In 2023, about 14 percent of District of Columbia's population lived below the poverty line. This accounts for persons or families whose collective income in the preceding 12 months was below the national poverty level of the United States.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in District of Columbia (S1701ACS011001) from 2012 to 2023 about DC, Washington, percent, poverty, 5-year, population, and USA.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

2020 data excluded because the U.S. Census Bureau did not release 2020 ACS 1-year estimates due to COVID-19. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1-Year Estimates

Why This Matters

Poverty threatens the overall well-being of individuals and families, limiting access to stable housing, healthy foods, health care, and educational and employment opportunities, among other basic needs.Poverty is associated with a higher risk of adverse health outcomes, including chronic physical and mental illness, lower life expectancy, developmental delays, and others.

Racist policies and practices have contributed to racial economic inequities. Nationally, Black, Indigenous, and people of color experience poverty at higher rates than white Americans, on average.

The District's Response

Boosting assistance programs that provide temporary cash and health benefits to help low-income residents meet their basic needs, including Medicaid, TANF For District Families, SNAP, etc.

Housing assistance and employment and career training programs to support resident’s housing and employment security. These include the Emergency Rental Assistance Program, Permanent Supportive Housing vouchers, Career MAP, the DC Infrastructure Academy, among other programs and services.

Creation of the DC Commission on Poverty to study poverty issues, evaluate poverty reduction initiatives, and make recommendations to the Mayor and the Council.

Dashboard featuring statistics regarding poverty in Washington, DC. Data derived from ACS Poverty Status Variables - Boundaries, which is a layer by Esri and is available on Living Atlas.

Indonesia (DC)Average Monthly Poverty Line per Capita: Riau Islands: Riau Islands data was reported at 290,994.000 IDR in 2012. This records an increase from the previous number of 208,350.000 IDR for 2007. Indonesia (DC)Average Monthly Poverty Line per Capita: Riau Islands: Riau Islands data is updated yearly, averaging 198,185.000 IDR from Dec 2005 (Median) to 2012, with 4 observations. The data reached an all-time high of 290,994.000 IDR in 2012 and a record low of 174,756.000 IDR in 2005. Indonesia (DC)Average Monthly Poverty Line per Capita: Riau Islands: Riau Islands data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE015: Poverty Line: by Regency.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Poverty threatens the overall well-being of individuals and families, limiting access to stable housing, healthy foods, health care, and educational and employment opportunities, among other basic needs.Poverty is associated with a higher risk of adverse health outcomes, including chronic physical and mental illness, lower life expectancy, developmental delays, and others.

Racist policies and practices have contributed to racial economic inequities. Nationally, Black, Indigenous, and people of color experience poverty at higher rates than white Americans, on average.

The District's Response

Boosting assistance programs that provide temporary cash and health benefits to help low-income residents meet their basic needs, including Medicaid, TANF For District Families, SNAP, etc.

Housing assistance and employment and career training programs to support resident’s housing and employment security. These include the Emergency Rental Assistance Program, Permanent Supportive Housing vouchers, Career MAP, the DC Infrastructure Academy, among other programs and services.

Creation of the DC Commission on Poverty to study poverty issues, evaluate poverty reduction initiatives, and make recommendations to the Mayor and the Council.

Gambia GM: (DC)Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data was reported at 10.100 % in 2015. This records a decrease from the previous number of 25.100 % for 2010. Gambia GM: (DC)Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data is updated yearly, averaging 35.200 % from Dec 1998 (Median) to 2015, with 4 observations. The data reached an all-time high of 70.500 % in 1998 and a record low of 10.100 % in 2015. Gambia GM: (DC)Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gambia – Table GM.World Bank: Poverty. Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Gambia GM: (DC)Poverty Gap at $1.90 a Day: 2011 PPP: % data was reported at 2.200 % in 2015. This records a decrease from the previous number of 7.400 % for 2010. Gambia GM: (DC)Poverty Gap at $1.90 a Day: 2011 PPP: % data is updated yearly, averaging 12.550 % from Dec 1998 (Median) to 2015, with 4 observations. The data reached an all-time high of 36.000 % in 1998 and a record low of 2.200 % in 2015. Gambia GM: (DC)Poverty Gap at $1.90 a Day: 2011 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gambia – Table GM.World Bank.WDI: Poverty. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This layer shows Population and Poverty Status. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of people whose income in the past 12 months is below poverty level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2018-2022ACS Table(s): B17017, C17002, DP02, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Gambia GM: (DC)Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 4.630 % in 2015. Gambia GM: (DC)Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 4.630 % from Dec 2015 (Median) to 2015, with 1 observations. Gambia GM: (DC)Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gambia – Table GM.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. 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. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Gambia GM: (DC)Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -0.480 % in 2015. Gambia GM: (DC)Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -0.480 % from Dec 2015 (Median) to 2015, with 1 observations. Gambia GM: (DC)Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gambia – Table GM.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. 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. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Gambia GM: (DC)Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 37.800 % in 2015. This records a decrease from the previous number of 53.000 % for 2010. Gambia GM: (DC)Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 61.300 % from Dec 1998 (Median) to 2015, with 4 observations. The data reached an all-time high of 86.800 % in 1998 and a record low of 37.800 % in 2015. Gambia GM: (DC)Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gambia – Table GM.World Bank: Poverty. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Gambia GM: (DC)Poverty Gap at $5.50 a Day: 2011 PPP: % data was reported at 30.100 % in 2015. This records a decrease from the previous number of 41.000 % for 2010. Gambia GM: (DC)Poverty Gap at $5.50 a Day: 2011 PPP: % data is updated yearly, averaging 47.250 % from Dec 1998 (Median) to 2015, with 4 observations. The data reached an all-time high of 69.800 % in 1998 and a record low of 30.100 % in 2015. Gambia GM: (DC)Poverty Gap at $5.50 a Day: 2011 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Gambia – Table GM.World Bank: Poverty. Poverty gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This layer shows Population and Poverty Status. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of people whose income in the past 12 months is below poverty level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2018-2022ACS Table(s): B17017, C17002, DP02, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

Indonesia (DC)Poverty Severity Index: Riau Islands: Riau Islands data was reported at 0.140 % in 2012. This records a decrease from the previous number of 0.440 % for 2007. Indonesia (DC)Poverty Severity Index: Riau Islands: Riau Islands data is updated yearly, averaging 0.575 % from Dec 2005 (Median) to 2012, with 4 observations. The data reached an all-time high of 1.560 % in 2005 and a record low of 0.140 % in 2012. Indonesia (DC)Poverty Severity Index: Riau Islands: Riau Islands data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE011: Poverty Severity Index: by Regency.

(DC)Average Monthly Poverty Line per Capita: Riau Islands: Riau Islands在2012达290,994.000 IDR，相较于2007的208,350.000 IDR有所增长。(DC)Average Monthly Poverty Line per Capita: Riau Islands: Riau Islands数据按每年更新，2005至2012期间平均值为198,185.000 IDR，共4份观测结果。该数据的历史最高值出现于2012，达290,994.000 IDR，而历史最低值则出现于2005，为174,756.000 IDR。CEIC提供的(DC)Average Monthly Poverty Line per Capita: Riau Islands: Riau Islands数据处于定期更新的状态，数据来源于Central Bureau of Statistics，数据归类于Indonesia Premium Database的Socio and Demographic – Table ID.GAE015: Poverty Line: by Regency。

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction.

In the process leading to its first Poverty Reduction Strategy (that is the National Strategy for Socioeconomic Development, now renamed the National Strategy for Development and Integration), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making.

Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country. They provide an indispensable tool to assist policy-makers in monitoring and targeting social programs. In its first phase (2001-2006), this monitoring system included the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a subsample of LSMS households (in 2003, and 2004), drawing heavily on the 2001 census information.

A poverty profile based on 2002 data showed that some 25 percent of the population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by poor access to basic infrastructure (regular supply of electricity, clean water), education and health services, housing, etc.

The 2005 LSMS was in the field between May and early July, with an additional visit to agricultural households in October, 2005. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National coverage. Domains: Tirana, other urban, rural; Agro-ecological areas (coastal, central, mountain)

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For census purposes, the cities and the villages have been divided into enumeration areas (EAs).

1. Sampling frame

The Enumeration Areas (EA) that make up the sampling frame come from the April 2001 General Census of Population and Housing. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HU), the number of occupied HUs, the number of unoccupied HUs, the number of households, and the population. We are using occupied dwellings and not total number of dwellings since many EAs contain a large number of empty dwellings.

A detailed study of the list of census EAs shows that many have zero population. In order to obtain EAs with the minimum of 50 and the maximum of 120 occupied housing units, the EAs with zero population have been taken off the sampling frame. Since the sizes of the EAs varied from 0 to 395 HUs, the smaller EAs (with less than 50 HU) have been collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) have been split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared. Given that the 2002 LSMS has been conducted less than a year after the April 2001 census, a listing operation to update the sample EAs was not conducted in the field. However, since the level of construction is very high in the city of Tirana and its suburbs, a quick count of the 75 sample EAs selected in Tirana was carried out followed by a listing operation. The check of the listing based on the Census data revealed two types of discrepancies: - HUs had become invalid, i.e. vacant, nonresidential, demolished, seasonally occupied, etc. - Instead of one small building (with one or two HU), a new one with 15 HUs was identified.

During of the listing update process, HUs identified as invalid were taken off the frame. In the case of a new building, these new HUs were entered with a new sequential code. The listing sheets prepared during the listing operation in Tirana, become the sampling frame for the final stage of selection of 12 HU which has to be interviewed. The unit of analysis and the unit of observation is the household. The universe under study consists of all the households in the Republic of Albania. We have used the Housing Unit (defined as the space occupied by one household) as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field.

1. Sample Size

In the LSMS the sample size is 450 EA and in each EA 8 households were selected. So the total sample size of the LSMS is 3600 households. In addition, since a certain level of nonresponse is expected, 4 reserve units were selected in each sample EA.

1. Stratification

The sampling frame has been divided in three regions (strata) 1. Coastal Area 2. Central Area 3. Mountain Area and Tirana (urban and other urban) is consider as a separate strata.

The first three strata were divided into major cities (the most important cities in the region), other urban (the rest of cities in the region), and rural. In each more importance was given to the major cities and rural areas. We have selected 10 EA for each major city and 65 EAs (75 EAs for Mountain Area) for each region. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design.

1. Procedure for the Selection of Housing Units

A fixed number of valid dwelling units (12) was selected systematically and with equal probability from the Listing Form pertaining to Tirana and from the Census forms for the other areas. Once the 12 HUs were selected, 4 of them were chosen at random and kept as reserve units. The selected HUs were numbered within the EA and identified with a circle around the number in the listing form, as well as a circle on the maps. The reserve sample (units 9 to 12) were identified from R1 to R4 during data collection to emphasize the fact that they were reserve units.

Two copies of the sample listing sheets and two copies of maps for each EA were printed. The first copy of the listing sheet and the map were given to the supervisor and included the 12 HU, the second copy was given to the enumerator. The enumerator only received the 8 dwelling units, not the reserve ones. Each time the enumerator needed a reserve HU, he/she had to ask the supervisor and explain the reason why a reserve unit was needed. This process helped determine the reason why reserve units were used and provided more control on their use.

In the field the enumerator registered the occupancy status of every unit: - occupied as principal residence - vacant - under construction (not occupied) - demolished or abandoned (not occupied) - seasonally occupied

In the case that one HU was found to be invalid, the enumerator used the first reserve unit (identified with the code R1). In the case that in one EA more than 4 DU selected were invalid, other units from that EA chosen at random by headquarter (in Tirana) were selected as replacement units to keep the enumerator load constant and maintain a uniform sample size in each EA. Before identifying the invalid HUs, the interviewer had to note the interview status of each visit for all the units for which an interview was attempted, whether these are original units or reserve units. This was done to determine the interview status: interview completed, nonresponse, refusal, etc. In other words, this will allow identifying: the completed interviews (responses obtained), the incomplete but usable ones (responses obtained), the incomplete ones but not usable (nonresponse), the refusals (nonresponse) and the "not at home" (nonresponse). Subsequently, the invalid units identified were substituted with the available reserves, always maintaining the sample of 8 HUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four survey instruments were used to collect information for the 2005 Albania LSMS: a household questionnaire, a diary for recording household food consumption, a community questionnaire, and a price questionnaire.

The household questionnaire included all the core LSMS modules as defined in Grosh and Glewwe (2000)1, plus additional modules on migration, fertility, subjective poverty, agriculture, non-farm enterprises, and social capital. Geographical referencing data on the longitude and latitude of each household were also recorded using portable GPS devices. Geo-referencing will enable a more efficient spatial link among the different surveys of the system, as well as between the survey households and other geo-referenced information.

The choice of the modules was aimed at matching as much as

(DC)Agricultural Offtake: Kerala: Rice: TPDS: Above Poverty Line (APL)在2016-11达12.688Ton th，相较于2016-10的37.216Ton th有所下降。(DC)Agricultural Offtake: Kerala: Rice: TPDS: Above Poverty Line (APL)数据按月度更新，2011-10至2016-11期间平均值为50.429Ton th，共62份观测结果。该数据的历史最高值出现于2014-08，达83.895Ton th，而历史最低值则出现于2016-11，为12.688Ton th。CEIC提供的(DC)Agricultural Offtake: Kerala: Rice: TPDS: Above Poverty Line (APL)数据处于定期更新的状态，数据来源于Department of Food & Public Distribution，数据归类于India Premium Database的Agriculture Sector – Table IN.RII015: Agricultural Offtake under Targeted Public Distribution System (TPDS): Rice: by States 。