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Multidimensional poverty comparisons can be sensitive to the choice of welfare indicators, the weights assigned to the indicators, as well as the aggregate poverty measure used. This paper examines the robustness of trends in multidimensional poverty in the Philippines to these choices by presenting estimates for three alternative weighting schemes and three measures of multidimensional poverty. The weighting schemes range from uniform weights similar to those used in the global multidimensional poverty indexes produced by the United Nations Development Programme, to weights based on inverse incidence of different deprivations and those derived from the estimated relationship of deprivations to a survey-based measure of subjective welfare. The multidimensional poverty measures similarly range from the "dual cut-off" indexes analogous to the United Nations Development Programme's global Multidimensional Poverty Index, to "union-based" indexes that count all deprivations, to indexes that are also responsive to the distribution of deprivations. Using data for 2004-13, the paper finds evidence of a significant decline in multidimensional poverty that is robust to these alternatives, although the magnitude of the decline in, and especially the dimensional contributions to, aggregate multidimensional poverty are quite sensitive to the alternatives considered.

Identification And Aggregation --- Multidimensional Poverty --- Weighting

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survey methods --- data nonresponse --- face-to-face surveys --- cati --- weighting --- response rates

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Noise levels (sound pressure levels, SPLs) were monitored over 24 and 48 h in a number of different types of kennels including shelters, training establishments and research laboratories. Two measures of SPL were used, Lpeak and Leq, over both low (1 Hz-20 kHz) and high (12.5-70 kHz) frequency ranges and using a linear weighting. At most sites the noise levels followed a diurnal pattern; levels were generally low and relatively constant overnight, increased gradually in the early morning and then fluctuated during the working day. Levels decreased in the evening at different times depending on the local regimes. In one facility near railway lines the diurnal pattern was less obvious. During the day Lpeak values regularly exceeded 100 dB and often reached 125 dB; Leq values were between 65 and 100 dB.The high noise levels were caused mainly by barking, but husbandry procedures such as cleaning also contributed to them. The noise levels recorded here may have welfare implications. If this is shown to be the case, it is not yet clear what are the best methods of reducing the levels. There is currently a lack of adequate guide lines for noise levels in dog kennels. The current work has highlighted an area of concern in dog husbandry that urgently needs to be addressed

Area. --- Barking. --- Cleaning. --- Dog. --- Dogs. --- Frequency. --- Husbandry. --- Laboratory. --- Level. --- Method. --- Need. --- Needs. --- Noise. --- Pattern. --- Research. --- Shelter. --- Shelters. --- Sound-pressure. --- Sound. --- Time. --- Training. --- Weighting. --- Welfare. --- Work.

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The COVID-19 pandemic has created urgent demand for timely data, leading to a surge in mobile phone surveys for tracking the impacts of and responses to the pandemic. This paper assesses, and attempts to mitigate, selection biases in individual-level analyses based on phone survey data. The research uses data from (i) national phone surveys that have been implemented in Ethiopia, Malawi, Nigeria, and Uganda during the pandemic, and (ii) the pre-COVID-19 national face-to-face surveys that served as the sampling frames for the phone surveys. The availability of pre-COVID-19 face-to-face survey data permits comparisons of phone survey respondents with the general adult population. Phone survey respondents are more likely to be household heads or their spouses and non-farm enterprise owners, and on average, are older and better educated vis-a-vis the general adult population. To improve the representativeness of individual-level phone survey data, the household-level phone survey sampling weights are calibrated based on propensity score adjustments that are derived from a model of an individual's likelihood of being interviewed as a function of individual- and household-level attributes. Reweighting improves the representativeness of the estimates for the phone survey respondents, moving them closer to those of the general adult population. This holds for women and men and a range of demographic, education, and labor market outcomes. However, reweighting increases the variance of the estimates and fails to overcome selection biases. Obtaining reliable data on men and women through phone surveys requires random selection of adult interviewees within sampled households.

Coronavirus --- COVID-19 --- Disease Control and Prevention --- Education --- Gender --- Gender and Development --- Health, Nutrition and Population --- Household Survey --- Phone Survey --- Primary Education --- Statistical and Mathematical Sciences --- Survey Methodology --- Survey Sampling --- Weighting Methods

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I discuss some of the major conceptual and methodological problems that have arisen in our attempts to assess the relative levels of farm animal welfare in different housing systems. In some cases these problems arise because applied research has not kept pace with more fundamental research. First, the concepts of animal welfare used by researchers are often too limited and do not address many of the issues of concern to the public. This is particularly true for concepts of animal welfare that ignore the topic of suffering. Redefining animal welfare to make it more convenient for research risks making research irrelevant to the issues at hand. Concepts of animal welfare have not dealt adequately with the multidimensional nature of animal welfare: when housing systems are compared, different welfare indicators favour different housing systems and there is a trade-off between different challenges to animal welfare, but conceptual solutions to this problem are few. Furthermore, we have tended to rely too much on physiological, immune and behavioural measures of welfare that have not adequately been validated, and have not given sufficient weighting to health problems, which are some of the major threats to farm animal welfare. Second, we have focused too much upon the type of housing and have paid less attention to other important sources of variability in animal welfare, especially the quality of stockmanship, nutritional effects and the effects of breeding. Third, we have relied too much on a controlled, experimental approach and have not taken enough advantage of the power of epidemiological approaches to identify the main threats to welfare. Finally, we have tried to use physiological, immune and behavioural measures of welfare before we have adequately understood their underlying biological mechanisms. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved

Animal welfare. --- Animal-welfare. --- Animal. --- Attention. --- Behavior. --- Breeding. --- Dairy-cows. --- Epidemiological. --- Farm animal welfare. --- Feeding motivation. --- Food. --- Gilts. --- Health. --- Heifers. --- High-fiber diets. --- Housing system. --- Housing. --- Immune. --- Kept. --- Level. --- Mechanisms. --- Physiological. --- Pigs. --- Production systems. --- Quality. --- Research. --- Risk. --- Sows. --- Stereotypies. --- Stress. --- Suffering. --- Swine. --- System. --- Systems. --- Time. --- Variability. --- Weighting. --- Welfare indicators. --- Welfare.