scholarly journals Multiple Systems Estimation for Modern Slavery: Robustness of List Omission and Combination

2020 ◽  
pp. 001112872095142
Author(s):  
Serveh Sharifi Far ◽  
Ruth King ◽  
Sheila Bird ◽  
Antony Overstall ◽  
Hannah Worthington ◽  
...  
Keyword(s):  
Linear Model ◽  
Vulnerable Populations ◽  
Simulation Study ◽  
Total Population ◽  
Multiple Systems ◽  
Modern Slavery ◽  
Partial Enumeration ◽  
The Uk ◽  
Log Linear ◽  
The Impact

Performing censuses on stigmatized or vulnerable populations is challenging, however, for such populations partial enumeration is often possible using different lists or sources. If the sources overlap then multiple systems estimation (MSE) methods can be applied to obtain an estimate of the total population. These are typically expressed by a log-linear model which permits positive/negative dependencies between lists. This paper considers issues that arise for the application of MSE to modern slavery where there is little to no overlap of individuals across lists. We investigate the robustness of MSE in terms of the importance of each list and the impact of combining lists on the estimation process. We undertake a simulation study and consider real national modern slavery data from the UK and Romania.

Ageing Populations

2019 ◽  
pp. 35-37
Author(s):  
Andrew Smithers
Keyword(s):  
Growth Rates ◽  
Total Population ◽  
Living Standards ◽  
Favourable Change ◽  
Working Age ◽  
The Uk ◽  
The Impact ◽  
Ageing Populations ◽  
Trend Growth

The changes in demography, together with low investment and poor productivity, have been responsible for the whole of the decline in the trend growth rates of the UK and US economies. Living standards measured by GDP per person are given a boost when the population of working age grows faster than the total population. This favourable change in demography was the situation up to 2008. Until then living standards tended to improve faster than productivity. Since then the total population has been growing faster than the numbers of working age and living standards will now tend to grow less rapidly than productivity. The impact on prosperity has been sharp because we have moved from a favourable to an unfavourable situation.

An Empirical Comparison between Societal Expenditure and Marketing Expenditure on Bank’s Performance

2017 ◽  
Vol 18 (2) ◽  
pp. 509-523 ◽  
Author(s):  
Kaushik Mandal ◽  
Sujata Banerjee
Keyword(s):  
Linear Model ◽  
Performance Status ◽  
Social Concern ◽  
Empirical Comparison ◽  
Market Shares ◽  
Profit Performance ◽  
Social Cause ◽  
Indian Banks ◽  
Log Linear ◽  
The Impact

Marketing expenditure plays a crucial role in determining performance since promotion mix generates market shares and revenues for the brands. But, nowadays, the impact of promotional expenditure is appeared to be non-responsive to influence the revenue and profitability as the consumers are now having ample scopes of escaping advertisement. Moreover consumers prefer the products that are associated with some social cause. Hence the purpose of this paper is to compare the impact of societal and marketing expenditure on profitability. To attain the purpose, we have employed empirical results of various Indian banks by developing a model using profit after tax (PAT), societal and marketing expenses. In particular, we have considered log-linear model as it fits better for all the banks when compared with the linear model. Further, we have compared the profit elasticity between societal and marketing expenses. Finally, association between the profit performance status and the comparative profit contribution at equal expenditure has been tested by employing non-parametric χ² test and Cramer’s V. Result proves the efficiency of ‘expenditure in social concern’ compared to ‘Expenditure in traditional marketing tools’ and hence it suggests for adopting alternative route, that is, societal means of promotion for better customer connect.

scholarly journals The Impact of Multifactorial Genetic Disorders on Critical Illness Insurance: A Simulation Study Based on UK Biobank

2006 ◽  
Vol 36 (02) ◽  
pp. 311-346 ◽  
Author(s):  
Angus Macdonald ◽  
Delme Pritchard ◽  
Pradip Tapadar
Keyword(s):  
Critical Illness ◽  
Simulation Study ◽  
Large Scale ◽  
Genetic Disorders ◽  
Single Gene ◽  
Uk Biobank ◽  
Odds Ratios ◽  
Case Control Studies ◽  
The Uk ◽  
The Impact

The UK Biobank project is a proposed large-scale investigation of the combined effects of genotype and environmental exposures on the risk of common diseases. It is intended to recruit 500,000 subjects aged 40-69, to obtain medical histories and blood samples at outset, and to follow them up for at least 10 years. This will have a major impact on our knowledge of multifactorial genetic disorders, rather than the rare but severe single-gene disorders that have been studied to date. What use may insurance companies make of this knowledge, particularly if genetic tests can identify persons at different risk? We describe here a simulation study of the UK Biobank project. We specify a simple hypothetical model of genetic and environmental influences on the risk of heart attack. A single simulation of UK Biobank consists of 500,000 life histories over 10 years; we suppose that case-control studies are carried out to estimate age-specific odds ratios, and that an actuary uses these odds ratios to parameterise a model of critical illness insurance. From a large number of such simulations we obtain sampling distributions of premium rates in different strata defined by genotype and environmental exposure. We conclude that the ability of such a study reliably to discriminate between different underwriting classes is limited, and depends on large numbers of cases being analysed.

scholarly journals Fast initial Covid-19 response means greater caution may be needed later

2020 ◽  
Author(s):  
Joël J.-M. Hirschi
Keyword(s):  
Numerical Model ◽  
Effective Treatment ◽  
Total Population ◽  
Herd Immunity ◽  
Major Influence ◽  
Small Reduction ◽  
The Uk ◽  
The Impact ◽  
Reproduction Factor ◽  
Simple Numerical Model

ABSTRACTBackgroundAs the Covid-19 pandemic unfolds it is becoming increasingly clear that the strength of the first wave of the epidemic varies significantly between countries. In this study a simple numerical model is used to illustrate the impact the timing of initial measures against Covid-19 has on the first wave of infection and possible implications this may have for the measures taken as the first wave is ebbing. The results highlight that delaying measures by 10 days is sufficient to largely account for the differences seen between countries such as the UK and Germany for the first wave of infections. A pronounced first wave means that a larger fraction of the total population will have been infected and is therefore likely to display immunity. Even if this fraction is far below the level needed for “herd immunity” the effective reproduction factor Re is decreased compared to a population that had no prior exposure to the virus. Even a small reduction in Re can have major influence on the evolution of the epidemic after the first wave of infections. A large first wave means the resulting value for Re will be lower than if the first wave was mild. Without either vaccine or effective treatment countries that experienced a small first wave should therefore relax measures at a slower pace than countries where the first wave was strong.

An analysis of growth trend, instability and determinants of rice production in Assam

2017 ◽  
Vol 51 (04) ◽  
Author(s):  
Annesha Mech
Keyword(s):  
Linear Model ◽  
Negative Impact ◽  
Rice Production ◽  
Rice Cultivation ◽  
Growth Trend ◽  
Function Coefficient ◽  
Rice Area ◽  
Yearly Average ◽  
Log Linear ◽  
The Impact

Rice is one of the most dominant crop in Assam occupying about 70 per cent of the net cultivated area. It accounts for about 6 per cent of the national rice area and 4 per cent of production of India. This paper makes an attempt to examine the growth trend, instability and factors influencing rice production in Assam over the period 1972-73 to 2014-15. Annual Compound Growth Rates of area, production and yield is calculated using log-linear function. Coefficient of Variation is employed to assess the instability of rice production in Assam. To estimate the impact of various factors on rice production in Assam, three types of models are used namely linear model, log-linear model and a log-linear model with autocorrelation corrections. In all the models, the dependent variable is rice yield and the independent variables are yearly average rainfall, yearly average temperature of the state; area of rice cultivation in hectare; area covered by HYV seeds in hectare; area covered by irrigation in hectare; fertilizer used in kg per hectare. The estimated result of log-linear model with autocorrelation corrections shows that among the various determinants influencing rice production during the period 1972 to 2014, area under rice cultivation in hectares; area covered by HYV seeds in hectare; fertilizer used in kg per hectare were found to have a positive significant impact on rice production in Assam. Temperature is found to have a negative impact on rice production.

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