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I analyze factory worker households in the early 1920s in Osaka to examine idiosyncratic income shocks and consumption. Using the household-level monthly panel dataset, I find that while households could not fully cope with idiosyncratic income shocks at that time, they mitigated fluctuations in indispensable consumption during economic hardship. In terms of risk-coping mechanisms, I find suggestive evidence that savings institutions helped mitigate vulnerabilities and that both using borrowing institutions and adjusting labor supply served as risk-coping strategies among households with less savings.

Due to a dearth of data, nineteenth century lending to sovereign borrowers was a blind date. We argue this is the reason for collateral pledges found in contemporary lending covenants, which enabled not execution, but the production of reliable fiscal data. Lawyers injected collateral clauses in sovereign debt covenants to permit credible disclosure of hard-to-access tax data. The study foregrounds the importance of big law firms as financial intermediaries and information producers. It also contributes a new view on the role played by contracts in sovereign debt. “No [underwriting] firm can take precautions against the repudiation of a [sovereign] hypothecation.”—Thomas Baring, 1865

This paper estimates the migratory and fertility effects of the federal Relocation Program, which attempted to move Native American individuals to urban areas under the promises of financial assistance and job training. I find the Relocation Program increased the Native American population in the target cities by more than 100,000 people. I also find that second- and third-generation Native American women living in cities have a 50 percent lower fertility rate than those living in areas with historically large Native American populations. These findings indicate that this program meaningfully shifted the spatial distribution of the Native American population.

This paper shows how to remove attenuation bias in regression analyses due to measurement error in historical data for a given variable of interest by using a secondary measure that can be easily generated from digitized newspapers. We provide three methods for using this secondary variable to deal with non-classical measurement error in a binary treatment: set identification, bias reduction via sample restriction, and a parametric bias correction. We demonstrate the usefulness of our methods by replicating four recent economic history papers. Relative to the initial analyses, our results yield markedly larger coefficient estimates.