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Estimating the contribution of enslaved workers to output and growth in the United States during the first half of the nineteenth century is a crucial building block to better understand the contours of nineteenth‐century US economic history, and more generally, the connection between slavery and capitalism. Existing estimates only present a partial picture and are potentially problematic. In this paper, we use data on enslaved person valuations to calculate the contribution made by enslaved workers to regional and national gross national product (GNP) in 1839 and 1859 and to the growth in per capita output in the 20 years before the Civil War. We find that in the United States, enslaved workers were responsible for somewhere between 12.49 per cent and 18.0 per cent of the increase in output per capita between 1839 and 1859. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the onset of and policy response to the novel coronavirus in the United States. The WEI is a weekly composite index of real economic activity, with eight of 10 series available the Thursday after the end of the reference week. In addition to being a weekly real activity index, the WEI has strong predictive power for output measures and provided an accurate nowcast of current‐quarter GDP growth in the first half of 2020, with weaker performance in the second half. We document how the WEI responded to key events and data releases during the first 10 months of the pandemic. [ABSTRACT FROM AUTHOR]

This paper estimates the medium‐ to long‐run effects of slavery in the United States in a spatial regression discontinuity design. Using the boundary between free and slave states immediately antebellum, we find that legal slavery decreased per capita manufacturing output by as much as 30% in the decades following the Civil War. Perhaps surprisingly, agricultural output and farm values were only briefly depressed in former slave states after the war. Although emancipation ended slavery, political forces kept the institution from being completely disintegrated, and we explore channels through which this was possible. We show that slavery affected the structure of the economy in a given region—specifically through agricultural production decisions—and that these structures persisted long after passage of the 13th Amendment. However, sharecropping played a relatively small role in this region. Our results support mounting evidence in recent literature of the significant and lasting effects of institutions on economic development. [ABSTRACT FROM AUTHOR]

As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are "good" or "bad." This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like "season" or "variability," and that simple decompositions can be combined to represent more complex concepts, like "seasonal variability," creating an expressive language through which to interrogate models and data. Plain Language Summary: Models are essential scientific tools for explaining and predicting phenomena ranging from weather and climate, to health outcomes, to economic development, to the origins of the universe, and testing competing models is one of the most basic scientific activites. Yet, how scientists evaluate and justify their models can be inconsistent or even arbitrary. Traditionally, one performance metric—such as mean squared error—is used to identify the best model, but one metric provides little insight into what aspects of a model are "good" or "bad." This paper proposes a basic language for expressing different aspects of a model's performance. On one hand, this is useful for determining which aspects of model may require revision, but it also allows the modeler to separate out the best elements among several models and combine them to form an ensemble, analogous to how an audio engineer mixes together multiple tracks to form the best rendition of a musical piece. Key Points: Mean squared error (MSE) is an objective but somewhat enigmatic measure of model performanceMSE can be decomposed into components that quantify specific aspects of model performance, such as bias and varianceMixing components among models yields a form of ensemble prediction [ABSTRACT FROM AUTHOR]

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Economists have developed a vast empirical literature on how cultural traits like generalized trust affect economic output. Much of this literature finds a positive causal relationship between measures of generalized trust, as gathered by international surveys, and economic output. However, the trust literature commits five deadly empirical and theoretical sins that undermine many of its findings. From the quality of the survey questions and responses to the paucity of theoretical models used to explain how trust affects economic outcomes to the radically different results from experimental evidence and others, the trust literature is riven with poor methods and bad data. Even so, applying common methods used in the trust literature to regional level analysis in the United States during the 1972–2018 period reveals no statistically significant correlation between economic output and trust. Given our lack of findings at the subnational level, we find further evidence to be skeptical of the trust literature. [ABSTRACT FROM AUTHOR]

R&D subsidisation is acknowledged as a major spurrer of the economic development of regions, but is seldom considered in the literature concerning regional diversification. Conversely, robust evidence has been gathered about the impact of relatedness on the ability of regions to explore new technological domains. The work employs patent data to assess the extent to which related and unrelated diversification processes in the United States, between 1981 and 2010, are influenced by the existing regional knowledge base and the magnitude of R&D public funds, providing evidence for both drivers by means of econometric estimations. [ABSTRACT FROM AUTHOR]

Privatization of state government services is commonplace, but our understanding of its effects is limited by data availability. We study the relationship between American state government contracting and public sector wages. Governments have used public sector employment to support a variety of goals, including social equity and economic development, but privatization, as a new public management (NPM) reform, may shift the focus. Our empirical analysis shows that state privatization of service delivery is associated with decreases in the public sector wage premium, but that these effects are not driven by gender, race, or low‐levels of educational attainment. The fidelity of the implementation to NPM values conditions these effects. We also find that contracting service delivery is associated with a lower public sector wage premium for middle‐class workers. [ABSTRACT FROM AUTHOR]