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We study the coevolution of the fall in the US corporate-sector labor share and the rise of business activity in tax-preferred pass-throughs. We find that reallocating activity to the form it would have taken prior to the Tax Reform Act of 1986 accounts for one-third of the decline in the corporate-sector labor share between 1978 and 2017. Our adjustments are concentrated among mid-market firms in services, magnifying the role of the manufacturing sector and superstar firms in driving the remaining decline in the labor share. Our findings highlight the importance of tax policy when measuring factor shares. (JEL D22, E25, H25, K34, L60, L80)

Using a novel empirical approach and newly available administrative data on U.S. tax filings, we estimate the corporate elasticity of taxable income, decompose the elasticity into economic responses versus other tax-motivated "accounting" transactions, and determine how responsiveness varies depending on accounting method, firm size, and interest rate. In response to a 10% increase in the expected marginal tax rate, private U.S. firms decrease taxable income by 9.1%, which indicates a discernibly more elastic response than prevailing estimates. This response reflects a decrease in taxable income of 3.0% arising from real economic responses to a firm's scale of operations and 6.1% arising from accounting transactions via (for example) revenue and expense timing. Responsiveness to the corporate tax rate is more elastic if a firm uses cash (9.9%) rather than accrual accounting (7.4%), if the firm is small (9.9%) rather than large (8.6%), and if the firm discounts future cash flows at a lower rate.

This article uses administrative tax data to estimate top wealth in the United States. We assemble new data that link people to their sources of capital income and develop new methods to estimate the degree of return heterogeneity within asset classes. Disaggregated fixed-income data reveal that rich individuals earn much more of their interest income in higher-yielding forms and have much greater exposure to credit risk. Consequently, in recent years, the interest rate on fixed income at the top is approximately 3.5 times higher than the average. We value the population of U.S. firms using firm-level characteristics and apportion this wealth using firm-owner links. We combine this new data on fixed income and pass-through business returns with refined estimates of C-corporation equity, housing, and pension wealth to deliver new capitalized wealth estimates that build upon the methods of . From 1989 to 2016, the top 1%, 0.1%, and 0.01% wealth shares increased by 6.6, 4.6, and 2.9 percentage points, respectively, to 33.7%, 15.7%, and 7.1%. Overall, although we estimate a large degree of return heterogeneity, accounting for this heterogeneity does not change the fundamental story for top wealth shares and their growth-wealth inequality is high and has risen substantially over recent decades.

This paper uses administrative tax data to estimate top wealth in the United States. We assemble new data that links people to their sources of capital income and develop new methods to estimate the degree of return heterogeneity within asset classes. Disaggregated fixed income data reveal that rich individuals earn much more of their interest income in higher-yielding forms, and have much greater exposure to credit risk. Consequently, in recent years, the interest rate on fixed income at the top is approximately three times higher than the average. Using firm-level characteristics to value firms, we find that twenty percent of total pass-through business wealth accrues to those with losses. We combine this new data on fixed income and pass-through business returns with refined estimates of C-corporation equity, housing, and pension wealth to deliver new capitalized wealth estimates. Our approach---which builds on Saez and Zucman (2016) and Bricker, Henriques, and Hansen (2018)---reduces bias because wealth and rates of return are correlated. From 1989 to 2016, the top 1%, 0.1%, and 0.01% wealth shares increased by 7.6, 5.1, and 3.0 percentage points, respectively, to 31.5%, 15.0%, and 7.0%. While these changes are less dramatic than some prior estimates, wealth is very concentrated: the top 1% holds nearly as much wealth as either the bottom 90% or the "P90-99" class. We discuss implications for income inequality measures, capital tax policy, and savings behavior.

Have the idle rich replaced the working rich at the top of the U.S. income distribution? Using tax data linking 11 million firms to their owners, this paper finds that entrepreneurs who actively manage their firms are key for top income inequality. Most top income is non-wage income, a primary source of which is private business profit. These profits accrue to working-age owners of closely-held, mid-market firms in skill-intensive industries. Private business profit falls by three-quarters after owner retirement or premature death. Classifying three-quarters of private business profit as human capital income, we find that most top earners are working rich: they derive most of their income from human capital, not physical or financial capital. The human capital income of private business owners exceeds top wage income and top public equity income. Growth in private business profit is explained by both rising productivity and a rising share of value added accruing to owners.

Consumption-based asset-pricing models have experienced success in recent years by augmenting the consumption process in 'exotic' ways. Two notable examples are the Long-Run Risk and rare disaster frameworks. Such models are difficult to characterize from consumption data alone. Accordingly, concerns have been raised regarding their specification. Acknowledging that both phenomena are naturally subject to ambiguity, we show that an ambiguity-averse agent may behave as if Long-Run Risk and disasters exist even if they do not or exaggerate them if they do. Consequently, prices may be misleading in characterizing these phenomena since they encode a pessimistic perspective of the data-generating process.

Bosek and Krawczyk exhibited an online algorithm for partitioning an online poset of width $w$ into $w^{14\lg w}$ chains. We improve this to $w^{6.5 \lg w + 7}$ with a simpler and shorter proof by combining the work of Bosek & Krawczyk with work of Kierstead & Smith on First-Fit chain partitioning of ladder-free posets. We also provide examples illustrating the limits of our approach.

We study rising business income among top income households using a large sample of U.S. firms linked to their owners and workers from 1999-2015. We establish four findings. First, business income growth accounts for nearly all of the rise in top income inequality in the 21st century. Second, business income growth is broad-based across sectors and not concentrated among a few top firms. Third, top-owned firms earn higher profit margins than other firms, which is primarily due to diverging firm performance rather than reallocation of top ownership, risk, or disguised labor payments for tax minimization. Fourth, profits have increased most in firms that rely on high-skilled human capital, especially when deployed at the top of the organization. Our findings suggest that market-based forces play a leading role in generating not only labor income inequality, but also capital income inequality. These findings have implications for central issues in economics: the measurement and determinants of income inequality and the labor share, the dispersion in firm productivity, the returns to schooling, and taxation of top labor and capital income.

Using Bayesian methods, we estimate a nonlinear DSGE model in which the interest-rate lower bound is occasionally binding. We quantify the size and nature of disturbances that pushed the U.S. economy to the lower bound in late 2008 as well as the contribution of the lower bound constraint to the resulting economic slump. We find that the interest-rate lower bound was a significant constraint on monetary policy that exacerbated the recession and inhibited the recovery, as our mean estimates imply that the zero lower bound (ZLB) accounted for about 30 percent of the sharp contraction in U.S. GDP that occurred in 2009 and an even larger fraction of the slow recovery that followed.

We study rising business income among top income households using a large sample of U.S. firms linked to their owners and workers from 1999-2015. We establish four findings. First, business income growth accounts for nearly all of the rise in top income inequality in the 21st century. Second, business income growth is broad-based across sectors and not concentrated among a few top firms. Third, top-owned firms earn higher profit margins than other firms, which is primarily due to diverging firm performance rather than reallocation of top ownership, risk, or disguised labor payments for tax minimization. Fourth, profits have increased most in firms that rely on high-skilled human capital, especially when deployed at the top of the organization. Our findings suggest that market-based forces play a leading role in generating not only labor income inequality, but also capital income inequality. These findings have implications for central issues in economics: the measurement and determinants of income inequality and the labor share, the dispersion in firm productivity, the returns to schooling, and taxation of top labor and capital income.

Bosek and Krawczyk exhibited an on-line algorithm for partitioning an on-line poset of width $w$ into $w^{14\lg w}$ chains. They also observed that the problem of on-line chain partitioning of general posets of width $w$ could be reduced to First-Fit chain partitioning of $(2w^2 + 1)$-ladder-free posets of width $w$, where an $m$-ladder is the transitive closure of the union of two incomparable chains $x_1\le...\le x_m$, $y_1\le...\le y_m$ and the set of comparabilities $\{x_1\le y_1,..., x_m\le y_m\}$. Here, we improve the subexponential upper bound to $w^{6.5\lg w + O(1)}$ with a simplified proof, exploiting the First-Fit algorithm on ladder-free posets.

This paper derives the conditions under which the elasticity of capital with respect to the net of corporate tax rate is positive. In doing so, this model nests the traditional result originating from the works of Harberger [1962] and Jorgenson [1967] and the neutrality result obtained by Stiglitz [1973] and Sandmo [1974]. The key assumption is how the marginal dollar of investment is financed. If the marginal dollar is financed at a cost equal to debt financing the neutrality result obtains. If, instead, the marginal dollar is financed through equity, which is not tax deductible, then the traditional result obtains. We test this implication of the model using administrative tax records for the population of US corporations and the control group bunching method in Patel, Seegert, and Smith (2014).

In a real business cycle model, an agent's fear of model misspecification interacts with stochastic volatility to induce time varying worst case scenarios. These time varying worst case scenarios capture a notion of animal spirits where the probability distributions used to evaluate decision rules and price assets do not necessarily reflect the fundamental characteristics of the economy. Households entertain a pessimistic view of the world and their pessimism varies with the overall level of volatility in the economy, implying an amplification of the effects of volatility shocks. By using perturbation methods and Monte Carlo techniques we extend the class of models analyzed with robust control methods to include the sort of nonlinear production-based DSGE models that are popular in academic research and policymaking practice.

We examine the asset pricing properties of an endowment economy featuring stochastic volatility and an agent who fears his model is misspecified. Due to the nonlinearities inherent in our stochastic volatility model, we are forced to expand the toolkit of the robust control literature. We propose novel algorithms to characterize and simulate the robust agent’s worst case model. Using US consumption data, we estimate the parameters of the endowment process and find evidence of stochastic volatility. Introducing stochastic volatility helps the model generate a more plausible unconditional market price of risk and increases the measured welfare costs of business cycles by about 15%. These asset pricing and welfare results are the result of an agent valuing consumption streams as if a worst case model has generated the data. In our stochastic volatility set up, the robust agent’s worst case consumption growth process contains both disasters and a long run risk component.