scholarly journals Assessing location attractiveness for manufacturing automobiles

2017 ◽  
Vol 10 (5) ◽  
pp. 817
Author(s):  
Edward Hanawalt ◽  
William Rouse
Keyword(s):  
Rank Order ◽  
Secondary Analysis ◽  
Economic Incentives ◽  
Explanatory Variables ◽  
Country Level ◽  
Low Weight ◽  
Manufacturing Location ◽  
Wage Variation ◽  
Filter Layer ◽  
Initial List

Purpose: Evaluating country manufacturing location attractiveness on various performance measures deepens the analysis and provides a more informed basis for manufacturing site selection versus reliance on labor rates alone. A short list of countries can be used to drive regional considerations for site-specific selection within a country.Design/methodology/approach: The two-step multi attribute decision model contains an initial filter layer to require minimum values for low weighted attributes and provides a rank order utility score for twenty three countries studied. The model contains 11 key explanatory variables with Labor Rate, Material Cost, and Logistics making up the top 3 attributes and representing 54% percent of the model weights.Findings: We propose a multi attribute decision framework for strategically assessing the attractiveness of a country as a location for manufacturing automobiles.Research limitations/implications: Consideration of country level wage variation, specific tariffs, and other economic incentives provides a secondary analysis after the initial list of candidate countries is defined.Practical implications: The results of our modeling shows China, India, and Mexico are currently the top ranked countries for manufacturing attractiveness. These three markets hold the highest utility scores throughout sensitivity analysis on the labor rate attribute weight rating, highlighting the strength and potential of manufacturing in China, India, and Mexico.Originality/value: Combining MAUT with regression analysis to simplify model to core factors then using a “must have” layer to handle extreme impacts of low weight factors and allowing for ease of repeatability.

Languages and National Identity in Sub-Saharan Africa: A Multilevel Approach

2016 ◽  
Author(s):  
Katalin Buzási
Keyword(s):  
Positive Relationship ◽  
Multilevel Models ◽  
Sub Saharan Africa ◽  
Explanatory Variables ◽  
Country Level ◽  
National Identification ◽  
Positive Attitudes ◽  
Language Knowledge ◽  
French Colonies ◽  
Sub Saharan

This chapter contributes to the recent strand of the empirical political and economic literature that attempts to reveal the determinants of national identification in Sub-Saharan Africa. Although previous survey-based studies provide evidence that the socio-economic characteristics of individuals, the properties of ethnic groups they belong to, and certain country-level variables influence the probability of having positive attitudes toward the ethnic group or the nation, the role of languages has not been studied in this context yet. Inspired by findings of psycholinguistics and related disciplines, we utilize the fourth round of the Afrobarometer Project (surveyed in 2008 and 2009) to conduct analysis on the possible positive relationship between language knowledge and identification in national versus ethnic terms. We introduce two language-related explanatory variables. First, the Index of Communication Potential (ICP) reflects the probability that an individual can communicate with another randomly selected person within the society relying on commonly spoken languages. Second, we take into account the number of spoken languages in one’s repertoire. The multilevel models show that although speaking more than two languages increases the chance of identifying in national compared to ethnic terms, the ICP is not significant in this sense on the whole sample. But, when we consider the nationality of the former colonizers, the ICP exhibits positive relationship with national identification on the sub-sample of the former French colonies.

scholarly journals The Co-Development of Generativity and Well-Being into Early Late Life

2020 ◽  
Author(s):  
Erica Baranski ◽  
Jennifer Lodi-Smith ◽  
Elyse Ponterio ◽  
Nicky Newton ◽  
Michael Poulin ◽  
...  
Keyword(s):  
Rank Order ◽  
Secondary Analysis ◽  
Late Life ◽  
Well Being ◽  
Individual Variability ◽  
Individual Development ◽  
Future Research ◽  
Change Models ◽  
Demographic Covariates ◽  
Early Late

The current manuscript replicates and extends the few existing studies of generativity in later adulthood with regard to two aims: (1) to model individual differences in the development of generativity into early late life and (2) to quantify the relationship between development in generativity and development in well-being into late midlife and early older adulthood. Data from the Rochester Adult Longitudinal Study (RALS) are used to address these aims in a preregistered secondary analysis of existing RALS data (see https://osf.io/syp2u). Analyses quantify individual development of generativity in a sample of 284 RALS participants who completed the Loyola Generativity Scale (LGS; McAdams & de St. Aubin, 1992) and the Psychological Well-Being Scale (PWB; Ryff, 1989a) during the most recent two waves of the RALS (2000 – 2012). Both generativity and well-being demonstrated substantial rank-order stability and mean-level change as well as individual variability on both. Dual score change models showed a robust concurrent relationship between both constructs at the first assessment and meaningful correlated change between generativity and well-being over time. While demographic covariates were not associated with study findings, one of the most important limitations of the RALS is the racial and ethnic homogeneity of the sample that constrains generalizability to other racial and ethnic groups. Results are discussed in the context of our current understanding of the development and impact of generativity in later adulthood anddirections for future research in this area are identified.

scholarly journals Does governance matter? Country-level determinants of operational risk

2021 ◽  
Author(s):  
Edina Berlinger ◽  
Judit Lilla Keresztúri ◽  
Ágnes Lublóy ◽  
Zsuzsanna Tamásné Vőneki
Keyword(s):  
Explanatory Power ◽  
Extended Model ◽  
Technological Advancement ◽  
Explanatory Variables ◽  
The Past ◽  
Operational Risks ◽  
Country Level ◽  
Governance Indicators ◽  
Cross Country

Abstract The severity and frequency of operational loss events show high variability across the globe. In this paper, we first examine the extent to which the quality of country-level governance measured by the Worldwide Governance Indicators explains cross-country variation of operational losses. We use the comprehensive database of SAS OpRisk Global for the period of 2008–2019 covering 132 countries and 8,144 loss events with a total loss amount of almost 490 billion USD. Our findings indicate that the governance indicators lost their explanatory power over the past decades, which contradicts the academic consensus and calls for new explanatory variables. To find these variables, we hypothesize that the changes are driven by some important megatrends such as economic development and technological advancement, globalization, and sustainability. Accordingly, we propose an extended model where the number of mobile subscribers, the export to GDP ratio, and the poverty headcount ratio were significant for the frequency. For severity, only GDP is a significant and robust explanatory variable. Investors, regulators, and analysts should, therefore, concentrate on these factors if they wish to model, manage, or mitigate operational risks.

Forecasting global road traffic injury mortality for 2030

2019 ◽  
Vol 26 (4) ◽  
pp. 339-343 ◽  
Author(s):  
Haruhiko Inada ◽  
Qingfeng Li ◽  
Abdulgafoor Bachani ◽  
Adnan A Hyder
Keyword(s):  
Road Traffic ◽  
Secondary Analysis ◽  
Mortality Rates ◽  
Road Traffic Injury ◽  
Population Projection ◽  
Gdp Per Capita ◽  
Country Level ◽  
The World ◽  
Per Capita ◽  
Years Of Schooling

ObjectiveTo forecast the number and rate of deaths from road traffic injuries (RTI) in the world in 2030.MethodsThis study was a secondary analysis of annual country-level data of RTI mortality rates for 1990–2017 in the Global Burden of Disease (GBD) 2017 Study, population projection for 2030, gross domestic product (GDP) per capita for 1990–2030 and average years of schooling among people aged 15 years+ for 1990–2030. We developed up to 6884 combinations of forecasting models for each subgroup stratified by country, sex and mode of transport using linear and squared year, GDP per capita and average years of schooling as potential predictors. We conducted a fixed-size, rolling window out-of-sample forecast to choose the best combination for each subgroup. In the validation, we used the data for 1990–2002, 1991–2003 and 1992–2004 (fit periods) to forecast mortality rates in 2015, 2016 and 2017 (test periods), respectively. We applied the selected combination of models to the data for 1990–2017 to forecast the mortality rate in 2030 for each subgroup. To forecast the number of deaths, we multiplied the forecasted mortality rates by the corresponding population projection.ResultsDuring the test periods, the selected combination of models produced the number of deaths that is higher than that estimated in the GBD Study by 5.1% collectively. Our model resulted in 1.225 million deaths and 14.3 deaths per 100 000 population in 2030, which were 1% and 12% less than those for 2017 in the GBD Study, respectively.ConclusionsThe world needs to accelerate its efforts towards achieving the Decade of Action for Road Safety goal and the Sustainable Development Goals target.

scholarly journals A Hierarchical Multilevel Approach in Assessing Factors Explaining Country-Level Climate Change Vulnerability

2020 ◽  
Vol 12 (11) ◽  
pp. 4438
Author(s):  
George Halkos ◽  
Antonis Skouloudis ◽  
Chrisovalantis Malesios ◽  
Nikoleta Jones
Keyword(s):  
Climate Change ◽  
Developed Countries ◽  
Vulnerability Index ◽  
Explanatory Variables ◽  
Institutional Conditions ◽  
Climate Change Vulnerability ◽  
Country Level ◽  
Annual Time Series ◽  
Nuanced Understanding ◽  
Annual Time

Assessing vulnerability is key in the planning of climate change adaptation policies and, more importantly, in determining actions increasing resilience across different locations. This study presents the results of a hierarchical linear multilevel modeling approach that utilizes as dependent variable the Notre Dame Global Adaptation Initiative (ND-GAIN) Climate Change Vulnerability Index and explores the relative impact of a number of macro-level characteristics on vulnerability, including GDP, public debt, population, agricultural coverage and sociopolitical and institutional conditions. A 1995–2016 annual time series that yields a panel dataset of 192 countries is employed. Findings suggest that country-level climate change vulnerability is responding (strongly) to the majority of the explanatory variables considered. Findings also confirm that less-developed countries demonstrate increased vulnerability compared to the developed ones and those in transition stages. While these results indeed warrant further attention, they provide a background for a more nuanced understanding of aspects defining country-level patterns of climate vulnerability.

Why do Swedes use less cash-for-childcare than Norwegians?

2017 ◽  
Vol 37 (5/6) ◽  
pp. 327-340
Author(s):  
Julia Höppner
Keyword(s):  
Design Methodology ◽  
Robust Regression ◽  
Economic Incentives ◽  
Socioeconomic Background ◽  
Paid Work ◽  
Lower Socioeconomic Status ◽  
Content Type ◽  
Explanatory Variables ◽  
Attitudinal Differences ◽  
Lower Socioeconomic

Purpose The purpose of this paper is to explain the rather large difference in the take-up of the cash-for-childcare (CFC) benefit between Norway and Sweden. Design/methodology/approach A quantitative approach is employed, including the analysis of descriptive statistics of data on parents’ attitudes concerning the distribution of paid work and care and a robust regression analysis of data on parents’ behaviour regarding the distribution of paid work and care. Findings The results show that attitudes regarding childcare and mothers’ and fathers’ employment differ in the two countries. Swedish parents support public childcare and a gender equal employment distribution more than Norwegians. Thereby, attitudinal differences explain why Norwegian parents use the benefit more frequently. The findings indicate that in Sweden, parents’ socioeconomic background affects the duration of public childcare to a lesser extent than in Norway. Nevertheless, the economic incentives of the CFC benefit are more attractive for families with lower socioeconomic status. This explains why Swedes respond less to the incentives of the CFC benefit than Norwegians. Originality/value While previous research has focussed on the effect of policies on the take-up of the CFC benefit, this study shows that parents’ attitudes and behaviour are important explanatory variables to explain differences in the take-up of the benefit.

scholarly journals Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?

2021 ◽  
Author(s):  
Budour Alkaf ◽  
Alexandra I. Blakemore ◽  
Marjo-Riitta Järvelin ◽  
Nader Lessan
Keyword(s):  
Physical Activity ◽  
Risk Factor ◽  
Disease Risk ◽  
Communicable Disease ◽  
Secondary Analysis ◽  
Diabetes Risk ◽  
Differential Distribution ◽  
Country Level ◽  
The World ◽  
Different Populations

AbstractType 2 diabetes rates vary significantly across geographic regions. These differences are sometimes assumed to be entirely driven by differential distribution of environmental triggers, including obesity and insufficient physical activity (IPA). In this review, we discuss data which conflicts with this supposition. We carried out a secondary analysis of publicly available data to unravel the relative contribution of obesity and IPA towards diabetes risk across different populations. We used sex-specific, age-standardized estimates from Non-Communicable Disease Risk Factor Collaboration (NCD-RisC) on diabetes (1980–2014) and obesity (1975–2016) rates, in 200 countries, and from WHO on IPA rates in 168 countries in the year 2016. NCD-RisC and WHO organized countries into nine super-regions. All analyses were region- and sex-specific. Although obesity has been increasing since 1975 in every part of the world, this was not reflected in a proportional increase in diabetes rates in several regions, including Central and Eastern Europe, and High-income western countries region. Similarly, the association of physical inactivity with diabetes is not homogeneous across regions. Countries from different regions across the world could have very similar rates of diabetes, despite falling on opposite ends of IPA rate spectrum. The combined effect of obesity and IPA on diabetes risk was analyzed at the worldwide and country level. The overall findings highlighted the larger impact of obesity on disease risk; low IPA rates do not seem to be protective of diabetes, when obesity rates are high. Despite that, some countries deviate from this overall observation. Sex differences were observed across all our analyses. Overall, data presented in this review indicate that different populations, while experiencing similar environmental shifts, are apparently differentially subject to diabetes risk. Sex-related differences observed suggest that males and females are either subject to different risk factor exposures or have different responses to them.

scholarly journals Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256610
Author(s):  
Xingpei Yan ◽  
Zheng Zhu
Keyword(s):  
Traffic Safety ◽  
Traffic Congestion ◽  
Regression Models ◽  
Travel Demand ◽  
The Other ◽  
Explanatory Variables ◽  
Safety Issues ◽  
Country Level ◽  
The Impact ◽  
Cluster 2

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China’s e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

Worlds of Welfare Capitalism and Wellbeing: A Multilevel Analysis

2012 ◽  
Vol 41 (4) ◽  
pp. 811-829 ◽  
Author(s):  
CHRIS DEEMING ◽  
DAVID HAYES
Keyword(s):  
Multilevel Models ◽  
Welfare Regimes ◽  
Social Scientists ◽  
Welfare Capitalism ◽  
Ideal Types ◽  
Individual Level ◽  
Explanatory Variables ◽  
Country Level ◽  
Welfare Systems ◽  
Welfare Theory

AbstractSocial scientists in the comparative policy tradition have long argued that welfare systems in modern capitalist societies can be broken down into ideal types. The idea of different worlds of welfare capitalism has an enduring appeal and growing practical policy relevance as governments seek to enhance population wellbeing. In this paper, we explore the worlds of welfare theory from the perspective of happiness. Drawing on data from the World Values Survey, we examine how welfare regimes may contribute to wellbeing and we consider the significance of our findings for the development of social policy. By using multilevel models, it is possible to separate out effects due to observed and unobserved, as well as both individual-level and country-level, welfare state characteristics and we can make inferences to the distribution of social wellbeing across welfare typologies. We find that respondents living in liberal and conservative countries experience at least twice the odds of unhappiness of those living in social democracies, after controlling for individual- and country-level explanatory variables. The observed differences between the worlds of welfare were found to be highly statistically significant.

scholarly journals Statistical Analysis of Maximally Similar Sets in Ecological Research

Mathematics ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 317
Author(s):  
David Roberts
Keyword(s):  
Statistical Power ◽  
Rank Order ◽  
Dimensional Space ◽  
Random Sets ◽  
Forest Composition ◽  
Triangular Distribution ◽  
Large Set ◽  
Spring Temperature ◽  
Explanatory Variables ◽  
Size Number

Maximally similar sets (MSSs) are sets of elements that share a neighborhood in a high-dimensional space defined by a symmetric, reflexive similarity relation. Each element of the universe is employed as the kernel of a neighborhood of a given size (number of members), and elements are added to the neighborhood in order of similarity to the current members of the set until the desired neighborhood size is achieved. The set of neighborhoods is then reduced to the set of unique, maximally similar sets by eliminating all sets that are permutations of an existing set. Subsequently, the within-MSS variability of candidate explanatory variables associated with the elements is compared to random sets of the same size to estimate the probability of obtaining variability as low as was observed. Explanatory variables can be compared for effect size by the rank order of within-MSS variability and random set variability, correcting for statistical power as necessary. The analyses performed identify constraints, as opposed to determinants, in the triangular distribution of pair-wise element similarity. In the example given here, the variability in spring temperature, summer temperature, and the growing degree days of forest vegetation sample units shows the greatest constraint on forest composition of a large set of candidate environmental variables.

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