Macro Roundup

Antonio Afonso and Joao Tovar Jalles, “Economic Performance And Government Size,” European Central Bank, November 2011, https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1399.pdf

“..Our results show a significant negative effect of the size of government on growth. Similarly, institutional quality has a significant positive impact on the level of real GDP per capita. Interestingly, government consumption is consistently detrimental to output growth irrespective of the country sample considered (OECD, emerging and developing countries). Moreover, i) the negative effect of government size on GDP per capita is stronger at lower levels of institutional quality, and ii) the positive effect of institutional quality on GDP per capita is stronger at smaller levels of government size….”

Note they find the quality of institutions matters,

“…We construct a growth model with an explicit government role, where more government resources reduce the optimal level of private consumption and of output per worker. In the empirical analysis, for a panel of 108 countries from 1970-2008, we use different proxies for government size and institutional quality. Our results, consistent with the presented growth model, show a negative effect of the size of government on growth. Similarly, institutional quality has a positive impact on real growth, and government consumption is consistently detrimental to growth. Moreover, the negative effect of government size on growth is stronger the lower institutional quality, and the positive effect of institutional quality on growth increases with smaller governments. The negative effect on growth of the government size variables is more mitigated for Scandinavian legal origins, and stronger at lower levels of civil liberties and political rights. Finally, for the EU, better overall fiscal and expenditure rules improve growth…”

Ed, the ECB paper you asked about, looking at the impact of the size of government on growth btw 1970-2008.

Page 24 of 44, “….In Table 7 we allow for both heterogeneous technology parameters and factor loadings as explained above, by running the CCEP, CCEMG and AMG estimators with and without interaction terms (where the institutional proxy variable is now given by the EMA_PCA variable as explained in Section 3). When running the AMG estimator for the OECD group we find some evidence of a statistically significant negative coefficient on the government size variable…”

page 24 of 44, “….In Table 6.b an equivalent set of results is presented but now with the integration term between government size and an institutional proxy of interest. In the case of the OECD the interaction term is negative and statistically significant for the polity indicator instance. However, the government size is not significant…..”

Page 21 of 44, “…..Particularly under the total government expenditure and government spending specifications (4,5, 7, 8) we find statistically significant positive coefficients on the overall rule index and the expenditure rule index, meaning that having these fiscal numerical rules improves GDP growth for these set of EU countries. However, the government size proxy is never significant when these rules are included as additional regressors. When these rules are interacted with a relevant government size proxy, Table 5b, no coefficient is statistically significant….”

On page 20 of 44, “…ii) for the OECD sub-group debt has a statistically significant negative coefficient estimate and its interaction with the Freedom House index results in a negative estimate significant at 5 percent level…”

“…Well, I emphasize two sorts of policies as most important. I emphasize that we need lighter regulation. We have had a growth of something called the precautionary principle, where increasingly you have to prove that your innovation will not possibly cause any harm. But a large number of the great innovations of the past were ones that you didn’t know what harm there might be until you worked out the kinks and you tried it out. And so there’s a nice passage from a book by Cass Sunstein where he lists a whole bunch of important innovations that would never have existed if the precautionary principle had been in place at the time. And so it makes you wonder then, now that we’re increasingly having the precautionary principle, what innovations would exist that we’re not allowing ourselves to have because we’re enforcing this precautionary principle? So I emphasize less regulations, lighter regulations, as one of the key policy directions if we want more innovation, if we want to have faster growth, and if we don’t want to have stagnation. Another policy emphasis is we need to keep taxes lower. Part of what I argue is that the bigger the breakthrough innovation is, the more likely it is that people besides the entrepreneur are not going to be able to judge whether it’s plausible or not. Only the innovator behind that innovation will know. That means at the early, fragile, precarious stages of a breakthrough innovation, it’s going to almost inevitably have to be self-funded. For it to be self-funded, you have to allow the innovator to accumulate and have the funds to self-fund. And what that implies is you have to have a light tax policy to allow them to do that. So I also emphasized low taxation as a way to encourage the self-funding at these early precarious stages. There are other factors that matter too. Peter Thiel says entrepreneurs need to have more courage. So he’s emphasizing things that can be done. He thinks people are being too cautious in the projects they undertake….”

James Pethokoukis, "The case for ‘innovative dynamism’: My long-read Q&A with Arthur Diamond," American Enterprise Institute, July 30, 2020, https://www.aei.org/economics/making-the-case-for-innovative-dynamism-my-long-read-qa-with-arthur-diamond/

Economist Staff, "Money really can buy happiness and recessions can take it away,"The Economist, July 11, 2020, https://www.economist.com/graphic-detail/2020/07/11/money-really-can-buy-happiness-and-recessions-can-take-it-away

Economist Staff, "Banks lose out to capital markets when it comes to credit provision,"The Economist, July 25, 2020, https://www.economist.com/finance-and-economics/2020/07/25/banks-lose-out-to-capital-markets-when-it-comes-to-credit-provision

*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