19 datasets found
  1. South Africa CCI: Lower Middle Income (LM)

    • ceicdata.com
    Updated Jun 15, 2018
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    CEICdata.com (2018). South Africa CCI: Lower Middle Income (LM) [Dataset]. https://www.ceicdata.com/en/south-africa/consumer-survey/cci-lower-middle-income-lm
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    Dataset updated
    Jun 15, 2018
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 1, 2015 - Jun 1, 2018
    Area covered
    South Africa
    Variables measured
    Consumer Survey
    Description

    South Africa CCI: Lower Middle Income (LM) data was reported at 12.333 % in Jun 2018. This records a decrease from the previous number of 24.667 % for Mar 2018. South Africa CCI: Lower Middle Income (LM) data is updated quarterly, averaging 7.000 % from Mar 1995 (Median) to Jun 2018, with 94 observations. The data reached an all-time high of 28.000 % in Dec 1996 and a record low of -17.400 % in Jun 2015. South Africa CCI: Lower Middle Income (LM) data remains active status in CEIC and is reported by Bureau for Economic Research. The data is categorized under Global Database’s South Africa – Table ZA.H006: Consumer Survey.

  2. Income per capita in Africa 2023, by country

    • statista.com
    Updated Sep 30, 2024
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    Statista (2024). Income per capita in Africa 2023, by country [Dataset]. https://www.statista.com/statistics/1290903/gross-national-income-per-capita-in-africa-by-country/
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    Dataset updated
    Sep 30, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Africa
    Description

    Seychelles recorded the highest Gross National Income (GNI) per capita in Africa as of 2023, at 16,940 U.S. dollars. The African island was, therefore, the only high-income country on the continent, according to the source's classification. Mauritius, Gabon, Botswana, Libya, South Africa, Equatorial Guinea, Algeria, and Namibia were defined as upper-middle-income economies, those with a GNI per capita between 4,516 U.S. dollars and 14,005 U.S. dollars. On the opposite, 20 African countries recorded a GNI per capita below 1,145 U.S. dollars, being thus classified as low-income economies. Among them, Burundi presented the lowest income per capita, some 230 U.S. dollars. Poverty and population growth in Africa Despite a few countries being in the high income and upper-middle countries classification, Africa had a significant number of people living under extreme poverty. However, this number is expected to decline gradually in the upcoming years, with experts forecasting that this number will decrease to almost 400 million individuals by 2030 from nearly 430 million in 2023, despite the continent currently having the highest population growth rate globally. African economic growth and prosperity In recent years, Africa showed significant growth in various industries, such as natural gas production, clean energy generation, and services exports. Furthermore, it is forecast that the GDP growth rate would reach 4.5 percent by 2027, keeping the overall positive trend of economic growth in the continent.

  3. Forecast of the global middle class population 2015-2030

    • statista.com
    Updated Jan 23, 2025
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    Statista (2025). Forecast of the global middle class population 2015-2030 [Dataset]. https://www.statista.com/statistics/255591/forecast-on-the-worldwide-middle-class-population-by-region/
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    Dataset updated
    Jan 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2017
    Area covered
    Worldwide
    Description

    By 2030, the middle-class population in Asia-Pacific is expected to increase from 1.38 billion people in 2015 to 3.49 billion people. In comparison, the middle-class population of sub-Saharan Africa is expected to increase from 114 million in 2015 to 212 million in 2030.

    Worldwide wealth

    While the middle-class has been on the rise, there is still a huge disparity in global wealth and income. The United States had the highest number of individuals belonging to the top one percent of wealth holders, and the value of global wealth is only expected to increase over the coming years. Around 57 percent of the world’s population had assets valued at less than 10,000 U.S. dollars; while less than one percent had assets of more than million U.S. dollars. Asia had the highest percentage of investable assets in the world in 2018, whereas Oceania had the highest percent of non-investable assets.

    The middle-class

    The middle class is the group of people whose income falls in the middle of the scale. China accounted for over half of the global population for middle-class wealth in 2017. In the United States, the debate about the middle class “disappearing” has been a popular topic due to the increase in wealth to the top billionaires in the nation. Due to this, there have been arguments to increase taxes on the rich to help support the middle-class.

  4. Additional file 2: of The learning environment of paediatric interns in...

    • springernature.figshare.com
    xlsx
    Updated Jun 3, 2023
    + more versions
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    Kimesh Naidoo; Jacqueline Van Wyk; Miriam Adhikari (2023). Additional file 2: of The learning environment of paediatric interns in South Africa [Dataset]. http://doi.org/10.6084/m9.figshare.5648680.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Kimesh Naidoo; Jacqueline Van Wyk; Miriam Adhikari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    Appendix B: Modified PHEEM for Intern-superviors in South Africa. (XLSX 11Â kb)

  5. 南非 CCI:中等偏下收入(LM)

    • ceicdata.com
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    CEICdata.com, 南非 CCI:中等偏下收入(LM) [Dataset]. https://www.ceicdata.com/zh-hans/south-africa/consumer-survey/cci-lower-middle-income-lm
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    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 1, 2015 - Jun 1, 2018
    Area covered
    南非
    Variables measured
    Consumer Survey
    Description

    CCI:中等偏下收入(LM)在06-01-2018达12.333%,相较于03-01-2018的24.667%有所下降。CCI:中等偏下收入(LM)数据按季更新,03-01-1995至06-01-2018期间平均值为7.000%,共94份观测结果。该数据的历史最高值出现于12-01-1996,达28.000%,而历史最低值则出现于06-01-2015,为-17.400%。CEIC提供的CCI:中等偏下收入(LM)数据处于定期更新的状态,数据来源于Bureau of Economic Research,数据归类于Global Database的南非 – 表 ZA.H006:消费者调查。

  6. f

    Rural, Urban and Migrant Differences in Non-Communicable Disease...

    • plos.figshare.com
    tiff
    Updated Jun 1, 2023
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    Oyinlola Oyebode; Utz J. Pape; Anthony A. Laverty; John T. Lee; Nandita Bhan; Christopher Millett (2023). Rural, Urban and Migrant Differences in Non-Communicable Disease Risk-Factors in Middle Income Countries: A Cross-Sectional Study of WHO-SAGE Data [Dataset]. http://doi.org/10.1371/journal.pone.0122747
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    tiffAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Oyinlola Oyebode; Utz J. Pape; Anthony A. Laverty; John T. Lee; Nandita Bhan; Christopher Millett
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundUnderstanding how urbanisation and rural-urban migration influence risk-factors for non-communicable disease (NCD) is crucial for developing effective preventative strategies globally. This study compares NCD risk-factor prevalence in urban, rural and migrant populations in China, Ghana, India, Mexico, Russia and South Africa.MethodsStudy participants were 39,436 adults within the WHO Study on global AGEing and adult health (SAGE), surveyed 2007–2010. Risk ratios (RR) for each risk-factor were calculated using logistic regression in country-specific and all country pooled analyses, adjusted for age, sex and survey design. Fully adjusted models included income quintile, marital status and education.ResultsRegular alcohol consumption was lower in migrant and urban groups than in rural groups (pooled RR and 95%CI: 0.47 (0.31–0.68); 0.58, (0.46–0.72), respectively). Occupational physical activity was lower (0.86 (0.72–0.98); 0.76 (0.65–0.85)) while active travel and recreational physical activity were higher (pooled RRs for urban groups; 1.05 (1.00–1.09), 2.36 (1.95–2.83), respectively; for migrant groups: 1.07 (1.0 -1.12), 1.71 (1.11–2.53), respectively). Overweight, raised waist circumference and diagnosed diabetes were higher in urban groups (1.19 (1.04–1.35), 1.24 (1.07–1.42), 1.69 (1.15–2.47), respectively). Exceptions to these trends exist: obesity indicators were higher in rural Russia; active travel was lower in urban groups in Ghana and India; and in South Africa, urban groups had the highest alcohol consumption.ConclusionMigrants and urban dwellers had similar NCD risk-factor profiles. These were not consistently worse than those seen in rural dwellers. The variable impact of urbanisation on NCD risk must be considered in the design and evaluation of strategies to reduce the growing burden of NCDs globally.

  7. k

    The Human Capital Report

    • datasource.kapsarc.org
    Updated Dec 17, 2024
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    (2024). The Human Capital Report [Dataset]. https://datasource.kapsarc.org/explore/dataset/the-human-capital-report-2016/
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    Dataset updated
    Dec 17, 2024
    Description

    Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

    Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

    Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Vietnam, Yemen, Zambia, Zimbabwe, WORLD

    Follow data.kapsarc.org for timely data to advance energy economics research.

    Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

  8. H

    Multi-Country Analysis of Treatment Costs for HIV/AIDS (MATCH): Unit costing...

    • dataverse.harvard.edu
    bin, doc, docx, xls +1
    Updated Jun 11, 2013
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    Harvard Dataverse (2013). Multi-Country Analysis of Treatment Costs for HIV/AIDS (MATCH): Unit costing at 161 Representative Facilities in Ethiopia, Malawi, Rwanda, South Africa and Zambia [Dataset]. http://doi.org/10.7910/DVN/SAL1TO
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    xls(2000384), xlsx(1070071), docx(167757), docx(34294), bin(2906756), doc(1041741), xlsx(1110137)Available download formats
    Dataset updated
    Jun 11, 2013
    Dataset provided by
    Harvard Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2009 - 2011
    Area covered
    Africa, Malawi, Ethiopia, Africa
    Description

    Background Today’s uncertain funding landscape threatens to halt progress towards global HIV/AIDS treatment and prevention goals. Improving the efficiency of HIV/AIDS spending is essential to effectively allocate existing funds and promote additional investment. Prior research focused on efficiency opportunities in ART, however recent and representative data on ART costs were limited. Methods and Findings Comprehensive data on twelve months of ART costs w ere collected at 161 representative facilities across five countries. Sites were selected using stratified random sampling. Patient characteristics and treatment outcomes were measured by reviewing 100 randomly-selected charts per facility. ART costs were significantly lower than expected, averaging $200 per patient-year in low- and lower-middle income countries. Costs in South Africa were higher at $682 per patient-year. Main cost components were ARVs and personnel, together comprising more than 70% of costs. Facilities demonstrated the ability to keep patients alive and on treatment at these cost levels, with relatively low average annual attrition rates for established patients, ranging from 2 - 8%. Retention rates for new patients were highly variable and strongly correlated with CD4 count at initiation. Conclusions This study provides new evidence that aggressive scale-up of high-quality treatment in developing countries is possible and sustainable. The low cost of ART should allay concerns about incurring a prohibitively expensive “treatment mortgage” through aggressive scale-up. Additionally, there has been a significant global focus on driving savings by optimizing service delivery design and it appears as though significant savings are unlikely in facility-level A RT costs. Optimization efforts should instead focus on treatment costs outside the facility and program costs unrelated to treatment. In South Africa, and likely other upper-middle income countries, optimizing ART service delivery will generate savings and should be pursued. Finally, across countries, there are clear opportunities to improve patient outcomes without substantially increasing cost, the most prominent of which is better using the money already being spent on pre-ART to more effectively drive earlier initiation of treatment. Funding The Bill and Melinda Gates Foundation

  9. Research on Early Life and Aging Trends and Effects (RELATE): A...

    • search.gesis.org
    Updated Mar 11, 2021
    + more versions
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    McEniry, Mary (2021). Research on Early Life and Aging Trends and Effects (RELATE): A Cross-National Study - Archival Version [Dataset]. http://doi.org/10.3886/ICPSR34241
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    Dataset updated
    Mar 11, 2021
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    GESIS search
    Authors
    McEniry, Mary
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de450289https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de450289

    Description

    Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

  10. a

    WDIData

    • hub.arcgis.com
    Updated Sep 12, 2018
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    lzwartje (2018). WDIData [Dataset]. https://hub.arcgis.com/datasets/f104cffd61d04d9d8806d3c7827c67c4
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    Dataset updated
    Sep 12, 2018
    Dataset authored and provided by
    lzwartje
    Area covered
    Description

    Acronym: WDIType: Time SeriesTopics: Agriculture and Food Security, Climate Change, Economic Growth, Education, Energy and Extractives, Environment and Natural Resources, Financial Sector, Development,GenderHealth Nutrition and Population,Macroeconomic Vulnerability and Debt,Poverty, Private Sector Development, Public Sector Management, Social Development, Social Protection and Labor, Trade, Economy Coverage: High Income IBRD IDA Low Income Lower Middle Income Upper Middle IncomeLanguages Supported: English Arabic Chinese French SpanishNumber of Economies: 217Geographical Coverage: World East Asia & Pacific American Samoa Australia Brunei Darussalam Cambodia China FijiFrench Polynesia Guam Hong Kong SAR, China Indonesia Japan KiribatiKorea, Dem. People's Rep. Korea, Rep. Lao PDR Macao SAR, China Malaysia Marshall IslandsMongolia Myanmar Nauru New Caledonia New Zealand Northern Mariana Islands PalauPapua New Guinea Philippines Samoa Singapore Solomon Islands Thailand Timor-LesteTonga Tuvalu Vanuatu Vietnam Europe & Central Asia Albania Andorra Armenia AustriaAzerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech RepublicDenmark Estonia Faroe Islands Finland France Georgia Germany Gibraltar Greece GreenlandHungary Iceland Ireland Isle of Man Italy Kazakhstan Kyrgyz Republic Latvia LiechtensteinLithuania Luxembourg Macedonia, FYR Moldova Monaco Montenegro Netherlands NorwayPoland Portugal Romania Russian Federation San Marino Serbia Slovak Republic SloveniaSpain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United KingdomUzbekistan Latin America & Caribbean Antigua and Barbuda Aruba Argentina Bahamas, TheBarbados Belize Bolivia Brazil Cayman Islands Chile Costa Rica Colombia Cuba CuraçaoDominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana HaitiHonduras Jamaica Mexico Nicaragua Panama Paraguay Peru Puerto RicoSint Maarten (Dutch part) St. Kitts and Nevis St. Martin (French part) St. LuciaSt. Vincent and the Grenadines Suriname Trinidad and Tobago Turks and Caicos IslandsUruguay Venezuela, RB Virgin Islands (U.S.) Middle East & North Africa Algeria BahrainEgypt, Arab Rep. Djibouti Iraq Iran, Islamic Rep. Israel Jordan Kuwait Lebanon Libya MaltaMorocco Oman Qatar Saudi Arabia Syrian Arab Republic United Arab Emirates TunisiaYemen, Rep. Bermuda Canada United States South Asia Afghanistan Bangladesh BhutanIndia Pakistan Nepal Maldives Sri Lanka Angola Benin Botswana Burkina Faso BurundiCabo Verde Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep.Côte d'Ivoire Ethiopia Eritrea Equatorial Guinea Gabon Gambia, The Ghana GuineaGuinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania MauritiusMozambique Namibia Niger Nigeria Rwanda São Tomé and Principe Seychelles SenegalSierra Leone Somalia South Africa South Sudan Sudan Swaziland Tanzania Togo UgandaZambia Zimbabwe

  11. Data from: Associations of adverse maternal experiences and diabetes on...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    bin, csv, txt
    Updated Aug 17, 2023
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    Yael K. Rayport; Yael K. Rayport; Ayesha Sania; Maristella Lucchini; Carlie Du Plessis; Mandy Potter; Priscilla E. Springer; Priscilla E. Springer; Lissete A. Gimenez; Lissete A. Gimenez; Hein J. Odendaal; Hein J. Odendaal; William P. Fifer; Lauren C. Shuffrey; Ayesha Sania; Maristella Lucchini; Carlie Du Plessis; Mandy Potter; William P. Fifer; Lauren C. Shuffrey (2023). Associations of adverse maternal experiences and diabetes on postnatal maternal depression and child social-emotional outcomes in a South African community cohort [Dataset]. http://doi.org/10.5061/dryad.kkwh70s73
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    bin, txt, csvAvailable download formats
    Dataset updated
    Aug 17, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Yael K. Rayport; Yael K. Rayport; Ayesha Sania; Maristella Lucchini; Carlie Du Plessis; Mandy Potter; Priscilla E. Springer; Priscilla E. Springer; Lissete A. Gimenez; Lissete A. Gimenez; Hein J. Odendaal; Hein J. Odendaal; William P. Fifer; Lauren C. Shuffrey; Ayesha Sania; Maristella Lucchini; Carlie Du Plessis; Mandy Potter; William P. Fifer; Lauren C. Shuffrey
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Previous literature has identified associations between diabetes during pregnancy and postnatal maternal depression. Both maternal conditions are associated with adverse consequences on childhood development. Despite an especially high prevalence of diabetes during pregnancy and maternal postnatal depression in low- and middle-income countries, related research predominates in high-income countries. In a South African cohort with or without diabetes, we investigated associations between adverse maternal experiences with postnatal maternal depression and child social-emotional outcomes. South African mother-child dyads were recruited from the Bishop Lavis community in Cape Town. Participants consisted of 82 mother-child dyads (53 women had GDM or type 2 diabetes). At 14–20 months postpartum, maternal self-report questionnaires were administered to assess household socioeconomic status, food insecurity, maternal depressive symptoms (Edinburgh Postnatal Depression Scale (EPDS)), maternal trauma (Life Events Checklist), and child social-emotional development (Brief Infant Toddler Social Emotional Assessment, Ages and Stages Questionnaires: Social-Emotional, Second Edition). Lower educational attainment, lower household income, food insecurity, living without a partner, and having experienced physical assault were each associated with postnatal maternal depressive symptoms and clinical maternal depression (EPDS ≥ 13). Maternal postnatal depression, lower maternal educational attainment, lower household income, household food insecurity, and living in a single-parent household were each associated with child social-emotional problems. Stratified analyses revealed maternal experiences (education, income, food insecurity, trauma) were associated with postnatal maternal depressive symptoms and child social-emotional problems only among dyads with in utero exposure to diabetes. Women with pre-existing diabetes or gestational diabetes in LMIC settings should be screened for health-related social needs to reduce the prevalence of depression and to promote child social-emotional development.

  12. Primary Humid Forest

    • globil.panda.org
    Updated Jul 2, 2020
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    World Wide Fund for Nature (2020). Primary Humid Forest [Dataset]. https://globil.panda.org/datasets/primary-humid-forest
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    Dataset updated
    Jul 2, 2020
    Dataset authored and provided by
    World Wide Fund for Naturehttp://wwf.org/
    Area covered
    Description

    To advance our understanding of forest cover changes, given the discrepancies, this work providesan original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) toestimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, forwhich data was available. This assumes that no single approach or data source can capture majortrends everywhere; therefore, an all-available data approach is needed to overcome shortcomings ofindividual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends tounderestimate the conversion in dry forests ecosystems and lacks explicit assessment ofuncertainties across the different datasets.“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing fromforest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m giventhe original resolutions of the datasets incorporated and on the understanding that forest areasshould be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.species distribution, ecology, ecosystem services), rather than depicting individual trees or groups oftrees.According to our analysis, about 20% of total forest cover loss takes place in core forest, which welabel “primary forest loss”, while the remaining 80% results from the conversion of edge andpatched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss inthe period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperateforests. A portion of the loss in temperate and boreal forests will not be permanent and might referto other types of natural forest disturbances produced by insects, fire, and severe weather, as wellas by felling of plantations or semi-natural forests as part of forest management.Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss ismainly in South America and Africa. When looking at countries by income levels, as defined by theWorld Bank, much of deforestation takes place in upper middle and lower middle-income countries.To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurswhen countries increase economic development. In the tropics, upper-middle income countriesdominate forest cover loss in South America, due to the influence of Brazil, and lower middle-income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occursmainly in Brazil and Argentina in South America, many lower-middle income countries in SouthAmerica, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest coverloss, likely not all permanent, occurs in high-income countries (Russia), and North America (UnitedStates and Canada) Unfortunately, this data does not identify changes over time or land use interactions amongcountries. Reduced forest cover loss in some mainly high-income countries, except North America, isassociated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. Interactions are informed by the “forest transition” effect. Forest transition dynamics occur whennet forest restoration replaces net forest cover loss in some specific place. The countries thatunderwent a forest transition that reduced forest loss and encouraged regrowth may have placedadditional pressure on forests outside their borders, thus displacing deforestation. The debate onforest transitions and leakage is quite controversial given its policy implications.Recent analysis, based on a land-balance model that quantifies deforestation due to global trade atcountry level in the tropics and sub-tropics, linked to a country-to-country trade model, found thatfrom 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.About 26% of total deforestation was attributed to international demand, 87% of which wasexported to countries with decreasing deforestation or increasing forest cover in Europe and Asia(i.e. China, India). Some of this displacement pressure may be reduced by land intensification. Global patterns of forest fragmentationIn this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-factorial phenomenon that includes amongst others loss of native species, appearance of invasivespecies, pollution damage, structural changes, selective timber removal and many more. Here weuse fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect offorest degradation but does not capture all aspects. The change in spatial pattern and structure byfragmentation of forest into smaller patches or “islands” damages forest ecosystem services such ascarbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. These impacts are created by changes at forest edges, which include increased exposure to differentclimate, fire, wind, mortality, and human access. The increasing isolation of forest patchescontributes to long-term changes in biodiversity, including species richness and productivity,creating fundamental changes in forest ecosystems.We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, todetermine forest cover transitions between different type of fragmentation classes (i.e. stable core,inner edges, outer edges, and patches). Changes between fragmentation classes over time aredefined as primary and secondary degradation based on their initial state, in contrast to forestswhich remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. Inthis definition, primary degradation is a result of the fragmentation of core forests into forest withmore edges, reducing the area of continuous forest extent, and resulting in greater losses of carbonand associated ecosystem services such as biodiversity present in intact forests. Secondarydegradation is the conversion of edge forests into more fragmented classes, occurring in secondaryforests which may already be degraded and are more accessible and easier to deforest

  13. Annual poverty rate in Southern Africa 2023, by country and income level

    • statista.com
    Updated Jun 3, 2025
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    Statista (2025). Annual poverty rate in Southern Africa 2023, by country and income level [Dataset]. https://www.statista.com/statistics/1551703/southern-africa-poverty-rate-by-country-and-income-level/
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    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Africa
    Description

    In 2023, the international poverty (based on 2017 purchasing power parities (PPPs)) and the lower-income poverty rate (3.65 U.S. dollars in 2017 PPP), was highest for Mozambique within the Southern Africa region, with 74.7 percent and 88.7 percent, respectively. However, the upper middle-income poverty rate was highest for Zambia, at 93 percent.

  14. a

    Global Forest Analysis

    • hub.arcgis.com
    Updated Jul 2, 2020
    + more versions
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    World Wide Fund for Nature (2020). Global Forest Analysis [Dataset]. https://hub.arcgis.com/maps/ef56ea768f354941ae20e74a4458e37d
    Explore at:
    Dataset updated
    Jul 2, 2020
    Dataset authored and provided by
    World Wide Fund for Nature
    Area covered
    Description

    To advance our understanding of forest cover changes, given the discrepancies, this work providesan original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) toestimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, forwhich data was available. This assumes that no single approach or data source can capture majortrends everywhere; therefore, an all-available data approach is needed to overcome shortcomings ofindividual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends tounderestimate the conversion in dry forests ecosystems and lacks explicit assessment ofuncertainties across the different datasets.“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing fromforest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m giventhe original resolutions of the datasets incorporated and on the understanding that forest areasshould be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.species distribution, ecology, ecosystem services), rather than depicting individual trees or groups oftrees.According to our analysis, about 20% of total forest cover loss takes place in core forest, which welabel “primary forest loss”, while the remaining 80% results from the conversion of edge andpatched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss inthe period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperateforests. A portion of the loss in temperate and boreal forests will not be permanent and might referto other types of natural forest disturbances produced by insects, fire, and severe weather, as wellas by felling of plantations or semi-natural forests as part of forest management.Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss ismainly in South America and Africa. When looking at countries by income levels, as defined by theWorld Bank, much of deforestation takes place in upper middle and lower middle-income countries.To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurswhen countries increase economic development. In the tropics, upper-middle income countriesdominate forest cover loss in South America, due to the influence of Brazil, and lower middle-income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occursmainly in Brazil and Argentina in South America, many lower-middle income countries in SouthAmerica, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest coverloss, likely not all permanent, occurs in high-income countries (Russia), and North America (UnitedStates and Canada) Unfortunately, this data does not identify changes over time or land use interactions amongcountries. Reduced forest cover loss in some mainly high-income countries, except North America, isassociated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. Interactions are informed by the “forest transition” effect. Forest transition dynamics occur whennet forest restoration replaces net forest cover loss in some specific place. The countries thatunderwent a forest transition that reduced forest loss and encouraged regrowth may have placedadditional pressure on forests outside their borders, thus displacing deforestation. The debate onforest transitions and leakage is quite controversial given its policy implications.Recent analysis, based on a land-balance model that quantifies deforestation due to global trade atcountry level in the tropics and sub-tropics, linked to a country-to-country trade model, found thatfrom 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.About 26% of total deforestation was attributed to international demand, 87% of which wasexported to countries with decreasing deforestation or increasing forest cover in Europe and Asia(i.e. China, India). Some of this displacement pressure may be reduced by land intensification. Global patterns of forest fragmentationIn this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-factorial phenomenon that includes amongst others loss of native species, appearance of invasivespecies, pollution damage, structural changes, selective timber removal and many more. Here weuse fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect offorest degradation but does not capture all aspects. The change in spatial pattern and structure byfragmentation of forest into smaller patches or “islands” damages forest ecosystem services such ascarbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. These impacts are created by changes at forest edges, which include increased exposure to differentclimate, fire, wind, mortality, and human access. The increasing isolation of forest patchescontributes to long-term changes in biodiversity, including species richness and productivity,creating fundamental changes in forest ecosystems.We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, todetermine forest cover transitions between different type of fragmentation classes (i.e. stable core,inner edges, outer edges, and patches). Changes between fragmentation classes over time aredefined as primary and secondary degradation based on their initial state, in contrast to forestswhich remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. Inthis definition, primary degradation is a result of the fragmentation of core forests into forest withmore edges, reducing the area of continuous forest extent, and resulting in greater losses of carbonand associated ecosystem services such as biodiversity present in intact forests. Secondarydegradation is the conversion of edge forests into more fragmented classes, occurring in secondaryforests which may already be degraded and are more accessible and easier to deforest

  15. P

    Packaging Industry in Africa Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 25, 2025
    + more versions
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    Market Report Analytics (2025). Packaging Industry in Africa Report [Dataset]. https://www.marketreportanalytics.com/reports/packaging-industry-in-africa-92565
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    ppt, doc, pdfAvailable download formats
    Dataset updated
    Apr 25, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The African packaging industry, valued at $43.48 million in 2025, is experiencing robust growth, projected at a 3.85% Compound Annual Growth Rate (CAGR) from 2025 to 2033. This expansion is fueled by several key factors. The burgeoning food and beverage sector, driven by rising population and urbanization, is a significant driver, demanding increased packaging solutions for safe and efficient product distribution. The growth of e-commerce further accelerates demand, particularly for protective packaging materials. A shift towards sustainable and eco-friendly packaging options, such as biodegradable materials and reduced plastic usage, is a prominent trend, influencing both consumer preferences and regulatory frameworks. However, challenges remain, including inconsistent infrastructure, fluctuating raw material prices, and a need for enhanced technological adoption across the supply chain. The market is segmented by material (paper & paperboard, plastic, metal, glass), product type (bottles, boxes, cans), and end-user industry (beverage, food, pharmaceuticals, personal care). Key players like Astrapak Ltd, Nampak Limited, and Mondi Group are actively shaping the market landscape through innovation and expansion strategies. The industry's regional distribution reflects the diverse economic development across Africa. While South Africa currently holds a significant market share, driven by its advanced infrastructure and established industries, substantial growth potential lies within other regions, particularly in rapidly developing economies experiencing increased consumer spending and industrialization. The forecast period (2025-2033) is expected to witness significant investment in automated packaging technologies and the adoption of advanced materials, further propelling market growth. Addressing the challenges related to sustainability and infrastructure will be crucial in maximizing the industry’s potential and ensuring its long-term competitiveness on the global stage. Recent developments include: June 2022: Dow expanded the flexible packaging recycling initiative to new African markets. Dow announced that its flexible packaging recycling initiative, project REFLEX, will be expanded to Egypt and Guinea. The expansion of Project REFLEX into Egypt began in December 2021, with Dow entering an 18-month partnership with the international non-government organization, WasteAid, which shares waste management and recycling skills with lower- and middle-income countries. WasteAid will work with Dow to advance waste recovery and recycling in Aswan, a city located in southern Egypt., April 2022: Ardagh Group announced the completion of the acquisition of Consol Holdings Proprietary Limited, a leading producer of glass packaging on the African continent. The acquisition, for USD 1 billion, including net debt assumed in Consol, indicates a significant inward investment into the South African and other markets in which Consol operates, with a further ZAR 3 billion (USD 200 million) investment program in two new furnaces.. Key drivers for this market are: Glass Bottles to Drive the Market Growth, Beverage Industry to Lead the Market Growth. Potential restraints include: Glass Bottles to Drive the Market Growth, Beverage Industry to Lead the Market Growth. Notable trends are: Glass Bottles to Drive the Market Growth.

  16. c

    GCRF Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods:...

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Jun 14, 2025
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    Wang, Y; Kintrea, K; Osborne, M; Schweisfurth, M; Mitchell, R; Kamete, A; Yao, J; Stewart, A; Ahmad, S; Young, G; Nesterova, Y; Everatt, D; Lynge, H; Abrahams, C; Turok, I; Scheba, A; Visagie, J; Manirakiza, V; Malonza, J; Nduwayezu, G; Mugabe, L; Nsabimana, A; Rutayisire, P; Nzayirambaho, M; Njunwa, J; Levira, F, Ifakara Health Institute; Moshi, I, Ifakara Health Institute; Msuya, I, Ifakara Health Institute; Kundu, D, National Institute of Urban Affairs; Sharma, P, National Institute of Urban Affairs; Debnath; Roy, S, Khulna University; Sowgat, T, Khulna University; ISLAM; Shakil, I, Khulna University; Reyes, M; Gamboa, M; Rivera, R; Caluag, A; Manlapas, I; Racoma, D; Sun, T, Nankai University; Zhai, L, Nankai University; Li, C, Nankai University; Liu, Y, Nankai University; Wang, C, Nankai University; Zhang, L, Nankai University; Sun, X, Nankai University; Bhandari, R; Baffoe, G; Victoria, L; Jinqiao, L (2025). GCRF Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods: Household Survey and Neighbourhood Focus Group Data from Seven Asian and African Countries, 2021-2022 [Dataset]. http://doi.org/10.5255/UKDA-SN-855998
    Explore at:
    Dataset updated
    Jun 14, 2025
    Dataset provided by
    University of Glasgow
    University of the Witwatersrand
    Chester University
    University of Rwanda
    T
    Tanzania
    India
    Bangladesh
    Centre for Neighbourhood Studies
    York University
    Human Sciences Research Council
    China
    Authors
    Wang, Y; Kintrea, K; Osborne, M; Schweisfurth, M; Mitchell, R; Kamete, A; Yao, J; Stewart, A; Ahmad, S; Young, G; Nesterova, Y; Everatt, D; Lynge, H; Abrahams, C; Turok, I; Scheba, A; Visagie, J; Manirakiza, V; Malonza, J; Nduwayezu, G; Mugabe, L; Nsabimana, A; Rutayisire, P; Nzayirambaho, M; Njunwa, J; Levira, F, Ifakara Health Institute; Moshi, I, Ifakara Health Institute; Msuya, I, Ifakara Health Institute; Kundu, D, National Institute of Urban Affairs; Sharma, P, National Institute of Urban Affairs; Debnath; Roy, S, Khulna University; Sowgat, T, Khulna University; ISLAM; Shakil, I, Khulna University; Reyes, M; Gamboa, M; Rivera, R; Caluag, A; Manlapas, I; Racoma, D; Sun, T, Nankai University; Zhai, L, Nankai University; Li, C, Nankai University; Liu, Y, Nankai University; Wang, C, Nankai University; Zhang, L, Nankai University; Sun, X, Nankai University; Bhandari, R; Baffoe, G; Victoria, L; Jinqiao, L
    Time period covered
    Jul 1, 2021 - Mar 30, 2022
    Area covered
    Rwanda, Bangladesh, India, Philippines, Tanzania, South Africa, People's Republic of China, Africa
    Variables measured
    Individual, Household
    Measurement technique
    In each country the study selected one large city and one smaller regional cities as case studied. Within each case study cities, neighbourhoods were categorised roughly into five income and wealth bands: the rich, upper middle income, middle income, lower middle and low income neighbourhoods. A household survey was carried out face to face by trained interviewers with a random adult member of the household. A common questionnaire was designed and adopted by all teams. The sample was distributed in the city to representative the five neighbourhood types. The survey was followed by focus group interviews. A carefully designed and agreed common interview guide was used by all team. The target was one focus group for a sample neighbourhood in each income band in each city. Focus groups were recorded, all transcripts were translated into English for analysis.
    Description

    In order to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by cities and local communities in the developing countries, the SHLC team conducted a major household survey followed by a neighbourhood focus group interview in seven Asian and African countries from late 2021 to early 2022. In each country the study includes two case study cities: one large city and one smaller regional cities. Within each case study cities, neighbourhoods were identified and categorised into five income and wealth bands: the rich, upper middle income, middle income, lower middle and low income neighbourhoods.

    A household survey was carried out face to face by trained interviewers with a random adult member of the household. The 20 page common questionnaire was designed and adopted by all teams, which cover topics of housing, residence, living conditions, migration, education, health, neighbourhood infrastructure, facilities, governance and relations, income and employments, gender equality and impacts from Covid-19. The sample was distributed in the city to representative the five neighbourhood types. The survey was completed in 13 of the 14 case study cities (fieldwork in Chongqing in China was delayed by the Covid-19 lockdowns and implemented in August 2023). The target sample for each city was 1000; the total sample in the database (SPSS and STATA) include 14245 households.

    The survey was followed by focus group interviews. A carefully designed and agreed common interview guide was used by all team. The target was to have one focus group for one neighbourhood in each income band in each city. A total of 74 focus group interviews were conducted (Fieldwork in Datong and Chongqing in China was delayed). The transcripts are the qualitative data shared here.

    The Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods (SHLC) was funded by UKRI Global Challenge Research Fund (GCRF) from 2017 to 2023. Its main aim was to grow research capability to meet the challenges faced by developing countries (Grow). SHLC, led by University of Glasgow, was set up as an international collaborative research centre to address urban challenges across communities in Africa and Asia. Its work contributed to three UN 2030 Sustainable Development Goals: 11 - Make cities and human settlements sustainable; 3 - Ensure healthy lives for all; 4 - Ensure inclusive and equitable quality education for all. SHLC brought together the expertise of urban studies, education, health, geography, planning and data science from nine institutions in eight countries. Its international partners included: Ifakara Health Institute (Tanzania), Khulna University (Bangladesh), Nankai University (China), National Institute of Urban Affairs (India), The Human Sciences Research Council and University of Witwatersrand (South Africa), The University of the Philippines and The University of Rwanda. SHLC working programme had two streams of work and eight specific task packages. Stream one included four Capacity Strengthening Packages which involved the training of over 100 researchers and enhancing the associated academic networks. Steam two work consisted of four Research Task Packages. The co-designed research programme adopted a common research framework in all seven countries (14 case study cities), aiming to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by these cities and local communities. Apart from policy reviews, secondary data analysis, the project employed two major primary data collection methods – household questionnaire survey and neighbourhood focus groups. The team have overcome many challenges brought by the Covid-19 pandemics and completed the household survey in 13 cities with a total sample size of 14245, which covered five different types of neighbourhoods ranging from the rich to the poor. The team also completed 74 neighbourhood focus group interviews. Data collection was carried out from late 2021 to early 2022. Huge resources and researchers’ time were dedicated to coordinate, collect, translate, clean and merge these quantitative and qualitative data.

  17. w

    Global Male Aesthetic Market Research Report: By Product Type (Cosmetic...

    • wiseguyreports.com
    Updated Dec 3, 2024
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Male Aesthetic Market Research Report: By Product Type (Cosmetic Procedures, Skincare Products, Hair Transplant, Weight Management, Fitness Programs), By Age Group (18-25, 26-35, 36-45, 46-55, 56 and above), By Income Level (Lower Income, Middle Income, Upper Middle Income, High Income), By Treatment Focus (Anti-Aging, Body Contouring, Hair Restoration, Skin Rejuvenation, Facial Aesthetics) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/male-aesthetic-market
    Explore at:
    Dataset updated
    Dec 3, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 202328.32(USD Billion)
    MARKET SIZE 202430.17(USD Billion)
    MARKET SIZE 203250.0(USD Billion)
    SEGMENTS COVEREDProduct Type, Age Group, Income Level, Treatment Focus, Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSincreasing male grooming popularity, rising disposable incomes, evolving beauty standards, advancements in aesthetic technology, growing influence of social media
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDRevance Therapeutics, Prollenium Medical Technologies, Allergan, Ipsen, Hugel, Sientra, Aesthetics Biomedical, Evolus, Alastin Skincare, Medytox, Galderma
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIESIncreased demand for grooming products, Growth in aesthetic procedures, Rising awareness of male skincare, Expanding social media influence, Development of gender-neutral brands
    COMPOUND ANNUAL GROWTH RATE (CAGR) 6.52% (2025 - 2032)
  18. a

    Sustainable Development Report 2024 (with indicators)

    • sdg-transformation-center-sdsn.hub.arcgis.com
    Updated Jun 5, 2024
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    Sustainable Development Solutions Network (2024). Sustainable Development Report 2024 (with indicators) [Dataset]. https://sdg-transformation-center-sdsn.hub.arcgis.com/datasets/sustainable-development-report-2024-with-indicators
    Explore at:
    Dataset updated
    Jun 5, 2024
    Dataset authored and provided by
    Sustainable Development Solutions Network
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    Since 2016, the global edition of the Sustainable Development Report (SDR) has provided the most up-to-date data to track and rank the performance of all UN member states on the SDGs. This year’s edition was written by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN. It focuses on the UN Summit of the Future, with an opening chapter endorsed by 100+ global scientists and practitioners. The report also includes two thematic chapters, related to SDG 17 (Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development) and SDG 2 (End hunger, achieve food security and improved nutrition and promote sustainable agriculture).This year’s SDR highlights five key findings:On average, globally, only 16% of the SDG targets are on track to be achieved by 2030, with the remaining 84% demonstrating limited or a reversal of progress. At the global level, SDG progress has been stagnant since 2020, with SDG 2 (Zero Hunger), SDG11 (Sustainable Cities and Communities), SDG14 (Life Below Water), SDG15 (Life on Land) and SDG16 (Peace, Justice, and Strong Institutions) particularly off-track. Globally, the five SDG targets on which the highest proportion of countries show a reversal of progress since 2015 include: obesity rate (under SDG 2), press freedom (under SDG 16), the red list index (under SDG 15), sustainable nitrogen management (under SDG 2), and – due in a large part to the COVID-19 pandemic and other factors that may vary across countries – life expectancy at birth (under SDG 3). Goals and targets related to basic access to infrastructure and services, including SDG9 (Industry, Innovation, and Infrastructure), show slightly more positive trends, although progress remains too slow and uneven across countries.The pace of SDG progress varies significantly across country groups. Nordic countries continue to lead on SDG achievement, with BRICS demonstrating strong progress and poor and vulnerable nations lagging far behind. Similar to past years, European countries – notably Nordic countries – top the 2024 SDG Index. Finland ranks number 1 on the SDG Index, followed by Sweden (#2), Denmark (#3), Germany (#4), and France (#5). Yet, even these countries face significant challenges in achieving several SDGs. Average SDG progress in BRICS (Brazil, the Russian Federation, India, China, and South Africa) and BRICS+ (Egypt, Ethiopia, Iran, Saudi Arabia, and the United Arab Emirates) since 2015 has been faster than the world average. In addition, East and South Asia has emerged as the region that has made the most SDG progress since 2015. By contrast, the gap between the world average SDG Index and the performance of the poorest and most vulnerable countries, including Small Island Developing States (SIDS), has widened since 2015.Sustainable development remains a long-term investment challenge. Reforming the Global Financial Architecture is more urgent than ever. The world requires many essential public goods that far transcend the nation-state. Low-income countries (LICs) and lower-middle-income countries (LMICs) urgently need to gain access to affordable long-term capital so that they can invest at scale to achieve their sustainable development objectives. Mobilizing the necessary levels of finance will require new institutions, new forms of global financing — including global taxation —, and new priorities for global financing, such as investing in quality education for all. The report presents five complementary strategies to reform the Global Financial Architecture.Global challenges require global cooperation. Barbados ranks the highest in its commitment to UN-based multilateralism; the United States ranks last. As with the challenge of SDGs, strengthening multilateralism requires metrics and monitoring. The report’s new Index of countries’ support to UN-based multilateralism (UN-Mi) ranks countries based on their engagement with the UN system including treaty ratification, votes at the UN General Assembly, membership in UN organizations, participation in conflicts and militarization, use of unilateral sanctions and financial contributions to the UN. The five countries most committed to UN-based multilateralism are: Barbados (#1), Antigua and Barbuda (#2), Uruguay (#3), Mauritius (#4), and the Maldives (#5). By contrast, the United States (#193), Somalia (#192), South Sudan (#191), Israel (#190), and the Democratic Republic of Korea (#189) rank the lowest on the UN-Mi.SDG targets related to food and land systems are particularly off-track. The SDR presents new FABLE pathways to support sustainable food and land systems. Globally, 600 million people will still suffer from hunger by 2030, obesity is increasing globally, and greenhouse gas emissions from Agriculture, Forestry, and Other Land Use (AFOLU) represent almost a quarter of annual global GHG emissions. The new FABLE pathways brought together more than 80 local researchers across 22 countries to assess how 16 targets related to food security, climate mitigation, biodiversity conservation, and water quality could be achieved by 2030 and 2050. The continuation of current trends widens the gap with targets related to climate mitigation, biodiversity, and water quality. Pursuing commitments that have been already taken by countries would improve the situation, but they are still largely insufficient. Significant progress is possible but requires several dramatic changes: 1) avoid overconsumption beyond recommended levels and limit animal-based protein consumption with dietary shifts compatible with cultural preferences; 2) invest to foster productivity, particularly for products and areas with strong demand growth; and 3) implement inclusive, robust, and transparent monitoring systems to halt deforestation. Our sustainable pathway avoids up to 100 million hectares of deforestation by 2030 and 100 Gt CO2 emissions by 2050. Additional measures would be needed to avoid trade-offs with on-farm employment and water pollution due to excessive fertilizer application and ensure that no one is left behind, particularly to end hunger.About the AuthorsProf. Jeffrey SachsDirector, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume LafortuneDirector, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: EmailGrayson FullerManager, SDG Index & Data team, SDSNGrayson Fuller is the manager of the SDG Index and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: EmailAbout the PublishersDublin University PressDublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press

  19. Insurance penetration in Sub-Saharan Africa in 2017, by country

    • statista.com
    Updated Nov 1, 2024
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    Statista (2024). Insurance penetration in Sub-Saharan Africa in 2017, by country [Dataset]. https://www.statista.com/statistics/727403/insurance-penetration-in-sub-saharan-africa-by-country/
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    Dataset updated
    Nov 1, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2017
    Area covered
    Africa
    Description

    In South Africa, the insurance penetration rate was 16.99 percent in 2017, which was the highest rate in Sub-Saharan Africa. South Africa was followed by Namibia, Lesotho, Mauritius and Zimbabwe, which had insurance penetration rates ranging between four and seven percent. Over half of the countries in the region had a rate of less than one percent.

    What is insurance penetration? Insurance penetration refers to the ratio between the value of premiums written in a particular year in a particular country to the GDP of the respective country. Most countries in Sub-Saharan Africa have lower than average insurance penetration rates, when compared with other parts of the world.

    The future of insurance in Africa This low penetration is due to the fact that the African insurance industry is still in its infancy, premiums are financially out of reach of many people and financial literacy is relatively low. However, African insurers believe that rising education and financial literacy levels, the growth of the black middle class and the increase in the working population will have a large impact in the insurance industry in the region.

  20. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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CEICdata.com (2018). South Africa CCI: Lower Middle Income (LM) [Dataset]. https://www.ceicdata.com/en/south-africa/consumer-survey/cci-lower-middle-income-lm
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South Africa CCI: Lower Middle Income (LM)

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Dataset updated
Jun 15, 2018
Dataset provided by
CEIC Data
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Sep 1, 2015 - Jun 1, 2018
Area covered
South Africa
Variables measured
Consumer Survey
Description

South Africa CCI: Lower Middle Income (LM) data was reported at 12.333 % in Jun 2018. This records a decrease from the previous number of 24.667 % for Mar 2018. South Africa CCI: Lower Middle Income (LM) data is updated quarterly, averaging 7.000 % from Mar 1995 (Median) to Jun 2018, with 94 observations. The data reached an all-time high of 28.000 % in Dec 1996 and a record low of -17.400 % in Jun 2015. South Africa CCI: Lower Middle Income (LM) data remains active status in CEIC and is reported by Bureau for Economic Research. The data is categorized under Global Database’s South Africa – Table ZA.H006: Consumer Survey.

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