scholarly journals Socio-economic Development and Anti-government Protests in Light of a New Quantitative Analysis of Global Databases

2020 ◽  
Vol 26 (4) ◽  
pp. 61-78
Author(s):  
Andrey Korotayev ◽  
Patrick Sawyer ◽  
Leonid Grinin ◽  
Daniil Romanov ◽  
Alisa Shishkina
Keyword(s):  
Formal Education ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
High Income ◽  
Gdp Per Capita ◽  
Negative Binomial Regression Model ◽  
Strong Positive Correlation ◽  
Per Capita Gdp ◽  
Positive Correlation ◽  
Per Capita

Previous studies have revealed a somewhat paradoxical strong positive correlation between per capita GDP and the intensity of anti-government demonstrations observed for the vast majority of countries (indeed, it turns out that the better people live, the more likely they are to join anti-government protests). The goal of this article is to identify possible causes of this unusual correlation. Our tests show that the processes of democratization and urbanization, as well as the expansion of formal education, are likely to be the main factors determining the positive relationship between per capita GDP and the intensity of antigovernment demonstrations, as urbanization, democratization, and expansion of education lead to an increase in the intensity of protests. Moreover, when controlling for these factors, the relationship between per capita GDP and anti-government protests becomes negative. Thus, high per capita GDP turns out to be a direct (proximate) significant negative factor affecting the intensity of anti-government demonstrations, but at thesame time it is an ultimate, even more significant positive factor in the intensity of protests. The growth of per capita GDP is quite naturally accompanied by an increase in the level of urbanization, democratization and education, which more than compensates for the direct inhibiting effect on the protests on the part of the growing per capita GDP (at least for low- and middle-income countries). In addition, the negative binomial regression model that we propose can explain not only the strong positive correlation between per capita GDP and the intensity of protests, which can be traced for a range of GDP per capita values of less than $20,000, but also the weaker negative correlation recorded for the range exceeding $20,000. The fact is that in rich countries urbanization, democratization and education indicators reach saturation levels and the vast majority of high-income countries have more or less similar levels for all three indicators. As a result, for a zone of per capita GDP values of more than $20,000, we are essentially dealing with automatic control of the correlation between GDP per capita and the intensity of protests for factors of democratization, education and urbanization, and, as our model predicts, the final effect of GDP per capita on the intensity of protests for high-income countries becomes negative, not positive.

scholarly journals INSTITUTIONAL FACTORS OF ECONOMIC PERFORMANCE: EVIDENCE FROM THE WORLD VALUE SURVEY

2021 ◽  
Vol 7 (2) ◽  
pp. 146-160
Author(s):  
Andriy Maksymuk ◽  
Nataliya Kuzenko
Keyword(s):  
Regression Analysis ◽  
Oecd Countries ◽  
High Income ◽  
Institutional Factors ◽  
Gdp Per Capita ◽  
Strong Positive Correlation ◽  
Middle Income ◽  
The World ◽  
Per Capita ◽  
The Impact

This article highlights the impact of values on the country’s welfare. Values that are quite constant over a long period of time form an institutional framework within the country. They can contribute to economic development or even prevent it. The aim of the article is to explore, what is the influence of social values, democracy and trade on welfare levels in different counties. The hypothesis is that the dominance in society of secular-rational values and the values of self-expression, democracy and trade (openness to the world) have a positive effect on the level of welfare of countries. The empirical part of the paper is based on the comparative analysis of relationship between GDP per capita and four values such as tolerance and respect, obedience, trust and freedom of choice for two waves of WVS – 2005-2009 and 2010-2014. Using correlation and regression analysis, the relationships between these indicators were evaluated. These values have a positive impact on welfare in OECD countries, some countries of Latin America, Asia and Africa with middle income per capita. However, there is a negative relationship between obedience and GDP per capita. This value is more important for some African and Asian countries and India. The relationship between GDP per capita and the aggregate value index showed a strong positive correlation for OECD countries. Then the regression model was estimated to assess the impact of values, trade and level of democracy on welfare growth and development. The results of the regression analysis showed a significant effect of the aggregated value indicator for all six samples, but this effect is weaker for high-income countries. The effect of the level of democracy is significant and positive only for the sub-sample of democratic countries, while it is negative for high-income countries. The effect of the level of trade on GDP per capita is statistically significant for the sample of all countries, the sub-sample of non-democratic countries and the sub-sample of high income and upper-middle income countries. Thus, we conclude that the institutional factors (the values and the level of democracy) are important determinants of GDP per capita for democratic countries while for non-democratic countries trade is more important.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

Bike Count Forecast Model with Multimodal Network Connectivity Measures

2021 ◽  
pp. 036119812110218
Author(s):  
Bingqing Liu ◽  
Divya Bade ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
Negative Binomial ◽  
Network Connectivity ◽  
Negative Binomial Regression ◽  
Forecast Model ◽  
Negative Binomial Regression Model ◽  
Significance Level ◽  
Commute Time ◽  
Improve Model ◽  
Subway Stations

With the rise of cycling as a mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure for the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations, and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with regard to bike-friendliness using the New York City transit trip planner and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.

A Negative Binomial Regression Model for Accuracy Tests

2012 ◽  
Vol 36 (2) ◽  
pp. 88-103 ◽  
Author(s):  
Lai-Fa Hung
Keyword(s):  
Negative Binomial ◽  
Social Science Research ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Science Research ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Negative Binomial Regression Model ◽  
Marginal Maximum Likelihood ◽  
The Mean

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

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