Poverty and Population Density: Implications for Economic Development Policy
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Globalization and Infectious Diseases: a Review of the Linkages
TDR/STR/SEB/ST/04.2 SPECIAL TOPICS NO.3 Globalization and infectious diseases: A review of the linkages Social, Economic and Behavioural (SEB) Research UNICEF/UNDP/World Bank/WHO Special Programme for Research & Training in Tropical Diseases (TDR) The "Special Topics in Social, Economic and Behavioural (SEB) Research" series are peer-reviewed publications commissioned by the TDR Steering Committee for Social, Economic and Behavioural Research. For further information please contact: Dr Johannes Sommerfeld Manager Steering Committee for Social, Economic and Behavioural Research (SEB) UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR) World Health Organization 20, Avenue Appia CH-1211 Geneva 27 Switzerland E-mail: [email protected] TDR/STR/SEB/ST/04.2 Globalization and infectious diseases: A review of the linkages Lance Saker,1 MSc MRCP Kelley Lee,1 MPA, MA, D.Phil. Barbara Cannito,1 MSc Anna Gilmore,2 MBBS, DTM&H, MSc, MFPHM Diarmid Campbell-Lendrum,1 D.Phil. 1 Centre on Global Change and Health London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK 2 European Centre on Health of Societies in Transition (ECOHOST) London School of Hygiene & Tropical Medicine Keppel Street, London WC1E 7HT, UK TDR/STR/SEB/ST/04.2 Copyright © World Health Organization on behalf of the Special Programme for Research and Training in Tropical Diseases 2004 All rights reserved. The use of content from this health information product for all non-commercial education, training and information purposes is encouraged, including translation, quotation and reproduction, in any medium, but the content must not be changed and full acknowledgement of the source must be clearly stated. -
African Development Report 2015 Growth, Poverty and Inequality Nexus
African Development Report 2015 African Despite earlier periods of limited growth, African economies Sustaining recent growth successes while making future growth have grown substantially over the past decade. However, poverty more inclusive requires smart policies to diversify the sources African Development and inequality reduction has remained less responsive to growth of growth and to ensure broad-based participation across successes across the continent. How does growth affect poverty segments of society. Africa needs to adopt a new development and inequality? How can Africa overcome contemporary and trajectory that focuses on effective structural transformation. Report 2015 future sustainable development challenges? This 2015 edition Workers need to move from low productivity sectors to those of the African Development Report (ADR) offers analysis, where both productivity and earnings are higher. Key poverty- Growth, Poverty and Inequality Nexus: synthesis and recommendations that are relevant to these reducing sectors, such as agriculture and manufacturing, should Overcoming Barriers to Sustainable Development questions. The objective of this Report is to guide policy be targeted and accorded high priority for public and private Growth, Poverty Growth, Development and Inequality Sustainable to Nexus: Overcoming Barriers processes by contributing to the debate analysing what has investment. Adding value to many of Africa’s primary exports happened during recent years, what has worked well, what may earn the continent a competitive margin in international hasn’t worked well, and what needs to be done to address markets, while also meeting domestic market needs, especially further barriers to sustainable development in Africa? Africa’s with regard to food security. Apart from the need to prioritise recent economic growth has not been accompanied by a real certain sectors, other policy recommendations emanating from structural transformation. -
Litter Pollution in Densely Versus Sparsely Populated Areas: Dog River Watershed
LITTER POLLUTION IN DENSELY VERSUS SPARSELY POPULATED AREAS: DOG RIVER WATERSHED Gabrielle M. Hudson, Department of Earth Sciences, University of South Alabama, Mobile, AL 36688. E-mail: [email protected]. It is commonly known that when humans populate an area that area is usually subject to some environmental degradation. One of the more common aspects of environmental degradation is litter. This type of degradation is no stranger to the Dog River watershed. For years residents have seen the rivers in this watershed covered in trash, specifically after rain events. The vast majority of the trash is a result of litter from roadsides being carried into the river via drainage pipes. This paper is a comparative study of litter in areas of varying population densities in the Dog River watershed. It seeks to distinguish between the amount of litter found in densely populated areas and sparsely populated areas, and to find out if there is a correlation between population density and litter. I utilize GIS to map population density of the Dog River watershed, and analyze and compare the amounts of litter in areas of sparse and dense populations. The results show that there is no correlation between population density and litter. It also shows that there is no difference in the amounts of litter found in densely and sparsely populated areas. Keyword: litter, population density, watershed Introduction Pollution has long been an issue in the Dog River watershed, in particular litter pollution. The extent of the pollution has not gone unnoticed. There are groups of people and organizations who have taken increased interest in the Dog River watershed with intentions of reducing pollution, including Dog River Clearwater Revival and its numerous volunteers. -
Income and Poverty in the United States: 2018 Current Population Reports
Income and Poverty in the United States: 2018 Current Population Reports By Jessica Semega, Melissa Kollar, John Creamer, and Abinash Mohanty Issued September 2019 Revised June 2020 P60-266(RV) Jessica Semega and Melissa Kollar prepared the income section of this report Acknowledgments under the direction of Jonathan L. Rothbaum, Chief of the Income Statistics Branch. John Creamer and Abinash Mohanty prepared the poverty section under the direction of Ashley N. Edwards, Chief of the Poverty Statistics Branch. Trudi J. Renwick, Assistant Division Chief for Economic Characteristics in the Social, Economic, and Housing Statistics Division, provided overall direction. Vonda Ashton, David Watt, Susan S. Gajewski, Mallory Bane, and Nancy Hunter, of the Demographic Surveys Division, and Lisa P. Cheok of the Associate Directorate for Demographic Programs, processed the Current Population Survey 2019 Annual Social and Economic Supplement file. Andy Chen, Kirk E. Davis, Raymond E. Dowdy, Lan N. Huynh, Chandararith R. Phe, and Adam W. Reilly programmed and produced the historical, detailed, and publication tables under the direction of Hung X. Pham, Chief of the Tabulation and Applications Branch, Demographic Surveys Division. Nghiep Huynh and Alfred G. Meier, under the supervision of KeTrena Phipps and David V. Hornick, all of the Demographic Statistical Methods Division, conducted statistical review. Lisa P. Cheok of the Associate Directorate for Demographic Programs, provided overall direction for the survey implementation. Roberto Cases and Aaron Cantu of the Associate Directorate for Demographic Programs, and Charlie Carter and Agatha Jung of the Information Technology Directorate prepared and pro- grammed the computer-assisted interviewing instrument used to conduct the Annual Social and Economic Supplement. -
Final Report (Housing and Health).Pdf
HEALTH EQUITY DATA ANALYSIS The links between health and housing inequities are Housing and Health was prepared by the City of Bloomington, Division of Public Health to fulfill the requirements of the Statewide Healthundeniable, Improvement and the Partnership challenges faced by households HOUSING struggling to afford housing in opportunity-rich Authors: Joan Bulfer, M.S., R.N. LN, Health Promotion Specialist, Bloomington Public Health Margaret Perez, Health Promotion Specialist,communities Bloomington like Public Richfield Health undermine the collective Sophia Long, graduate student, University of Minnesota,health and School prosperity of Public of the Health community. Households of color are disproportionately impacted. When some AND community members cannot access safe, affordable housing, their physical and mental health may be negatively affected. In turn, this can affect success in school, workplace and community. Policy decisions HEALTH made by the City that affect housing can go a long way toward making Richfield a healthier, more vibrant, and ultimately more equitable community. The Effect of Housing on the Health of Low-income Renters in City of Bloomington Richfield, Minnesota Division of Public Health April 30, 2018May 2018 This project was made possible through funding from the Statewide Health Improvement Partnership, Minnesota Department of Health, 2018 Table of Contents Table of Contents ..................................................................................................................................... -
Exploring the Use of Residual Measures of Housing Affordability In
AHURI Essay Exploring the use of residual measures of housing affordability in Australia: methodologies and concepts authored by Paul Henman and Andrew Jones for the Australian Housing and Urban Research Institute Queensland Research Centre January 2012 AHURI Final Report No. 180 ISSN: 1834-7223 ISBN: 978-1-921610-91-2 Authors Henman, Paul University of Queensland Jones, Andrew University of Queensland Title Exploring the use of residual measures of housing affordability in Australia: methodologies and concepts ISBN 978-1-921610-91-2 Format PDF Key words residual measures, housing affordability, Australia, methodologies, concepts, housing Editor Anne Badenhorst AHURI National Office Publisher Australian Housing and Urban Research Institute Melbourne, Australia Series AHURI Final Report; no.180. ISSN 1834-7223 Preferred citation Henman P. and Jones A. (2012) Exploring the use of residual measures of housing affordability in Australia: methodologies and concepts, AHURI Final Report No.180. Melbourne: Australian Housing and Urban Research Institute. i ACKNOWLEDGEMENTS This material was produced with funding from the Australian Government and the Australian states and territory governments. AHURI Limited gratefully acknowledges the financial and other support it has received from these governments, without which this work would not have been possible. AHURI comprises a network of universities clustered into Research Centres across Australia. Research Centre contributions, both financial and in-kind, have made the completion of this report possible. DISCLAIMER AHURI Limited is an independent, non-political body which has supported this project as part of its programme of research into housing and urban development, which it hopes will be of value to policy-makers, researchers, industry and communities. -
Poverty and Inequality Prof. Dr. Awudu Abdulai Department of Food Economics and Consumption Studies
Poverty and Inequality Prof. Dr. Awudu Abdulai Department of Food Economics and Consumption studies Poverty and Inequality Poverty is the inability to achieve a minimum standard of living Inequality refers to the unequal distribution of material or immaterial resources in a society and as a result, different opportunities to participate in the society Poverty is not only a question of the absolute income, but also the relative income. For example: Although people in Germany earn higher incomes than those in Burkina Faso, there are still poor people in Germany and non-poor people in Burkina Faso -> Different places apply different standards -> The poor are socially disadvantaged compared to other members of a society in which they belong Measuring Poverty How to measure the standard of living? What is a "minimum standard of living"? How can poverty be expressed in an index? Ahead of the measurement of poverty there is the identification of poor households: ◦ Households are classified as poor or non-poor, depending on whether the household income is below a given poverty line or not. ◦ Poverty lines are cut-off points separating the poor from the non- poor. ◦ They can be monetary (e.g. a certain level of consumption) or non- monetary (e.g. a certain level of literacy). ◦ The use of multiple lines can help in distinguishing different levels of poverty. Determining the poverty line Determining the poverty line is usually done by finding the total cost of all the essential resources that an average human adult consumes in a year. The largest component of these expenses is typically the rent required to live in an apartment. -
Income and Poverty in the United States: 2017 Issued September 2018
IncoIncomeme andand P Povertyoverty in in the theUn Unitedited State States:s: 2017 2017 CurrentCurrent Population Reports ByBy KaylaKayla Fontenot,Fontenot, JessicaJessica Semega,Semega, andand MelissaMelissa KollarKollar IssuedIssued SeptemberSeptember 20182018 P60-263P60-263 Jessica Semega and Melissa Kollar prepared the income sections of this report Acknowledgments under the direction of Jonathan L. Rothbaum, Chief of the Income Statistics Branch. Kayla Fontenot prepared the poverty section under the direction of Ashley N. Edwards, Chief of the Poverty Statistics Branch. Trudi J. Renwick, Assistant Division Chief for Economic Characteristics in the Social, Economic, and Housing Statistics Division, provided overall direction. Susan S. Gajewski and Nancy Hunter, Demographic Surveys Division, and Lisa P. Cheok, Associate Directorate Demographic Programs, processed the Current Population Survey 2018 Annual Social and Economic Supplement file. Kirk E. Davis, Raymond E. Dowdy, Shawna Evers, Ryan C. Fung, Lan N. Huynh, and Chandararith R. Phe programmed and produced the historical, detailed, and publication tables under the direction of Hung X. Pham, Chief of the Tabulation and Applications Branch, Demographic Surveys Division. Nghiep Huynh and Alfred G. Meier, under the supervision of KeTrena Farnham and David V. Hornick, all of the Demographic Statistical Methods Division, con- ducted statistical review. Tim J. Marshall, Assistant Survey Director of the Current Population Survey, provided overall direction for the survey implementation. Lisa P. Cheok and Aaron Cantu, Associate Directorate Demographic Programs, and Charlie Carter, Agatha Jung, and Johanna Rupp of the Application Development and Services Division prepared and programmed the computer-assisted interviewing instru- ment used to conduct the Annual Social and Economic Supplement. Additional people within the U.S. -
A Non-Linear Threshold Estimation of Density Effects
sustainability Article Population Dynamics and Agglomeration Factors: A Non-Linear Threshold Estimation of Density Effects Mariateresa Ciommi 1, Gianluca Egidi 2, Rosanna Salvia 3 , Sirio Cividino 4, Kostas Rontos 5 and Luca Salvati 6,7,* 1 Department of Economic and Social Science, Polytechnic University of Marche, Piazza Martelli 8, I-60121 Ancona, Italy; [email protected] 2 Department of Agricultural and Forestry Sciences (DAFNE), Tuscia University, Via San Camillo de Lellis, I-01100 Viterbo, Italy; [email protected] 3 Department of Mathematics, Computer Science and Economics, University of Basilicata, Viale dell’Ateneo Lucano, I-85100 Potenza, Italy; [email protected] 4 Department of Agriculture, University of Udine, Via del Cotonificio 114, I-33100 Udine, Italy; [email protected] 5 Department of Sociology, School of Social Sciences, University of the Aegean, EL-81100 Mytilene, Greece; [email protected] 6 Department of Economics and Law, University of Macerata, Via Armaroli 43, I-62100 Macerata, Italy 7 Global Change Research Institute of the Czech Academy of Sciences, Lipová 9, CZ-37005 Ceskˇ é Budˇejovice, Czech Republic * Correspondence: [email protected] or [email protected] Received: 14 February 2020; Accepted: 11 March 2020; Published: 13 March 2020 Abstract: Although Southern Europe is relatively homogeneous in terms of settlement characteristics and urban dynamics, spatial heterogeneity in its population distribution is still high, and differences across regions outline specific demographic patterns that require in-depth investigation. In such contexts, density-dependent mechanisms of population growth are a key factor regulating socio-demographic dynamics at various spatial levels. Results of a spatio-temporal analysis of the distribution of the resident population in Greece contributes to identifying latent (density-dependent) processes of metropolitan growth over a sufficiently long time interval (1961-2011). -
Global Typology of Urban Energy Use and Potentials for An
Global typology of urban energy use and potentials for SPECIAL FEATURE an urbanization mitigation wedge Felix Creutziga,b,1, Giovanni Baiocchic, Robert Bierkandtd, Peter-Paul Pichlerd, and Karen C. Setoe aMercator Research Institute on Global Commons and Climate Change, 10829 Berlin, Germany; bTechnische Universtität Berlin, 10623 Berlin, Germany; cDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742; dPotsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; and eYale School of Forestry & Environmental Studies, New Haven, CT 06511 Edited by Sangwon Suh, University of California, Santa Barbara, CA, and accepted by the Editorial Board December 4, 2014 (received for review August 17, 2013) The aggregate potential for urban mitigation of global climate (9, 10), and also with GHG emissions from the residential change is insufficiently understood. Our analysis, using a dataset sector (11). The recent IPCC report identifies urban form of 274 cities representing all city sizes and regions worldwide, as a driver of urban emissions but does not provide guidance on demonstrates that economic activity, transport costs, geographic its relative importance vis-à-vis other factors. In comparative factors, and urban form explain 37% of urban direct energy use studies, cities are often sampled from similar geographies (5, 12, and 88% of urban transport energy use. If current trends in urban 13) or population sizes (8). In these studies, causality is difficult expansion continue, urban energy use will increase more than to establish, and self-selection (14) as well as topological prop- threefold, from 240 EJ in 2005 to 730 EJ in 2050. Our model shows erties and specific urban form characteristics (15) partially ex- that urban planning and transport policies can limit the future plain the relationship between urban population density and increase in urban energy use to 540 EJ in 2050 and contribute to transport energy consumption. -
Framing the Poor: Media Coverage and US Poverty Policy, 1960-2008
bs_bs_banner The Policy Studies Journal, Vol. 41, No. 1, 2013 Framing the Poor: Media Coverage and U.S. Poverty Policy, 1960–2008 Max Rose and Frank R. Baumgartner Public policy toward the poor has shifted from an initial optimism during the War on Poverty to an ever-increasing pessimism. Media discussion of poverty has shifted from arguments that focus on the structural causes of poverty or the social costs of having large numbers of poor to portrayals of the poor as cheaters and chiselers and of welfare programs doing more harm than good. As the frames have shifted, policies have followed. We demonstrate these trends with new indicators of the depth of poverty, the generosity of the government response, and media framing of the poor for the period of 1960–2008. We present a simple statistical model that explains poverty spending by the severity of the problem, gross domestic product, and media coverage. We then create a new measure of the relative generosity of U.S. government policy toward the poor and show that it is highly related to the content of newspaper stories. The portrayal of the poor as either deserving or lazy drives public policy. KEY WORDS: policy change, framing, poverty policy, content analysis Framing the Poor In 1960, 22 percent of the American public, some 40 million people, earned an income below the poverty line.1 Fifteen years later, the rate had been reduced to 12 percent as spending on poverty assistance increased from 3 to about 8 percent of U.S. government spending.2 The War on Poverty had a dramatic impact. -
Handout: Ecological Footprints from Around the World
Ecological Footprints From Around the World Handout Map Your Eco-Footprint lesson plan support material Green Star! Newsletter May/June 2006 Handout: Ecological Footprints From Around the World (Adapted from: “How big is your footprint,” Energy for a Sustainable Future — Education Project, www.esfep.org/ ) Ecological Footprints From Around the World: Where Do You Fit In? How Much Land Do You Need to Live? If you had to provide everything you use from your own land — how much land area would you need? This land would have to provide you with all of your food, water, energy and everything else that you use. The amount of land you would need to support your lifestyle is called your Ecological Footprint. The ecological footprint is one way of measuring the impact a person has on the environment. Is the World Big Enough for All of Our BIG Feet? The size of a person’s Ecological Footprint will depend on many factors. Do you grow your own food? Do you walk or drive? Do you use renewable or non-renewable energy sources? Everyone has an ecological footprint because we all need to use the earth’s resources to survive. But we must make sure we don’t take more resources than the earth can provide. Different people in the same country will have different sized ecological footprints. Different countries also have different ecological footprints. For example, a person with the average Canadian lifestyle has an ecological footprint of 8.56 hectares. A person living in Ethiopia, Africa, has an average ecological footprint of 0.67 hectares.