This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

The Social Policy Simulation Database/Model (SPSD/M) is a tool designed to analyze the financial interactions between governments and individuals in Canada. It is used to evaluate the effects of the tax and transfer system on costs and income redistribution. The SPSD/M has four basic components: a database (the SPSD), a model (the SPSM, which includes a set of simulation algorithms), software for data extraction and data reporting, and user documentation. The SPSD/M is designed to be used in the analysis of the financial interactions between governments and individuals in Canada. 1. The SPSD/M is a representative and non-confidential statistical database of individuals in the context of their families, with sufficient information on each individual to enable the calculation of taxes paid to the government as well as amounts remitted by governments.2. SPSM is a static accounting model that processes every individual and family in the SPSD, calculates taxes and transfers using algorithms that simulate adopted or proposed programs, and reports on results. A sophisticated software environment gives the user a great deal of influence over the model's inputs and outputs, enabling him or her to modify existing programs or examine entirely new projects. Inside MSPS, there are two models, configured as two separate computer programs.2a.The central program, SPSM, is a microsimulation model that calculates taxes and transfers for individuals and families. These calculations are performed for everyone in the SPSD, and the results are then aggregated to produce estimates. The SPSM is a static incidence model and is not intended to simulate how an individual's behavior is likely to change in response to various policy options. The MSPS includes software that enables the user to perform summation and extraction operations on the information contained in the database. 2b. The consumption tax model (COMTAX) is a model based on macro-economic input-output data. This model is not part of the current version of SPSD/M, but the results obtained with it are. COMTAX estimates federal and provincial sales taxes and equivalent consumption taxes by province, household, expenditure category and tax type. This model is necessary because many consumption taxes are levied at various stages of production, not at the retail stage. The rates calculated by the COMTAX model can be used as input parameters to the SPSM to produce estimates of consumption taxes paid, directly and indirectly, by any given household. 3. Data extraction and reporting software are configured as functions that are accessed via the model. They enable the user to produce formatted output data and perform specific types of analysis. 4. The user documentation is voluminous and comprehensive. It is divided into three manuals containing a number of guides. There are also two ways to run SPSM: using the Visual SPSM interface or the Classic SPSM mode. 1. SPSM Visual: The SPSM Visual interface allows users to modify model parameters, run SPSM simulations and examine output. 2.SPSM Classic: SPSM can also be run from the command interpreter (cmd). For current data from the Social Policy Simulation Database/Model, see Statistics Canada

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

This table contains data on household income and poverty status from the American Community Survey 2006-2010 database for states. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10INCSTMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

Despite scholarly consensus on the harmful effects of economic sanctions on civilians, there is little micro-level empirical research on how and to what extent economic sanctions affect the food consumption of citizens in sanctioned countries. One of the methodological barriers to studying the micro-level dynamics of sanctions is the limited availability of reliable data in sanctioned countries, which are often governed by authoritarian leaders. Our study leverages an original dataset comprising approximately 1 million observations related to the income and expenses of Iranian households from 1991 to 2021. We assess the impact of economic sanctions on individual food consumption at the national level and among distinct demographic segments. To illustrate how and to what extent international sanctions affected citizens' eating habits, we pay particular attention to 2012-2015 and 2018-2021, when Iran was subject to the most severe sanctions. Our findings demonstrate that while all segments of society feel the effects of economic coercion, low-income citizens' food consumption is more likely to deteriorate due to sanctions. Nonetheless, the geographical impact of sanctions presents a mixed picture, with rural and urban areas each exhibiting specific vulnerabilities to certain food items. We discuss the implications of our findings for sanctions policy and human rights.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most States are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, and municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four States (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their States. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The 2010 Census boundaries for counties and equivalent entities are as of January 1, 2010, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS). This table contains data on household income and poverty status from the American Community Survey 2006-2010 database for counties. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables). The name for table 'ACS10INCCNTYMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

This table contains data on household income and poverty status from the American Community Survey 2006-2010 database for block groups. The American Community Survey (ACS) is a household survey conducted by the U.S. Census Bureau that currently has an annual sample size of about 3.5 million addresses. ACS estimates provides communities with the current information they need to plan investments and services. Information from the survey generates estimates that help determine how more than $400 billion in federal and state funds are distributed annually. Each year the survey produces data that cover the periods of 1-year, 3-year, and 5-year estimates for geographic areas in the United States and Puerto Rico, ranging from neighborhoods to Congressional districts to the entire nation. This table also has a companion table (Same table name with MOE Suffix) with the margin of error (MOE) values for each estimated element. MOE is expressed as a measure value for each estimated element. So a value of 25 and an MOE of 5 means 25 +/- 5 (or statistical certainty between 20 and 30). There are also special cases of MOE. An MOE of -1 means the associated estimates do not have a measured error. An MOE of 0 means that error calculation is not appropriate for the associated value. An MOE of 109 is set whenever an estimate value is 0. The MOEs of aggregated elements and percentages must be calculated. This process means using standard error calculations as described in "American Community Survey Multiyear Accuracy of the Data (3-year 2008-2010 and 5-year 2006-2010)". Also, following Census guidelines, aggregated MOEs do not use more than 1 0-element MOE (109) to prevent over estimation of the error. Due to the complexity of the calculations, some percentage MOEs cannot be calculated (these are set to null in the summary-level MOE tables).

The name for table 'ACS10INCBGMOE' was added as a prefix to all field names imported from that table. Be sure to turn off 'Show Field Aliases' to see complete field names in the Attribute Table of this feature layer. This can be done in the 'Table Options' drop-down menu in the Attribute Table or with key sequence '[CTRL]+[SHIFT]+N'. Due to database restrictions, the prefix may have been abbreviated if the field name exceded the maximum allowed characters.

Abstract

The COVID-19 pandemic is significantly having short term and long term impact on Burkinabe households’ welfare, impacting households through at least three broad channels: (i) the income/employment channel, which includes both labor and non-labor income, (ii) the price channel, and (iii) the long-term human capital channel. Most of these impacts are related to the direct health effect, but also to the containment measures that systematically altered socio-economic activities, resulting in a reduction of income across the board. Due to the urgent need for timely data and the limits on face-to-face surveys, the World Bank and the National Institute of Statistics and Demography (INSD) decided to implement a high-frequency phone survey of national households (HFPS) (https://microdata.worldbank.org/index.php/catalog/3768) to monitor the effects of COVID-19 on households, leveraging the available household phone number in the 2018/19 Enquete Harmonisée sur les Conditions de Vie des Ménages (EHCVM). In Burkina Faso, the forcibly displaced persons (FDP) include both refugees and internal displaced population. For security related issues, FDPs are predominantly internal displaced people (IDPs). According to recent studies, the number of internally displaced people soared from 87,000 in January 2019 to over 1 million in August 2020, an increase of more than 1000 per cent (Conseil National de Secours d'Urgence et de Réhabilitation – CONASUR, 2020). The unprecedented levels of displacement occurred as the coronavirus pandemic worsens an already critical humanitarian crisis in the violence-stricken country. This critical situation calls for the need of timely data and analysis especially during a pandemic for this vulnerable group in order to better inform policy and targeting programs. Given the mutual interest of the INSD, WB-UNHCR Joint Data Center on Forced Displacement (JDC), UNHCR, and World Bank, decision was made to further expand the sample of the high-frequency phone survey of national households (HFPS) to include IDPs for a total of three consecutive rounds. The core survey questionnaire of the Burkina Faso High Frequency Phone Survey on IDPs (BFA HFPS-IDP) is designed to cover important and relevant topics like employment, access to basic services and items, and non-labor sources of income. The core questionnaire is complemented by questions on selected topics that rotate each month, including knowledge of Covid-19 spread, social distancing and behavior, coping mechanisms to shocks, fragility, conflict and violence. Selected topics may be investigated more in detail in specific rounds.

The BFA HFPS-IDP is fielded alongside the Burkina Faso Covid-19 High Frequency Phone Survey of national households. Rounds 1, 2 and 3 of data collection for the HFPS-IDP occur simultaneously with round 9, 10 and 11 of the national HFPS operation, respectively.

Geographic coverage

The survey covers households from 9 of the 13 regions of Burkina Faso. These regions are: Boucle de Mouhoun, Cascades, Centre-Est, Centre-Nord, Est, Hauts-Bassins, Nord, Plateau Central, and Sahel.

Analysis unit

• Households
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The IDP sample is drawn from an IDP database named CONASUR database which serves as the sampling frame. The CONASUR has been developed and supported by the government of Burkina Faso with the technical and financial support of development partners, including UNHCR, IOM and OCHA. The CONASUR database is updated regularly, and has exhaustive list of refugees and IDPs, along with few socio-demographic characteristics, as well as information on the phone numbers of households. The sample is drawn from the 9 regions (out of 12) where the presence of IDPs is more relevant: Boucle du Mouhoun, Cascades, Centre-Est, Centre-Nord, Est, Hauts-Bassins, Nord, Plateau Central, Sahel. It is important to note that the BFA HFPS-IDPs is representative of households that have access to phones. Taken that into consideration, a key concern is the bias introduced by sampling households with at least a phone number, as phone penetration in some regions/areas might be limited. However, according to data from the CONASUR database, the percentage of households with at least one phone number is very high, accounting for above the 74% in all the sampled regions. To account for non-response and attrition, 1500 households were selected in baseline round of the HFS. 1,166 households were fully interviewed during the first round of interviews. The final successful sample have been contacted in subsequent rounds of the survey.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

ROUND 1: Household Respondent’s information; Access to Basic Services; Employment and revenues; Food Security and Other revenues. ROUND 2: Household Respondent’s information; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Covid-19 Testing and Vaccination; Access to Basic Services; Credit; Employment and revenue (with a focus on livestock activities); Food Security; Other revenues; Shocks; Concerns regarding the impact of COVID-19 on personal health and financial wealth of the household; Fragility, Conflict and Violence. ROUND 3: Household Respondent’s information; Early Child Development; Access to Basic Services; Employment and revenue (with a focus on agricultural activities); Food Security; Other revenues; Concerns regarding the current situation; Social Safety Nets. All the interview materials were translated in French for the INSD. The questionnaire was administered in local languages with about varying length (about 25 minutes).

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSD with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

BASELINE (ROUND 1): All 1500 households were called in the baseline round of the phone survey. 73.75 percent of sampled households were successfully contacted. Of those contacted, 1,156 households were fully interviewed. These 1,156 households constitute the final successful sample and will be contacted in subsequent rounds of the survey.

ROUND 2: Interviewers attempted to contact and interview all 1,156 households that were successfully interviewed in the Round 1 of the BFA COVID-19 HFPS. 1,114 households (96.3% of the 1,156 attempted) were contacted and 1,112 (96.1%) were successfully interviewed in the second round. Of those contacted, 2 households did not answer due to a language barrier.

ROUND 3: Interviewers attempted to contact and interview all 1,112 households that were successfully interviewed in the Round 2 of the BFA COVID-19 HFPS. 1,051 households (94.53% of the 1,112 attempted) were contacted and 1,048 (94.24%) were successfully interviewed in the third round. Of those contacted, 1 household refused the interview and 2 were only partially interviewed.

RESPONDENTS: Each round of the Burkina Faso COVID-19 HFPS has ONE RESPONDENT per household. The respondent was the household head or a knowledgeable adult household member. The respondent must be a member of the household. Unlike many other household surveys, interviewers were not expected to seek out other household members to provide their own information. The respondent may still consult with other household members as needed to respond to the questions, including to provide all the necessary information on each household member.

Interviewers were instructed to make every effort to reach the same respondent in subsequent rounds of the survey, in order to maintain the consistency of the information collected. However, in cases where the previous respondent was not available, interviewers would identify another knowledgeable adult household member to interview.

Data & Code will be made available by August 25th. Article Abstract Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100-million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (p = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities. Replication Data We include anonymized household survey data for replication of our analysis. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. This data can be found in the hh_data folder. Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in lsms_data folder). Finally, our data replication includes several models outputs, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our main_analysis.Rmd script. Replication Code We provide replication code in the form of an R Markdown (.Rmd) file. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article.

The data contains a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. The datasets are stored in a relational database format in Person, Household, and Activity-travel tables. The Person table contains the synthetic agents their socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. The Household table stores agets' household attributes such as household type, size, number of children, and number of cars. The Activity-travel table contains daily activity schedules of agents, i.e., where and when they do certain activities (work, home, school, and other) and how they travel between them (walk, bike, car, and public transport).

As of 2025, approximately 42 percent of consumers in the United States with over 50k$ household income considered it important for the food to have natural ingredients. A high percentage of pet owners also found the price important factors to keep in mind when making a purchasing decision.

Current policies to reduce greenhouse gas (GHG) emissions and increase adaptation and mitigation funding are insufficient to limit global temperature rise to 1.5°C. It is clear that further action is needed to avoid the worst impacts of climate change and achieve a just climate future. Here, we offer a new perspective on emissions responsibility and climate finance by conducting an environmentally extended input output analysis that links 30 years (1990–2019) of United States (U.S.) household-level income data to the emissions generated in creating that income. To do this we draw on over 2.8 billion inter-sectoral transfers from the Eora MRIO database to calculate both supplier- and producer-based GHG emissions intensities and connect these with detailed income and demographic data for over 5 million U.S. individuals in the IPUMS Current Population Survey. We find significant and growing emissions inequality that cuts across economic and racial lines. In 2019, fully 40% of total U.S. emissions were associated with income flows to the highest earning 10% of households. Among the highest earning 1% of households (whose income is linked to 15–17% of national emissions) investment holdings account for 38–43% of their emissions. Even when allowing for a considerable range of investment strategies, passive income accruing to this group is a major factor shaping the U.S. emissions distribution. Results suggest an alternative income or shareholder-based carbon tax, focused on investments, may have equity advantages over traditional consumer-facing cap-and-trade or carbon tax options and be a useful policy tool to encourage decarbonization while raising revenue for climate finance.