jakob Ahm Sorensen, “Predictable Financial Crises,” National Bureau Of Economic Research, June 2020, https://www.nber.org/papers/w27396

Some good charts from a new MS research note on the rise of evolution of equity markets plus this really good factoid, “…Virtually none of the $1.3 trillion in value that Amazon built was in the private market. Three percent of the value created by Alphabet, which controls Google, was in the private market, and that percentage was about 17 percent for Facebook. And the implied value of Uber in the private market was more than 100 percent of the total value created, as the company’s market capitalization is below what its IPO price implied….”

Michael Mauboussin and Dan Callahan, “Public To Private Equity In The United States: A Long-Term Look,” Morgan Stanley, August 4, 2020, https://www.morganstanley.com/im/publication/insights/articles/articles_publictoprivateequityintheusalongtermlook_us.pdf

MS:“….the mix of tangible and intangible investments has changed over the last 40 years. In the late 1970s tangible investments were nearly double those of intangible investments. Today, intangible investments are one-and-a-half times larger than tangible investments. A watershed change in the form of investment has occurred over a couple of generations.This shift has a few implications for our discussion. To begin, companies need less capital because they need fewer physical assets. For example, sales per employee for Facebook, Inc. were nearly double those of Ford Motor Company in 2019. From 1956 to 1976 the number of public companies grew fivefold, as many companies needed to finance “their mass production and mass distribution.” Today, companies simply do not require as much capital as they once did. This, along with freer access to private capital, allows private companies to remain private longer.Anotherimplication is that the rate of change, which we can measure by longevity, appears to be speeding up. The idea is that if longevity is decreasing, the rate of change is increasing. About 1,500 companies went public during the 1970s, 3,000 in the 1980s, 3,900 in the 1990s, and 2,100 in the 2000s. Companies that had listed before 1970 had a 92 percent probability of surviving the next five years, and those listed in the 2000s had a probability of only 63 percent. The chance of survival has dropped in each successive decade. The main reason companies delist is that they are acquired. This contributes to the last implication.In corporate America, the strong are getting stronger. This is giving rise to “superstar” firms. For example, the gap in return on invested capital between a U.S. company in the top 10 percent and the median has risen sharply in recent decades. Consolidation explains a large part of this. Measures of concentration, such as the Herfindahl-Hirschman Index, have shown a substantial increase for many industries since the mid-1990s. These include industries that rely on tangible assets.When the proper conditions are in place, certain businesses exhibit increasing returns, which include very high market shares and economic profits. Increasing returns are pronounced in intangible-based businesses, and there has been a growing gap between the intangible spending of the large firms relative to small ones.The shift from tangible to intangible assets has had a meaningful effect on the mix between public and private companies. That many young companies have less capital intensity means they don’t need to go public to raise capital. The mix of the companies that are public has shifted to more reliance on intangible investment, which in turn has led to a reduction in longevity. And the economics of information goods, combined with the concentration of traditional industries and the outsourcing of low-value-added activities, means that a handful of leading companies earn much higher economic rents than their competitors and businesses of the past….”