During World War II, the US government's Office of Scientific Research and Development (OSRD) supported one of the largest public investments in applied R&D in US history. Using data on all OSRD-funded invention, we show this shock had a formative impact on the US innovation system, catalyzing technology clusters across the country, with accompanying increases in high-tech entrepreneurship and employment. These effects persist until at least the 1970s and appear to be driven by agglomerative forces and endogenous growth. In addition to creating technology clusters, wartime R&D permanently changed the trajectory of overall US innovation in the direction of OSRD-funded technologies.

Social scientists commonly seek to make statements about how word use varies over circumstances—including time, partisan identity, or some other document-level covariate. For example, researchers might wish to know how Republicans and Democrats diverge in their understanding of the term “immigration.” Building on the success of pretrained language models, we introduce the à la carte on text (conText) embedding regression model for this purpose. This fast and simple method produces valid vector representations of how words are used—and thus what words “mean”—in different contexts. We show that it outperforms slower, more complicated alternatives and works well even with very few documents. The model also allows for hypothesis testing and statements about statistical significance. We demonstrate that it can be used for a broad range of important tasks, including understanding US polarization, historical legislative development, and sentiment detection. We provide open-source software for fitting the model.

Despite social scientists’ long-standing interest in the influences of siblings, previous research has not settled the debates on how relevant sibship size is to child development and whether growing up with more siblings could be beneficial. Using 30 years of longitudinal data and fixed-effects models, this study offers the most comprehensive evidence on how sibship size is tied to cognitive and sociobehavioral development. We also advance the literature by systematically comparing the consequences of gaining a sibling for children with varying ordinal positions. Contrary to prior studies using selective data from limited observation spans, we find that children experience net decreases in cognitive test scores as their family size grows. At the same time, our analysis shows that sibling additions are only important to first- and second-born children’s—not later-born children’s—cognitive development. Even for the first- and second-born, the marginal effect of adding a sibling lessens with each addition. Our results thus demonstrate the time-dependent nature of family resource-dilution processes. For sociobehavioral development, the evidence indicates that having an older sibling is beneficial, but gaining a younger sibling increases behavioral problems for some (e.g., first-born children). Because more children from large families have older siblings, children from larger families exhibit less problematic behavior, on average. By uncovering the complex relationship between siblings and noncognitive development, this study also generally contributes to the sociology of family and inequality.

Merging datafiles containing information on overlapping sets of entities is a challenging task in the absence of unique identifiers, and is further complicated when some entities are duplicated in the datafiles. Most approaches to this problem have focused on linking two files assumed to be free of duplicates, or on detecting which records in a single file are duplicates. However, it is common in practice to encounter scenarios that fit somewhere in between or beyond these two settings. We propose a Bayesian approach for the general setting of multifile record linkage and duplicate detection. We use a novel partition representation to propose a structured prior for partitions that can incorporate prior information about the data collection processes of the datafiles in a flexible manner, and extend previous models for comparison data to accommodate the multifile setting. We also introduce a family of loss functions to derive Bayes estimates of partitions that allow uncertain portions of the partitions to be left unresolved. The performance of our proposed methodology is explored through extensive simulations. Supplementary materials for this article are available online.

Construction booms have dominated Albania's economy and politics since the late 1990s. These booms continued even during times of illiquidity. One of the sources of financing construction in Albania is the practice of klering (in-kind payments). In this practice, developers pay subcontractors in (future) apartments in exchange for materials and labor. I argue that, in klering transactions, housing serves as an asset and a means of payment. The practice of klering emerged at the interface of postcommunist transformations, neoliberal reforms, and the fetishization of housing as an asset of more durable and multifaceted economic and cultural value. While grounded in the local histories and values of housing, klering is made possible by a fuzzy property regime, systemic corruption, and widespread informality. At the same time, klering echoes other global patterns pertaining to housing, such as the rise of asset economy, financialization, and money laundering through real estate purchases. The klering economy echoes speculative logics and practices that are prevalent across and that link centers and peripheries, formal and informal markets. These economic logics generate uncertainty and ambiguity; they mobilize social networks and cultural imaginaries; and they thrive on and further reproduce deep social and economic inequalities.

This article uncovers the financial knowledge and bond market campaigns of the paradigmatic non-violent revolution of the twentieth century—the civil rights movement. It builds on an interpretation made by the Congress of Racial Equality (CORE) and the National Association for the Advancement of Colored People (NAACP) during the early 1960s: segregation was a national problem because it was financed through a network of bankers across the country who specialized in the business of debt; certified by prominent bond attorneys in New York City, Chicago, and elsewhere; and because investors from around the country collected tax-exempt interest payments from indebted southern segregated municipalities. By weaving the internal memos, protest ephemera, and legal strategies of civil rights activists together with the credit assessments, scheduled bond offerings, and perspectives of financiers, this article reconstructs the attempts to politicize bond market transactions and efforts to place the economic certainty of segregation in doubt. In so doing, it offers a fresh perspective on the so-called classic phase of the civil rights movement (1954–1965). More generally, it raises powerful questions about the dilemmas of investment-focused campaigns, and how finance capital compounds the difficulties of organizing against authoritarian regimes.

At the onset of the COVID-19 pandemic, psychological scientists frequently made on-the-record predictions in public media about how individuals and society would change. Such predictions were often made outside these scientists’ areas of expertise, with justifications based on intuition, heuristics, and analogical reasoning (Study 1; N = 719 statements). How accurate are these kinds of judgments regarding societal change? In Study 2, we obtained predictions from scientists (N = 717) and lay Americans (N = 394) in Spring 2020 regarding the direction of change for a range of social and psychological phenomena. We compared them to objective data obtained at 6 months and 1 year. To further probe how experience impacts such judgments, 6 months later (Study 3), we obtained retrospective judgments of societal change for the same domains (Nscientists = 270; Nlaypeople = 411). Bayesian analysis suggested greater credibility of the null hypothesis that scientists’ judgments were at chance on average for both prospective and retrospective judgments. Moreover, neither domain-general expertise (i.e., judgmental accuracy of scientists compared to laypeople) nor self-identified domain-specific expertise improved accuracy. In a follow-up study on meta-accuracy (Study 4), we show that the public nevertheless expects psychological scientists to make more accurate predictions about individual and societal change compared to most other scientific disciplines, politicians, and nonscientists, and they prefer to follow their recommendations. These findings raise questions about the role psychological scientists could and should play in helping the public and policymakers plan for future events.