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We compare the deinstitutionalization of psychiatric patients and the developmentally disabled in the United States and demonstrate that there were two path-dependent processes with significant qualitative and quantitative differences, ultimately leading to better outcomes for developmentally disabled individuals.

Using secondary literature, we construct a sustained comparison of the two processes in terms of outcomes, timing, tempo, extent, funding, demographic composition, and investment in community services. We then reconstruct the strategies of de-stigmatization and framings of moral worth deployed in the two cases, analyzing their effects on deinstitutionalization in terms of conceptions of risk, rights, and care.

Deinstitutionalization began later for developmentally disabled individuals than for psychiatric patients, and was a more gradual, protracted process. It was not driven by fiscal conservatism, discharges, and the trans-institutionalization of the senile aged, as was deinstitutionalization for psychiatric patients, but primarily by the prevention of institutionalization of young children, and increased investment in infrastructure. Consequently, the deinstitutionalization of the developmentally disabled was far more thorough and successful. The process was shaped by the framing of the developmentally disabled as “forever children” by parents’ organizations that demanded a balance between autonomy, protection, and the provision of care. In contrast, the deinstitutionalization of psychiatric patients was shaped by their framing as autonomous citizens temporarily suffering from “mental health problems” that could be prevented, treated, and cured. This frame foregrounded the right to choose (and also refuse) treatment, while undervaluing the provision of care.

This chapter uses the case of American think tanks to develop the idea of a “boundary organization,” or a formal organization that acquires its distinctiveness and efficacy from its intermediate location in the social structure. Traversing, overlapping, and incorporating the logics of multiple institutional spheres – including those of academia, politics, business, and the market – think tanks at first seem to be organizations “divided against themselves.” However, by gathering complex mixtures of otherwise discordant resources, they create novel products, carry out novel practices, and claim for themselves a crucial mediating role in the social structure. This chapter's ultimate aim is to consider the implications of this idea for theories of organizational power. With respect to this aim, I argue that boundary organizations – and organizational boundary-making processes in general – underscore the need to think about power in relational and processional terms.

Market-based approaches to environmental management are increasingly common. In 1983 when Joeres and David published their pioneering collection, Buying a Better Environment, the concept was seen as at best novel, and at worst far-fetched. Yet today, conservation and water quality credits are for sale in many developed countries, and the idea of payment for ecosystem services is ubiquitous in environmental policy circles. This paper traces that shift from command-and-control to market-based environmental management through analysis of the evolving practice of stream mitigation banking (SMB) in the US. In the most common form of SMB today, a for-profit company buys land with a damaged stream on it and restores it to produce mitigation credits which can then be purchased by developers to fulfill their permit conditions under the Clean Water Act. Though decidedly noncommercial in origin, SMB was converted into for-profit tradable regulatory mechanism in 2000 and has since spread rapidly across the US with the strong support of the US Environmental Protection Agency. Using Bourdieu’s field concept as a framework, I argue that the neoliberal transformation of mitigation banking is a product of both relations within the regulatory field, of that field’s relations with the fields of science, and of power.

Does the rise of foreign direct investment (FDI) in Central and Eastern Europe lead to supraterritoriality? The analysis of FDI flows between world investor countries and Central and East European (CEE) hosts between 1989 and 2000 shows that the majority of FDI flows into CEE in this period do not exemplify a trend of undifferentiated transcendence of post-communist borders. Rather, FDI flows continue to be based in territoriality and embedded in existing social relations between investor and host countries: migration and trade flows, historical ties, political alliances, and cultural affinities. Nevertheless, the rhetoric supporting the opening of post-communist countries to FDI is widespread and consistent with the neoliberal credo, which has acquired a supraterritorial character. Ultimately, we see that embeddedness and supraterritoriality co-exist but they manifest themselves for distinct FDI phenomena: the concrete economic practice and the economic rhetoric, respectively.

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.