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India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).
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Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. India data available from WorldPop here.
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Context
This list ranks the 4 cities in the Rich County, UT by Hispanic American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
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This dataset tracks annual american indian student percentage from 2008 to 2015 for South Rich School vs. Utah and Rich School District
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Unemployment Rate in India remained unchanged at 5.60 percent in June. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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This dataset tracks annual asian student percentage from 1989 to 2023 for Indian River High School vs. New York and Indian River Central School District
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This dataset tracks annual white student percentage from 2013 to 2023 for Indian Springs High School vs. California and San Bernardino City Unified School District
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This dataset tracks annual american indian student percentage from 2012 to 2023 for Indian River High School vs. New York and Indian River Central School District
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This dataset tracks annual asian student percentage from 2013 to 2023 for Indian Springs High School vs. California and San Bernardino City Unified School District
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This dataset tracks annual black student percentage from 1993 to 2023 for Indian River High School vs. New York and Indian River Central School District
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This dataset tracks annual white student percentage from 1991 to 2023 for Indian Valley High School vs. Ohio and Indian Valley Local School District
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This dataset tracks annual white student percentage from 1993 to 2023 for Indian River High School vs. New York and Indian River Central School District
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This dataset tracks annual black student percentage from 1988 to 2023 for Indian Creek High School vs. Illinois and Indian Creek CUSD 425 School District
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This dataset tracks annual american indian student percentage from 2009 to 2023 for Sherman Indian High School District vs. California
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This dataset tracks annual hispanic student percentage from 1988 to 2023 for Indian Creek High School vs. Illinois and Indian Creek CUSD 425 School District
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This dataset tracks annual hispanic student percentage from 2013 to 2023 for Indian Valley High School vs. Ohio and Indian Valley Local School District
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This dataset tracks annual black student percentage from 1989 to 2023 for Indian Hills High School vs. New Jersey and Ramapo Indian Hills Regional High School District
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This dataset tracks annual black student percentage from 1991 to 2023 for Indian Hill High School vs. Ohio and Indian Hill Exempted Village School District
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This dataset tracks annual hispanic student percentage from 1991 to 2023 for Indian Hill High School vs. Ohio and Indian Hill Exempted Village School District
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This dataset tracks annual asian student percentage from 1992 to 2023 for Indian Hills High School vs. New Jersey and Ramapo Indian Hills Regional High School District
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).