Knowledge that Transforms

We study the history and geography of wealth accumulation in the United States using newly collected historical property tax records from the early 1800s onward. These records come from the administration of the General Property Tax–a tax that aspired to cover all types of property. We construct wealth series at the state, county, and national levels. At the state level, we use annual assessed values of wealth from state-level reports drawn from multiple sources. Because assessed values may differ from market values, we also require assessment ratios, defined as the ratio of assessed to market wealth. We obtain state-level assessment ratios from decadal U.S. Census wealth data (drawn from various Census reports, including those on “Wealth, Debt, and Taxation”), in which the Census carried out detailed valuation work, complemented with information on changes in assessment practices from the state reports, to build higher-frequency series of assessment ratios. The result is long-run annual wealth series for states from 1850 (or earlier, depending on the state) to 1935. We obtain national wealth series by aggregating these state series. At the county level, we use assessed values (or market values where available) from the U.S. Census wealth data for each decade, and apply either state-level or county-level assessment ratios, where available, to obtain market values of wealth for each decade from 1850 to 1930. We use these data to show, first, that the United States experienced extraordinary wealth accumulation after the Civil War and until the Great Depression. Second, spatial inequality in the United States has been large and highly persistent since the mid-1800s. We also examine the determinants of long-term wealth growth and find, among other results, that counties with a higher share of enslaved property before the Civil War or with higher wealth inequality experienced lower subsequent long-run wealth growth.

Discretionary announcement timing generates high conditional risk premia of stock returns and a pattern of negative drifts followed by positive jumps. Average announcement returns are much larger than unconditional risk premia. Capital Asset Pricing Model alphas turn negative when conditioning on nondisclosure because betas rise faster than risk premia prior to disclosures, but average announcement returns may appear to be too large relative to market risk when betas are estimated from past returns. The effects are amplified when multiple firms exercise discretion over the timing of correlated announcements. We present evidence that firms time earnings announcements in a manner consistent with our model.

In this paper, I document systematic heterogeneity in ownership and financing of firms across Eurozone countries. To rationalize these differences, I build a quantitative general equilibrium model of workers and entrepreneurs who choose debt and equity financing of their firms, subject to rich country-specific financial frictions. The novel data on firm ownership and financing, combined with the structure of the model, allows me to quantify the level of debt and equity frictions in each country. Quantitatively, I find much larger output effects from equity frictions: harmonizing them across countries would lead to nearly four times larger output effects compared to debt frictions, and removing them would increase aggregate output by 73% more. The larger impact on output is due not only to the estimated levels and dispersion of equity frictions but also to the greater risk sharing provided by equity, which further incentivizes entrepreneurs to expand their firms. Through their effect on risk sharing, equity frictions also rationalize the observed negative relationship between equity financing and wealth inequality. Quantitatively, they are responsible for over 70% of the explained variation in top wealth shares across countries.

Using transaction data from a U.S. account aggregator, I study how household balance-sheet conditions drive within-person variation in marginal propensities to consume, repay debt, and invest across three rounds of pandemic stimulus. Using a machine learning imputation estimator, I measure the sensitivity of responses to time-varying financial circumstances. Spending responses fall as liquid assets increase, while debt repayments crowd out consumption only for those with binding borrowing limits. Transfers also encourage retail investment in stocks and cryptocurrencies at both intensive and extensive margins. The findings show how liquidity constraints and debt overhang influence the allocation of transfers across spending, deleveraging, and financial assets.