scholarly journals Culture, COVID-19, and Collectivism: A Paradox of American Exceptionalism?

2020 ◽  
Author(s):  
Gregory D. Webster ◽  
Jennifer Lee Howell ◽  
Joy Ellen Losee ◽  
Elizabeth Mahar ◽  
Val Wongsomboon
Keyword(s):  
Spatial Dependence ◽  
State Level ◽  
American Exceptionalism ◽  
White Population ◽  
Death Rates ◽  
Per Capita Gdp ◽  
Country Level ◽  
Per Capita ◽  
The U.S ◽  
Relevant Covariates

We examined archival data from 98 countries (Study 1) and the 48 contiguous United States (Study 2) on country/state-level collectivism, COVID-19 case/death rates, relevant covariates (per-capita GDP, population density, spatial dependence), and in the U.S., percent of non-Whites. In Study 1, country-level collectivism negatively related to both cases (r = -.28) and deaths (r = -.40) in simple regressions; however, after controlling for covariates, the former became non-significant (rp = -.07), but the later remained significant (rp = -.20). In Study 2, state-level collectivism positively related to both cases (r = .56) and deaths (r = .41) in simple regressions, and these relationships persisted after controlling for all covariates except race, where a state’s non-White population dominated all other predictors of COVID-19 cases (rp = .35) and deaths (rp = .31). We discuss the strong link between race and collectivism in U.S. culture, and its implications for understanding COVID-19 responses.

IS THE AMOUNT OF DEATH INSTINCT CONSTANT AMONG U.S. INDIAN HEALTH SERVICE AREAS: A REPLICATION

1991 ◽  
Vol 19 (2) ◽  
pp. 105-107
Author(s):  
Thomas T. Young
Keyword(s):  
Native American ◽  
Heart Disease ◽  
Health Service ◽  
Indian Health Service ◽  
Death Instinct ◽  
Death Rates ◽  
Indian Health ◽  
Service Areas ◽  
Per Capita ◽  
The U.S

Death rates for nonmotor vehicle related accidents, heart disease, and murder were obtained from the U.S. Indian Health Service for all 11 health service areas. In contrast to predictions derived from Tabachnick and Klugman's hypothesis that the amount of death instinct per capita in different regions should be constant, no statistically significant negative correlations were found, for these three variables. These findings replicate results, from earlier studies using Native and non-Native American populations.

scholarly journals Global warming has increased global economic inequality

2019 ◽  
Vol 116 (20) ◽  
pp. 9808-9813 ◽  
Author(s):  
Noah S. Diffenbaugh ◽  
Marshall Burke
Keyword(s):  
Economic Growth ◽  
Global Warming ◽  
Economic Inequality ◽  
Climate Forcing ◽  
Per Capita Gdp ◽  
Poor Countries ◽  
Economic Output ◽  
Country Level ◽  
Per Capita ◽  
Historical Temperature

Understanding the causes of economic inequality is critical for achieving equitable economic development. To investigate whether global warming has affected the recent evolution of inequality, we combine counterfactual historical temperature trajectories from a suite of global climate models with extensively replicated empirical evidence of the relationship between historical temperature fluctuations and economic growth. Together, these allow us to generate probabilistic country-level estimates of the influence of anthropogenic climate forcing on historical economic output. We find very high likelihood that anthropogenic climate forcing has increased economic inequality between countries. For example, per capita gross domestic product (GDP) has been reduced 17–31% at the poorest four deciles of the population-weighted country-level per capita GDP distribution, yielding a ratio between the top and bottom deciles that is 25% larger than in a world without global warming. As a result, although between-country inequality has decreased over the past half century, there is ∼90% likelihood that global warming has slowed that decrease. The primary driver is the parabolic relationship between temperature and economic growth, with warming increasing growth in cool countries and decreasing growth in warm countries. Although there is uncertainty in whether historical warming has benefited some temperate, rich countries, for most poor countries there is >90% likelihood that per capita GDP is lower today than if global warming had not occurred. Thus, our results show that, in addition to not sharing equally in the direct benefits of fossil fuel use, many poor countries have been significantly harmed by the warming arising from wealthy countries’ energy consumption.

scholarly journals Impact of altitude on COVID-19 infection and death in the United States: A modeling and observational study

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245055
Author(s):  
Kenton E. Stephens ◽  
Pavel Chernyavskiy ◽  
Danielle R. Bruns
Keyword(s):  
Regression Analysis ◽  
Population Density ◽  
The United States ◽  
Infection Rates ◽  
Treatment Interventions ◽  
Death Rates ◽  
Ongoing Work ◽  
Per Capita ◽  
Case Mortality ◽  
Relevant Covariates

Background COVID-19, the disease caused by SARS-CoV-2, has caused a pandemic, sparing few regions. However, limited reports suggest differing infection and death rates across geographic areas including populations that reside at higher elevations (HE). We aimed to determine if COVID-19 infection, death, and case mortality rates differed in higher versus low elevation (LE) U.S. counties. Methods Using publicly available geographic and COVID-19 data, we calculated per capita infection and death rates and case mortality in population density matched HE and LE U.S. counties. We also performed population-scale regression analysis to investigate the association between county elevation and COVID-19 infection rates. Findings Population density matching of LA (< 914m, n = 58) and HE (>2133m, n = 58) counties yielded significantly lower COVID-19 cases at HE versus LE (615 versus 905, p = 0.034). HE per capita deaths were significantly lower than LE (9.4 versus 19.5, p = 0.017). However, case mortality did not differ between HE and LE (1.78% versus 1.46%, p = 0.27). Regression analysis, adjusted for relevant covariates, demonstrated decreased COVID-19 infection rates by 12.82%, 12.01%, and 11.72% per 495m of county centroid elevation, for cases recorded over the previous 30, 90, and 120 days, respectively. Conclusions This population-adjusted, controlled analysis suggests that higher elevation attenuates infection and death. Ongoing work from our group aims to identify the environmental, biological, and social factors of residence at HE that impact infection, transmission, and pathogenesis of COVID-19 in an effort to harness these mechanisms for future public health and/or treatment interventions.

scholarly journals Analyzing China’s provincial pollution and its influencing factors: A spatial analysis

2021 ◽  
Author(s):  
Xing Wang ◽  
Dequn Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Dependence ◽  
Transmission Mechanism ◽  
Per Capita Gdp ◽  
Level Data ◽  
Per Capita ◽  
Global And Local ◽  
Province Level ◽  
Local Perspectives ◽  
Spatial Transmission

Abstract In-depth analyses of the spatial heterogeneity in pollution, and the causes of differences are of great importance for contributing to provide reference for reduction policies. However, a spatial analysis of the existence and mechanism of China’s pollution is still ignored. Using the province-level data of thirty provinces in China over 2005–2017, this paper constructs a spatial Durbin model (SDM) to empirically address the existence and spatial transmission mechanism of pollution. The main results are as follows: first, China’s pollution shows significant characteristics of spatial dependence and clustering from global and local perspectives, indicating that the existence of spatial autocorrelation in pollution across regions. Second, both per capita GDP and urbanization have positive impacts on pollution, but the impacts of environmental regulation and FDI are insignificant. Third, urbanization not only directly influences pollution, but also indirectly influences pollution. Our analysis provides valuable information for developing policies to effectively alleviate pollution.

scholarly journals Understanding China's Growth: Past, Present, and Future

2012 ◽  
Vol 26 (4) ◽  
pp. 103-124 ◽  
Author(s):  
Xiaodong Zhu
Keyword(s):  
Productivity Growth ◽  
Capital Investment ◽  
Average Rate ◽  
Economic Incentives ◽  
Economic Transformation ◽  
Living Standard ◽  
Per Capita Gdp ◽  
The World ◽  
Per Capita ◽  
The U.S

The pace and scale of China's economic transformation have no historical precedent. In 1978, China was one of the poorest countries in the world. The real per capita GDP in China was only one-fortieth of the U.S. level and one-tenth the Brazilian level. Since then, China's real per capita GDP has grown at an average rate exceeding 8 percent per year. As a result, China's real per capita GDP is now almost one-fifth the U.S. level and at the same level as Brazil. This rapid and sustained improvement in average living standard has occurred in a country with more than 20 percent of the world's population so that China is now the second-largest economy in the world. I will begin by discussing briefly China's historical growth performance from 1800 to 1950. I then present growth accounting results for the period from 1952 to 1978 and the period since 1978, decomposing the sources of growth into capital deepening, labor deepening, and productivity growth. But the main focus of this paper will be to examine the sources of growth since 1978, the year when China started economic reform. Perhaps surprisingly, given China's well-documented sky-high rates of saving and investment, I will argue that China's rapid growth over the last three decades has been driven by productivity growth rather than by capital investment. I also examine the contributions of sector-level productivity growth, and of resource reallocation across sectors and across firms within a sector, to aggregate productivity growth. Overall, gradual and persistent institutional change and policy reforms that have reduced distortions and improved economic incentives are the main reasons for the productivity growth.

scholarly journals To Spend or Not To Spend? An Empirical Analysis of the Fundamental Role of State Governments in Economic Output

2012 ◽  
Vol 19 ◽  
pp. 87
Author(s):  
Stephen Holt ◽  
Matt McCreary ◽  
Lindsay Haslebacher
Keyword(s):  
Political Party ◽  
Government Spending ◽  
Explanatory Power ◽  
State Level ◽  
State Governments ◽  
Per Capita Gdp ◽  
Economic Output ◽  
Party Control ◽  
Per Capita

Amidst an economic recession and a long period of high rates of unemployment, the appropriate role of government expenditures in creating economic growth has become a major feature in current political discourse at both the federal and state level. This article uses an endogenous growth model to examine the fundamental relationship of state-level government spending and per capita GDP. Specifically, the analysis uses state-level data covering a six-year period controlling for state workforce characteristics, distribution of industrial activities, and tax revenue sources to develop a working model of state economies. The analysis found that state government spending had a positive, statistically significant effect on per capita GDP. The marginal return in per capita GDP for an additional dollar per capita of public expenditure was found to be between $1.89 and $2.39. In addition, indicator variables for political party in power were added to examine correlations between political party control and economic outcomes. The political party in power had no significant effect on GDP. The positive, statistically significant correlation between GDP and public expenditures alongside political variables with no significant effect on GDP indicates specific policies implemented by state governments may have more explanatory power of economic output than political party control.

International differences in labor productivity: Role of capital, technological level and resource rent

2016 ◽  
pp. 67-93 ◽  
Author(s):  
A. Zaytsev
Keyword(s):  
Labor Productivity ◽  
Developed Countries ◽  
Resource Rent ◽  
Per Capita Gdp ◽  
Technological Level ◽  
Per Capita ◽  
Oil Sector ◽  
Population Ratio ◽  
Gdp Gap ◽  
Productivity Gap

Using level accounting methodology this article examines sources of per capita GDP and labor productivity differences between Russia and developed and developing countries. It considers the role played by the following determinants in per capita GDP gap: per hour labor productivity, number of hours worked per worker and labor-population ratio. It is shown that labor productivity difference is the main reason of Russia’s lagging behind. Factors of Russia’s low labor productivity are then estimated. It is found that 33-39% of 2.5-5-times labor productivity gap (estimated for non-oil sector) between Russia and developed countries (US, Canada, Germany, Norway) is explained by lower capital-to-labor ratio and the latter 58-65% of the gap is due to lower technological level (multifactor productivity). Human capital level in Russia is almost the same as in developed countries, so it explains only 2-4% of labor productivity gap.

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