Wealth Inequality And Return Heterogeneity During The Covid 19 Pandemic

We provide a systematic analysis of the properties of individual returns to wealth using twelve years of population data from Norway’s administrative tax records. We document a number of novel results. First, during our sample period individuals earn markedly different average returns on their financial assets (a standard deviation of 14%) and on their net worth (a standard deviation of 8%). 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: moving from the 10th to the 90th percentile of the financial wealth distribution increases the return by 3 percentage points - and by 17 percentage points when the same exercise is performed for the return to net worth. Fourth, wealth returns exhibit substantial persistence over time. We argue that while this persistence partly reflects stable differences in risk exposure and assets scale, it also reflects persistent heterogeneity in sophistication and financial information, as well as entrepreneurial talent. Finally, wealth returns are (mildly) correlated across generations. We discuss the implications of these findings for several strands of the wealth inequality debate.

In this paper I study how firm heterogeneity affects wealth distribution through entrepreneurial income and capital gains. As shown in Hottman, Redding and Weinstein 2016, size distribution of firms is highly skewed. Top 1% of firms on average have market shares of 50%. I develop a dynamic heterogeneous-agent general equilibrium model, where firms produce multiple products and households invest by bargaining with firm owners. Every product in this model has two dimensions: its rate of return and its optimal output. Every firm thus has two dimensions: its rate of return and size. Investors sort into different firms based on their wealth, which increases due to either their share of firm's profit or their capital gains. The distribution of product space is calibrated with Nielsen barcode dataset. The simulation shows that the firm heterogeneity in its two dimensions implied by the dataset results in a severe wealth inequality. The model can explain 90% of the rise in top wealth concentration between 2003 and 2012 in the United States and shows that the upswing in the top 1% wealth share is due to the rise in the top 0.1%, which is consistent with Saez and Zucman 2016. Counterfactual exercises highlight entrepreneurship as the main driver in wealth equality and show that fluctuations in risk free rate of return and access to new investment opportunities reduce wealth inequality, of which each may offset up to half of the effect of entrepreneurship.

We document in US data that returns to wealth across households are significantly heterogeneous, and persistently so. Motivated by this observation, we build a tractable general equilibrium model where households face persistent idiosyncratic returns to study the US wealth distribution. We show theoretically that the wealth distribution in the model admits a Pareto tail and characterize how the tail index depends on salient equilibrium variables including capital-output ratio, labor share, interest rate, and growth rate. Quantitatively, to match the observed US wealth distribution it requires significant heterogeneity in returns, consistent with our empirical findings. Finally, we show in the model that financial deregulation and a reduction in US corporate tax rates can generate the joint evolution of rising wealth inequality, rising capital-output ratio and declining labor share since the 1980s.

Studies show that historically wealth distribution has again become very concentrated in the US since the end of the 1970s (Wolff 1996, 2010). Both the heavy concentration and the rapid increase in top wealth share in the US are well documented by Wolff (2006). In the US, the wealthiest 5 percent of American households held 54 percent of all wealth reported in the 1989 Survey of Consumer Finance; this share has reached 63 percent as of 2013 (Yellen, 2014).In the second chapter I study how firm heterogeneity affects wealth distribution through entrepreneurial income and capital gains. As shown in Hottman, Redding and Weinstein 2016, size distribution of firms is highly skewed. Top 1% of firms in a product group on average have market shares of 50%. I develop a dynamic general equilibrium model with heterogeneous households and multiproduct firms. Each product has its own rate of return and scale, which result in heterogeneity in the rate of return and size of firms. Firms with larger scope of products benefit more from stricter control in product quality and thus grow faster than the others do. Households need to pay a premium to firm owners to invest in their firm, which can be interpreted as capital gain. Wealth of households, especially entrepreneurs, accumulates partly because of entrepreneurial income, which is their claim on their firm's profits, and partly because they receive capital gain. Firms in the model have two dimensions: its rate of return and the firm size. The premium increases in both, which allows households to sort into different firms based on their wealth.The distribution of product space is calibrated with Nielsen barcode dataset. The simulation shows that the firm heterogeneity in its two dimensions results in a severe wealth inequality. The model can explain 90% of the rise in top wealth concentration between 2003 and 2012 in the United States. The result is consistent with findings in Saez and Zucman 2016 that the upswing in the top 1% wealth share is due to the rise in the top 0.1% and that the increase in wealth inequality is not due to rate of return differential on corporate stocks. Counterfactual exercises highlight entrepreneurship as the main driver in wealth equality and show that fluctuations in risk free rate of return and access to new investment opportunities reduce wealth inequality, of which each may offset up to half of the effect of entrepreneurship.The third chapter studies how households' cumulative health shocks affect their health investment, health insurance premium, and wealth. I develop a rich lifecycle model of endogenous health capital, insurance premium and wealth, with a diffusion income process and Poisson health shocks. In this model, health shocks affect a household's wealth through three channels. It increases medical spending, lowers productivity and thus income, and increases the probability of death. Theoretical results show that since health increases life expectancy, it becomes a luxury good relative to consumption. When income rises, health generates larger marginal utility relative to consumption. As a result, rich people invest more in health, live longer, and save more. This implies that vital health shocks may facilitate top concentration in wealth distribution. With the CDC data for the top nine death-causing diseases I estimate vital disease processes and calibrate the model. The counterfactual exercise shows that effects of vital health shocks indeed help explain wealth inequality.The fourth chapter is a complement to the second chapter Firm Heterogeneity and Wealth Distribution where households are risk-neutral due to linear utility. In this chapter I study effects of difference in households' attitudes to risk on wealth distribution by introducing heterogeneity on the risk aversion parameter of the utility function. A two-period model reveals that risk aversion heterogeneity exacerbates wealth inequality. Households with relatively low level of risk aversion or high level of wealth are more exposed to risk. The more risk-averse, the less risky the optimal project and, under some condition, the less the agent may invest. The wealthier, the larger the investment. As risks in investment opportunities are realized, the variance of income among relatively wealthy households are larger than its counterpart among poor households, which leads to a heavier top concentration in wealth distribution. Lucky wealthy risk lovers tend to move to the top while unlucky investors go to the bottom of the wealth distribution.

Why the United States lags behind other industrialized countries in sharing the benefits of innovation with workers and how we can remedy the problem. The United States has too many low-quality, low-wage jobs. Every country has its share, but those in the United States are especially poorly paid and often without benefits. Meanwhile, overall productivity increases steadily and new technology has transformed large parts of the economy, enhancing the skills and paychecks of higher paid knowledge workers. What’s wrong with this picture? Why have so many workers benefited so little from decades of growth? The Work of the Future shows that technology is neither the problem nor the solution. We can build better jobs if we create institutions that leverage technological innovation and also support workers though long cycles of technological transformation. Building on findings from the multiyear MIT Task Force on the Work of the Future, the book argues that we must foster institutional innovations that complement technological change. Skills programs that emphasize work-based and hybrid learning (in person and online), for example, empower workers to become and remain productive in a continuously evolving workplace. Industries fueled by new technology that augments workers can supply good jobs, and federal investment in R&D can help make these industries worker-friendly. We must act to ensure that the labor market of the future offers benefits, opportunity, and a measure of economic security to all.

This open access edited volume is a comparative effort to discern the short-term educational impact of the covid-19 pandemic on students, teachers and systems in Brazil, Chile, Finland, Japan, Mexico, Norway, Portugal, Russia, Singapore, Spain, South Africa, the United Kingdom and the United States. One of the first academic comparative studies of the educational impact of the pandemic, the book explains how the interruption of in person instruction and the variable efficacy of alternative forms of education caused learning loss and disengagement with learning, especially for disadvantaged students. Other direct and indirect impacts of the pandemic diminished the ability of families to support children and youth in their education. For students, as well as for teachers and school staff, these included the economic shocks experienced by families, in some cases leading to food insecurity and in many more causing stress and anxiety and impacting mental health. Opportunity to learn was also diminished by the shocks and trauma experienced by those with a close relative infected by the virus, and by the constrains on learning resulting from students having to learn at home, where the demands of schoolwork had to be negotiated with other family necessities, often sharing limited space. Furthermore, the prolonged stress caused by the uncertainty over the resolution of the pandemic and resulting from the knowledge that anyone could be infected and potentially lose their lives, created a traumatic context for many that undermined the necessary focus and dedication to schoolwork. These individual effects were reinforced by community effects, particularly for students and teachers living in communities where the multifaceted negative impacts resulting from the pandemic were pervasive. This is an open access book.

This erudite book offers an extensive overview of the most important debates taking place amongst Post Keynesian economists, acknowledging the vital contribution Post Keynesians have made to theoretical and policy discourse in the 21st century. Bringing together distinguished experts from across the globe, Post Keynesian Economics: Key Debates and Contending Perspectives discusses the profound questions of heterodox economic theory and their far-reaching implications for economic policy.

The global health and economic threats from the COVID-19 pandemic are not yet behind us. While the development of multiple safe and highly effective vaccines in less than a year is cause for hope, several significant dangers to recovery of global health and income are still clear and present: New concerning variants of SARS-CoV-2, the virus that causes COVID-19, continue to emerge at an alarming rate in different parts of the world; at the same time, vaccine rollouts have been shockingly inefficient even in some rich countries, while much of the developing world waits in line behind them for vaccines to arrive. The Briefing covers several policy areas in which cooperative forward-looking policy action will materially improve our chances of truly escaping today's pandemic and making future pandemics less costly.