Buckleyrichardson6775

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.The emergence of urban tech economies calls attention to the multidimensional spatiality of ecosystems made up of people and organizations that produce new digital technology. Since the economic crisis of 2008, city governments have aggressively pursued economic growth by nurturing these ecosystems. Elected officials create public-private-nonprofit partnerships to build an "innovation complex" of discursive, organizational, and geographical spaces; they aim not only to jump-start economic growth but to remake the city for a new modernity. But it is difficult to insert tech production space into the complicated urban matrix. Embedded industries and social communities want protection from expanding tech companies and the real estate developers who build for them. City council members, state legislators, and community organizations oppose the city government's attempts to satisfy Big Tech companies. While the city's density magnifies conflicts of interest over land-use and labor issues, the covid-19 pandemic raises serious questions about the city's ability to both oppose Big Tech and keep creating tech jobs.The rapid growth of Uber and analogous platform companies has led to considerable scholarly interest in the phenomenon of platform labor. Scholars have taken two main approaches to explaining outcomes for platform work-precarity, which focuses on employment classification and insecure labor, and technological control via algorithms. Both predict that workers will have relatively common experiences. On the basis of 112 in-depth interviews with workers on seven platforms (Airbnb, TaskRabbit, Turo, Uber, Lyft, Postmates, and Favor) we find heterogeneity of experiences across and within platforms. We argue that because platform labor is weakly institutionalized, worker satisfaction, autonomy, and earnings vary significantly across and within platforms, suggesting dominant interpretations are insufficient. We find that the extent to which workers are dependent on platform income to pay basic expenses rather than working for supplemental income explains the variation in outcomes, with supplemental earners being more satisfied and higher-earning. This suggests platforms are free-riding on conventional employers. We also find that platforms are hierarchically ordered, in terms of what providers can earn, conditions of work, and their ability to produce satisfied workers. Our findings suggest the need for a new analytic approach to platforms, which emphasizes labor force diversity, connections to conventional labor markets, and worker dependence.This article outlines a sociological agenda for the era of "tech," a period when digital technologies have come to dominate our social lives. It argues that we should break "tech" down into two parts, the production side and the consumption side. The production side concerns the ways in which these technologies are made, the social actors involved on the design, financing, and production side, and the consumption side refers to the ways in which ordinary users make use of these technologies and the ways in which their use is transforming everyday life. D-Luciferin molecular weight The article maintains that this is an area of research to which sociologists need to pay much greater attention if they are to understand the contemporary world satisfactorily.At present, many international organizations and scholars, who are aimed to compare and assess country-specific economy or competitiveness, have set different standards and indicators and tried to assess the economic strength of individual country. But most of these standards and indicators are for the assessment of individual aspects and what is worse, they are not suitable for the real situation of the countries concerned. This paper deals with methodological issues on the assessment of state economic strength. To this end, authors investigate the preceding studies on the assessment of economy of a given country, conceptualize the state economic strength, set a new system of indicators for assessing it and on this basis, produce a methodology for the synthetic assessment of state economic strength. The findings are that state economic strength must be defined in a view of economic capability which any country can exhibit by itself even under uncertain external environment, the indicators for assessing it include a variety of indicators in line with its essence, and assessment methodology must be synthetic one based on considering the weights of indicators.