Silvawelsh6172

The results of this implementation show that this framework was able to detect and track the topics at a level comparable to manual annotation. To better explain the emerging and changing topics in various locations over time the result is graphically displayed on top of the United States map.In India, the government launched a US$22.6 billion financial support package for the poor and marginalized as a result of Covid-19. Approximately US$ 4.2 billion (INR 310 billion) came from a vast pile of unspent social special-purpose funds. How and why did such a large volume of funds accumulate in the first place, and why did it take a public health emergency to release them? What might be the consequences of their use under such emergency conditions - especially for our understanding of governance and accountability in social welfare provision? This paper presents a brief analysis of two preliminary case studies of specific social special-purpose funds in India. We rely on a handful of unstructured interviews and informal discussions with subnational government officials, civil society actors, trade union representatives, and local community leaders that began in January 2020, and which were pursued virtually following the lockdown. This is bolstered by analysis of primary documents, including Comptroller and Auditor General of India (CAG) reports, relevant laws, and contemporary press coverage. We argue that non-disbursement should be understood as a institutional matter, and not only as technical or implementation failure. Moreover, as such funds are likely to mushroom following Covid-19, our findings suggest that policymakers should focus on the institutional design, decision-making and accountability structures for the flow and distribution of Covid funds, rather than merely emphasising their collection.In the context of the major potential impacts of COVID-19 on agriculture and agricultural trade in developing countries, this Viewpoint discusses the advantages of adopting a conceptual framework previously used to discuss the impact of the HIV/AIDS pandemic on agriculture and rural livelihoods. The framework is made up of two pairs of linked concepts 1) Susceptibility or the chance of an individual becoming infected; 2) Resistance or the ability of an individual to avoid infection; 3) Vulnerability or the likelihood of significant impacts occurring at individual, household or community level; and 4) Resilience the active responses that enable people to avoid the worst impacts of an epidemic at different levels or to recover faster to a level accepted as normal. This framework allows the clear formulation of key questions for COVID-19 factors in the labor process itself that make people more or less susceptible; broader socio-economic and biophysical determinants of susceptibility; factors that make farm households, food enterprises and value chains more vulnerable to the impacts of the pandemic; and aspects of COVID-19 responses by governments and the private sector that might increase vulnerability. 1-Deoxynojirimycin manufacturer Brief examples of susceptibility of value chain operations and of their vulnerability to COVID-19 lockdown measures are given. A focus on resistance and resilience encourages investigation of local-level responses by communities and NGOs, which with appropriate monitoring and learning could be scaled up.Zoonotic pathogens and parasites that are transmitted from vertebrates to humans are a major public health risk with high associated global economic costs. The spread of these pathogens and risk of transmission accelerate with recent anthropogenic land-use changes (LUC) such as deforestation, urbanisation, and agricultural intensification, factors that are expected to increase in the future due to human population expansion and increasing demand for resources.We systematically review the literature on anthropogenic LUC and zoonotic diseases, highlighting the most prominent mammalian reservoirs and pathogens, and identifying avenues for future research.The majority of studies were global reviews that did not focus on specific taxa. South America and Asia were the most-studied regions, while the most-studied LUC was urbanisation. Livestock were studied more within the context of agricultural intensification, carnivores with urbanisation and helminths, bats with deforestation and viruses, and primates with habitg in mammals.We consider a retail firm selling a durable product in a volatile market where the demand is price-sensitive and random but its distribution is unknown. The firm dynamically replenishes inventory and adjusts prices over time and learns about the demand distribution. Assuming that the demand model is of the multiplicative form and unmet demand is partially backlogged, we take the empirical Bayesian approach to formulate the problem as a stochastic dynamic program. We first identify a set of regularity conditions on demand models and show that the state-dependent base-stock list-price policy is optimal. We next employ the dimensionality reduction approach to separate the scale factor that captures observed demand information from the optimal profit function, which yields a normalized dynamic program that is more tractable. We also analyze the effect of demand learning on the optimal policy using the system without Bayesian update as a benchmark. We further extend our analysis to the case with unobserved lost sales and the case with additive demand.There has been an increased interest in optimizing pricing and sourcing decisions under supplier competition with supply disruptions. In this paper, we conduct an analytical game-theoretical study to examine the effects of supply capacity disruption timing on pricing decisions for substitute products in a two-supplier one-retailer supply chain setting. We investigate whether the timing of a disruption may significantly impact the optimal pricing strategy of the retailer. We derive the optimal pricing strategy and ordering levels with both disruption timing and product substitution. By exploring both the Nash and Stackelberg games, we find that the order quantity with the disrupted supplier depends on price leadership and it tends to increase when the non-disrupted supplier is the leader. Moreover, the equilibrium market retail prices are higher under higher levels of disruption for the Nash game, compared to the Stackelberg game. We also uncover that the non-disrupted supplier can always charge the highest wholesale price if a disruption occurs before orders are received. This highlights the critical role of order timing. The insights can help operations managers to proper design risk mitigation ordering strategies and re-design the supply contracts in the presence of product substitution under supply disruptions.Concepts of sharing and commons are normatively and historically ambivalent. Some forms of sharing, such as sharecropping or alms-giving, proceed from and sustain asymmetrical relations to the means of life. Access to commons in other social contexts merely serves to make unequal forms of life more bearable. In other words, some expressions of sharing and commons are "functional" within hierarchical societies. Departing from these observations, this contribution traces contests over the logic of sharing, and apportioned shares of common land, from Brazil's slave period through contemporary land rights movements in the northeastern state of Bahia. For former slaves and their descendants, "freedom" often meant sharecropping on the same plantations from which they had been released. However, rural Brazilians have also succeeded in transforming shared land into more equal and equitable distributions, from "peasant breaches" that emerged in slave gardens from the early colonial period through the abolition of slavery, to land occupations that occurred in the late twentieth century. By sharing land and other material resources-especially tree seeds, seedlings, and cuttings-rural laborers have established unexpected reconfigurations in distributions of property and social recognition that exceed institutionalized norms of sharing common land. With such outcomes in view, this contribution distinguishes socially replicative and transformative sharing.In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I show that the basic reproduction number R 0 can be identified from the data, conditional on epidemiologic parameters, and propose several nonlinear SUR approaches to estimate R 0 . I examine the performance of these methods using Monte Carlo studies and demonstrate that they yield fairly accurate estimates of R 0 . Next, I apply these methods to estimate R 0 for the US, California, and Japan, and document heterogeneity in the value of R 0 across regions. My estimation approach accounts for possible underreporting of the number of cases. I demonstrate that if one fails to take underreporting into account and estimates R 0 from the reported cases data, the resulting estimate of R 0 may be biased downward and the resulting forecasts may exaggerate the long run number of deaths. Finally, I discuss how auxiliary information from random tests can be used to calibrate the initial parameters of the model and narrow down the range of possible forecasts of the future number of deaths.We propose a model with involuntary unemployment, incomplete markets, and nominal rigidity, in which the effects of government spending are state-dependent. An increase in government purchases raises aggregate demand, tightens the labor market and reduces unemployment. This in turn lowers unemployment risk and thus precautionary saving, leading to a larger response of private consumption than in a model with perfect insurance. The output multiplier is further amplified through a composition effect, as the fraction of high-consumption households in total population increases in response to the spending shock. These features, along with the matching frictions in the labor market, generate significantly larger multipliers in recessions than in expansions. As the pool of job seekers is larger during downturns than during expansions, the concavity of the job-finding probability with respect to market tightness implies that an increase in government spending reduces unemployment risk more in the former case than in the latter, giving rise to countercyclical multipliers.