Multidimensional welfare in districts of Zambia: A first order dominance approach

In this paper we make welfare comparisons among districts of Zambia using multidimensional well-being indicators observed at the household level using the first order dominance approach developed by Arndt et al. in 2012. This approach allows welfare comparisons without making any assumptions about the relative importance of the indicators. Analysis of the 2010 Census of Population and Housing data has generated information on the poverty status of provinces and districts in Zambia and has ranked them from the relatively well-off to the worseoff. This information has been presented on a map showing the districts according to their poverty status. It is expected that this paper will contribute to fine-tuning geographic poverty targeting efforts in Zambia. The rationale is that with the availability of such analysis, it will be possible to make budgetary provisions that allow for the equitable distribution of public resources. The overriding objective of the government should be to channel public resources based on the spatial distribution of poverty

Estimating multidimensional childhood poverty in the Democratic Republic of Congo, 2007 through 2013

After years of economic decline, conflict, and instability, the Democratic Republic of Congo achieved rapid economic growth in the 2000s along with a reduction in rural consumption poverty. This paper evaluates the extent to which recent growth has been accompanied by improvements in multidimensional child welfare using a first-order dominance approach applied to Multiple Indicator Cluster Surveys and Demographic and Health Surveys. The authors explore the root of indeterminate outcomes in spatial analysis and evaluate the sensitivity of spatial and temporal outcomes to indicator choice. Though results do not indicate broad-based welfare advancement at the national, urban, or rural levels, they do suggest progress within a number of individual provinces

A High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber

© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.A new instrument, the High-speed Particle Phase Discriminator (PPD-HS), developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in situ analysis of the spatial intensity distribution of near-forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2-D scattering pattern to scattered light intensities captured onto two linear, one-dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles generated in a well-controlled laboratory setting using a vibrating orifice aerosol generator (VOAG) and covering a size range of approximately 3-32 μm. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5% for diameters >3μm. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in the case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes, independent of optical particle size. From our laboratory experiments we conclude that PPD-HS constitutes a powerful new instrument to size and discriminate the phase of cloud hydrometeors. The working principle of PPD-HS forms a basis for future instruments to study microphysical properties of atmospheric mixed-phase clouds that represent a major source of uncertainty in aerosol-indirect effect for future climate projections..Peer reviewe

Estimating multidimensional poverty in Zambia

In this paper, we apply the first-order dominance (FOD) approach to assessing multidimensional welfare to analyse multidimensional poverty in Zambia in 1996, 2006, and 2010. In addition to evaluating welfare across time and space, we extend the methodology to evaluate welfare by rural agricultural strata and urban housing cost areas. This modification allows a more detailed perspective on the evolution of rural poverty. Finally, we consider the sensitivity of FOD results to indicator definitions in a context where data prohibited the preferred definition

Poverty trends in Pakistan

The official estimates of poverty in Pakistan have shown a remarkable and consistent decline in the poverty headcount during the previous decade. This paper examines trends in poverty between 2001 and 2011 using the official food energy intake and the cost of basic needs approaches, both of which are modified to allow poverty lines to vary over time and space. The latter estimates provide utility-consistent poverty lines through the imposition of revealed preference conditions in maximum entropy adjustments. Evidence from both methods suggests that poverty incidence increased rather than declined as indicated in the official estimates

Multi-dimensional poverty analysis for Tanzania: First order dominance approach with discrete indicators

As in much of sub-Saharan Africa, Tanzania has attained rapid economic growth accompanied by only marginal reductions in poverty. Is this mismatch between high economic growth and less significant poverty reduction due to how growth and poverty are measured and reconciled, or more substantial underlying factors? Applying the first order dominance approach to multi-dimensional welfare comparisons, this paper seeks to gain a greater understanding of the evolution of poverty in Tanzania over time and space. Analysis of welfare indicators among four population groups in the regions, zones, and urban/rural areas of Tanzania reveal broad-based improvements in well-being between 1990 and 2010

Spatial and temporal analyses of women's wellbeing in the Democratic Republic of the Congo

This paper sets out to investigate the wellbeing of women in the Democratic Republic of the Congo (DRC). It undertakes spatial and temporal comparisons of women's wellbeing using data from the Demographic and Health Survey and the Multiple Indicator Cluster Survey. Using the multidimensional first-order dominance approach, the results reveal mixed evidence of improvement and deterioration of women's welfare across the DRC over a three-year period (2007 - 10)

Comparison of Heat and Moisture Fluxes from a Modified Soil-plant-atmosphere Model with Observations from BOREAS

This study evaluates the prediction of heat and moisture fluxes from a new land surface scheme with eddy correlation data collected at the old aspen site during the Boreal Ecosystem-Atmosphere Study (BOREAS) in 1994. The model used in this study couples a multilayer vegetation model with a soil model. Inclusion of organic material in the upper soil layer is required to adequately simulate exchange between the soil and subcanopy air. Comparisons between the model and observations are discussed to reveal model misrepresentation of some aspects of the diurnal variation of subcanopy processes. Evapotranspiratio

NASA Cold Land Processes Experiment (CLPX 2002/03): ground-based and near-surface meteorological observations

A short-term meteorological database has been developed for the Cold Land Processes Experiment (CLPX). This database includes meteorological observations from stations designed and deployed exclusively for CLPXas well as observations available from other sources located in the small regional study area (SRSA) in north-central Colorado. The measured weather parameters include air temperature, relative humidity, wind speed and direction, barometric pressure, short- and long-wave radiation, leaf wetness, snow depth, snow water content, snow and surface temperatures, volumetric soil-moisture content, soil temperature, precipitation, water vapor flux, carbon dioxide flux, and soil heat flux. The CLPX weather stations include 10 main meteorological towers, 1 tower within each of the nine intensive study areas (ISA) and one near the local scale observation site (LSOS); and 36 simplified towers, with one tower at each of the four corners of each of the nine ISAs, which measured a reduced set of parameters. An eddy covariance system within the North Park mesocell study area (MSA) collected a variety of additional parameters beyond the 10 standard CLPX tower components. Additional meteorological observations come from a variety of existing networks maintained by the U.S. Forest Service, U.S. Geological Survey, Natural Resource Conservation Service, and the Institute of Arctic and Alpine Research. Temporal coverage varies from station to station, but it is most concentrated during the 2002/ 03 winter season. These data are useful in local meteorological energy balance research and for model development and testing. These data can be accessed through the National Snow and Ice Data Center Web site