In 2023, **** percent of the families with a female householder in the U.S. lived in poverty. While high, it is a significant increase from the 1990 when the poverty rate was **** for female-led households. You can get an overview on the total number of households in the U.S. here.

This report was written in collaboration between the Mayor's Office of Innovation and the Rochester Monroe Anti-Poverty Initiative (RMAPI) and released in December 2019. Executive SummaryThe Rochester Monroe Anti-Poverty Initiative (RMAPI) has selected single female headed households with children as one of its key target populations in which to focus strategy and its next phase of initiatives. This report is intended to provide additional insight on this population to support the next phase of RMAPI’s strategic planning as well as broader advocacy efforts on behalf of this population.

We begin with a brief summary of historic policy and societal factors known to have contributed to the current day inequities, written in collaboration with content experts from RMAPI.

The core of this report is a fact sheet based on analysis of US Census data. Major findings include:

Finding 1: Families headed by unmarried parents are a significant segment of the city population and account for the majority of individuals living below the poverty level in the city.

Finding 2: Unmarried households with children experience lower incomes, lower rates of home ownership, and higher rent burdens compared to their married counterparts

Finding 3: Women and people of color are overrepresented among the heads of unmarried households with children.

Finding 4: Four in ten unmarried householders with children have less than a high school education. Nearly 80 percent of those without a high school education are in poverty.

Finding 5: Unmarried householders with children in poverty are more likely to be disabled or face other common barriers to employment.

Finding 6: The more adults present in unmarried households with children, the less likely that household is to be in poverty. This trend amplifies when considering the number of employed adults.

Finding 7: Unmarried parents under age 40 head the majority of all households with children in Rochester. Younger householders correlate with higher poverty rates regardless of marriage status.

Finding 8: A birth before age 20, being unmarried, and having not completed high school education are three factors that, when compounded, are associated with poor economic outcomes.

Finding 9: The highest densities of unmarried householders with children are clustered in the highest poverty neighborhoods in the city of Rochester

We end with a discussion of the gaps in available data, acknowledging that there is room for further investigation and interpretation, data collection, and insights. We recommend readers to think critically about what is presented and how it might impact their own work in poverty reduction efforts. We present a series of questions that are a jumping off point for new inquiry and reflection. Methodology can be found in the Appendix.

Data Source:2017 Census American Community Survey 5-Year Estimates, Public Microdata SampleData and documentation can be accessed here:https://www.census.gov/programs-surveys/acs/data/pums.html

In 2023, 37.7 percent of the population in Colombia belonging to a family whose head of household was female lived in poverty. Additionally, those belonging to a family whose household head was between 26 and 35 years old had a higher incidence of poverty.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

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In 2019, the average poverty rate in Moroccan households stood at *** percent among households headed by men. In contrast, families with female household heads recorded a poverty rate of *** percent. The poverty rate generally declined in the country from 2001 onwards.

In 1990, 48.1 percent of all Black families with a single mother in the United States lived below the poverty level. In 2023, that figure had decreased to 25.9 percent. This is significantly higher than white households with a single mother. Poverty is the state of one who lacks a certain amount of material possessions or money. Absolute poverty or destitution is inability to afford basic human needs, which commonly includes clean and fresh water, nutrition, health care, education, clothing and shelter.

A more recent web map on this same topic is available for ArcGIS Online subscribers here.This map shows the socioeconomic status of each census tract. Data come from the US Census Bureau's 2011-2015 American Community Survey. Neighborhood Socioeconomic Status, over and above individual socioeconomic status, is a predictor of many health outcomes. The Neighborhood Socioeconomic Status (NSES) Index is on a scale from 0 to 100 with 50 being the national average around 2010. The Index incorporates the following indicators (fields in this layer's attribute table):Median Household Income (from Table B19013)Percent of individuals with income below the Federal Poverty Line (from Table S1701)The educational attainment of adults (age 25+) (from Table B15003)Unemployment Rate (from Table S2301)Percent of households with children under the age of 18 that are "female-headed" (no male present) (from Table B11005)NSES = log(median household income) + (-1.129 * (log(percent of female-headed households))) + (-1.104 * (log(unemployment rate))) + (-1.974 * (log(percent below poverty))) + .451*((high school grads)+(2*(bachelor's degree holders)))To learn more about how the NSES Index was developed, please explore this journal articleMiles, Jeremy and Weden, Margaret; Lavery, Diana; Escarce, José; Kathleen Cagney; Shih, Regina. 2016. “Constructing a Time-Invariant Measure of the Socio-Economic Status of U.S. Census Tracts.” Journal of Urban Health, vol. 93, issue no.1, pp. 213-232. or this PPT presentation presented at the University of Texas at San Antonio's Applied Demography Conference in 2014.

As of 2019, the population mostly affected by poverty in Nigeria was those working exclusively in the agricultural sector. Households with a male household head were much more impacted than those with a female head. For instance, about 58 percent of people belonging to households with a male head working in the agriculture was living below the poverty line. According to national standards, an individual with less than 137.4 thousand Nigerian Naira (roughly 361 U.S. dollars) per year is considered poor. Nationwide, 40.1 percent of population lived in poverty.

Note: Summary statistics at the regional level are calculated considering 32 states both for women- and men-headed households. (XLSX)

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

In 2022, approximately ***** percent of Colombians were living on less than **** U.S. dollars per day, down from **** percent of the country's population in the beginning of the decade. Moreover, it was recently found that the incidence rate of poverty in Colombia is higher in families whose heads of household were women.

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.

This dataset contains measures of socioeconomic and demographic characteristics by US census tract 1990-2010. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.

Poverty in multi-ethnic regions has always been a concern due to its complex factors and persistent nature. Using a sample of 8,482 ethnic majority-headed households and 2,011 ethnic minority-headed households distributed in 200 villages of Wangqing County, China, this study uses hierarchical linear models to examine the factors of income at the household level, the ethnic disparities of the household-level effect, and the contextual effect on household-level outcomes. The findings suggest that, in comparison to the majority group, there exists a smaller income gap between male-headed and female-headed poor households within the minority group. Moreover, the positive impact of participating in off-farm work and receiving welfare payments on the income of poor households is significantly stronger within the minority group. These results not only highlight ethnic disparities in household-level effects but also underscore potential influences of ethnicity on the income dynamics of poor households. The contextual effect demonstrates that modifying the environment of poor households can either enhance or diminish some of the impacts resulting from factors at the household level, thereby facilitating the formulation of more effective targeting strategies at different levels. This study provides an important reference for understanding the ethnic differences of poor households and the mechanism of their income from a multilevel perspective.

Explore World Bank Health, Nutrition and Population Statistics dataset featuring a wide range of indicators such as School enrollment, UHC service coverage index, Fertility rate, and more from countries like Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

School enrollment, tertiary, UHC service coverage index, Wanted fertility rate, People with basic handwashing facilities, urban population, Rural population, AIDS estimated deaths, Domestic private health expenditure, Fertility rate, Domestic general government health expenditure, Age dependency ratio, Postnatal care coverage, People using safely managed drinking water services, Unemployment, Lifetime risk of maternal death, External health expenditure, Population growth, Completeness of birth registration, Urban poverty headcount ratio, Prevalence of undernourishment, People using at least basic sanitation services, Prevalence of current tobacco use, Urban poverty headcount ratio, Tuberculosis treatment success rate, Low-birthweight babies, Female headed households, Completeness of birth registration, Urban population growth, Antiretroviral therapy coverage, Labor force, and more.

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This graph shows the Percentage of households led by a female householder with no spouse present with own children under 18 years living in the household in the U.S. in 2021, by state. In 2021, about 4.24 percent of Californian households were single mother households with at least one child.

Additional information on single mother households and poverty in the United States

For most single mothers a constant battle persists between finding the time and energy to raise their children and the demands of working to supply an income to house and feed their families. The pressures of a single income and the high costs of childcare mean that the risk of poverty for these families is a tragic reality. Comparison of the overall United States poverty rate since 1990 with that of the poverty rate for families with a female householder shows that poverty is much more prevalent in the latter. In 2021, while the overall rate was at 11.6 percent, the rate of poverty for single mother families was 23 percent. Moreover, the degree of fluctuation tends to be lower for single female household families, suggesting the rate of poverty for these groups is less affected by economic conditions.

The sharp rise in the number of children living with a single mother or single father in the United States from 1970 to 2022 suggests more must be done to ensure that families in such situations are able to avoid poverty. Moreover, attention should also be placed on overall racial income inequality given the higher rate of poverty for Hispanic single mother families than their white or Asian counterparts.

Since its recovery of macroeconomic stability in 1991, the Dominican Republic has experienced a period of notable economic growth. Poverty has declined in the 1990s. Nevertheless, a segment of the population-mainly in rural areas-does not seem to have benefited from this growth. Poverty in this country in 1998 is less than that of other countries if one adjusts for the level of economic development. The principal poverty characteristics are the following: Disparity in poverty levels in rural areas relative to the rest of the country. Destitution in the "bateyes," the communities arising near the sugar cane plantations, that are mainly composed of women, children, and the aged. Urban vulnerability to environmental problems while access to basic services is restricted. Vulnerability to natural disasters that destroy the means of production. Poverty is high among children--especially those who have abandoned formal education-female-headed households, and the aged-the latter due to lack of social safety nets and the absence of pension systems. There is a strong correlation between poverty and health indicators like the presence of malnutrition, and poverty and education, and poverty and the absence of basic services. Government transfers and foreign remittances play an important role in reducing poverty.

LC-06: Concentrated DisadvantageBrief description: Proportion of households located in census tracts with a high level of concentrated disadvantage, calculated using five census variablesIndicator category: Community Well-beingIndicator domain: Risk/OutcomeNumerator: Number of households with children less than 18 years of age located in census tracts of high concentrated disadvantageDenominator: Total number of households with children less than 18 years of agePotential modifiers: age, race, ethnicity, gender, geographic locationData source: American Community Survey (ACS)Notes on calculation: Concentrated disadvantage is calculated from five Census variables: 1) Percent of individuals below the poverty line, 2) Percent of individuals on public assistance, 3) Percent female-headed households, 4) Percent unemployed, 5) Percent less than age 18. The percentages of each individual indicator are z-score transformed. A Z-score transformation is achieved by subtracting the mean of the distribution from the variable value and dividing the difference by the standard deviation of the distribution. Z = (score - mean)/standard deviation. The resulting value should be averaged into an overall index of concentrated disadvantage or deprivation.More Information: https://amchp.org/amchp-resource-library/Distinct from similar uploads in that this layer uses 2020 Census tract geography and 2023 5-Year ACS data for calculation. For more information, please contact egis@isd.lacounty.gov.

StoryMap link:https://arcg.is/1OXPW1This dataset contains the Hampton Roads Transportation Planning Organization (HRTPO) 9 Environmental Justice (EJ) Indicators (Carless Households, Cash Public Assistance Households, Disabled Population, Elderly Population, Female Head of Household, Food Stamps/SNAP Household, Limited English Proficiency Population, Minority Population, and Low-Income/Poverty Households) at the Census Block Group level. The U.S. Census data source uses the 2017-2021 ACS 5-Year Estimates. The dataset includes Youth Population, which is not an EJ Indicator but is used in the Transportation Challenges and Strategies Long-Range Transportation Plan (LRTP) report. This data will be used for the HRTPO 2050 LRTP, for planning purposes only.Title VI - Environmental Justice FrameworkApplied to 2050 Long-Range Transportation PlanIntroductionProviding equitable access to transportation is essential for thriving communities. Below are federal regulations to help foster transportation equity.Title VI of the Civil Rights Act prohibits discrimination based on race, color, and national origin in programs and activities receiving federal financial assistance.Environmental Justice (EJ) is the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. The Environmental Justice Executive Order 12898, signed in 1994, reinforces the requirements of Title VI.Transportation-Vulnerability Key IndicatorsThe following transportation-vulnerability key indicators were used to identify individuals or households that may experience varying degrees of disadvantage in transportation accessibility and/or the transportation planning process:MinorityLow-Income HouseholdsHouseholds Receiving Cash Public AssistanceHouseholds Receiving Food StampsCarless HouseholdsDisabled PopulationsElderly PopulationsFemale Heads of HouseholdLimited English Proficiency HouseholdsTransportation-Vulnerable CommunitiesUsing US Census Bureau’s 2017-2021 American Community Survey data, each transportation-vulnerability key indicator was assessed by census block groups, the smallest available geography for the identified key indicators, and compared to regional averages. Any census block group with an average key indicator equal to or higher than the regional average for that indicator is identified as a transportation-vulnerable community.The dataset contains the 9 EJ Indicators used for the HRTPO Title VI/EJ Analysis and the 2050 LRTP. The field names/aliases will change based on what platform the user is viewing the data (e.g., ArcMap, ArcPro, ArcGIS Online, Microsoft Excel, etc.). The suggestion is to view 'Field Alias Names'. To help preserve the field names and descriptions and to help the user understand the data, the following list contains the field names, field alias names, and field descriptions: (EXAMPLE: Field Name = Field Alias Name. Field Description.).OBJECTID = OBJECTID. Unique integer field used to identify rows in tables in a geodatabase uniquely. ESRI ArcMap/ArcPro automatically defines this field.Shape = Shape. The type of shape for the data. In this case, the EJ data are all 2021 Census Block Group (CBG) polygons. ESRI ArcMap/ArcPro automatically defines this field.GEOID = Census GEOID. Census numeric codes that uniquely identify all administrative/legal and statistical geographic areas. In this case, the EJ data are all 2021 CBGs.GEOID_1 = Census GEOID - Joined. Census numeric codes that uniquely identify all administrative/legal and statistical geographic areas. In this case, the EJ data are all 2021 CBGs.Block_Grou = Census Block Group. CBG is a geographical unit used by the U.S. Census Bureau which is between the Census Tract and the Census Block levels.TAZ = Transportation Analysis Zones (TAZ). HRTPO Transportation Analysis Zones (TAZs) that spatially join with the CBGs. Each CBG has a TAZ that intersects/overlays with the HRTPO TAZs.Locality = Locality. Locality name: the dataset includes 16 localities (Cities of Chesapeake, Franklin, Hampton, Newport News, Norfolk, Poquoson, Portsmouth, Suffolk, Virginia Beach, and Williamsburg, and the Counties of Gloucester, Isle of Wight, James City, Southampton, Surry*, and York). The HRTPO/MPO Boundary does not include Surry County, but the data is included for HRPDC/MPA purposes.Total_Popu = Total Population. Census Total Population.Total_Hous = Total Households. Census Total Households.Carless_To = Carless Total. Total Carless Households. Households with no vehicles available.Carless_Re = Carless regional Avg. Carless Households regional average.Carless_BG = Carless BG Avg. Carless Households Census Block Group average.Carless_AB = Carless Above Avg (Yes/No). Carless Households above the regional average. No = Not an EJ Community, Yes = EJ Community.Carless_Nu = Carless Numeric Value (0/1). Carless Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.Cash_Assis = Cash Public Assistance Total. Total Households Receiving Cash Public Assistance (CPA). household that received either cash assistance or in-kind benefits.Cash_Ass_1 = Cash Public Assistance Regional Avg. CPA Households regional average.Cash_Ass_2 = Cash Public Assistance BG Avg. CPA Households Census Block Group average.Cash_Ass_3 = Cash Assistance Above Avg (Yes/No). CPA Households above the regional average. No = Not an EJ Community, Yes = EJ Community.CPA_Num = Cash Public Assistance Numeric Value (0/1). CPA Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.Disability = Disability Total. Total Disabled Populations. non-institutionalized persons identified as having a disability of the following basic areas of functioning - hearing, vision, cognition, and ambulation.Disabili_1 = Disability Regional Avg. Disabled Populations regional average.Disabili_2 = Disability BG Average. Disabled Populations Census Block Group average.Disabili_3 = Disability Above Avg (Yes/No). Disabled Populations above the regional average. No = Not an EJ Community, Yes = EJ Community.Disabili_4 = Disability Numeric Value (0/1). Disabled Populations numerical value. 0 = Not an EJ Community, 1 = EJ Community.Elderly_To = Elderly Total. Total Elderly Populations. People who are aged 65 and older.Elderly_Re = Elderly Region Avg. Elderly Population regional average.Elderly_BG = Elderly BG Avg. Elderly Population Census Block Group avg.Elderly_Ab = Elderly Above Avg (Yes/No). Elderly Population above the regional average. No = Not an EJ Community, Yes = EJ Community.Elderly_Num = Elderly Numeric Value (0/1). Elderly Population numerical value. 0 = Not an EJ Community, 1 = EJ Community.Female_HoH = Female Head of Households Total. Total Female Head of Households. Households where females are the head of households with children present and no husband present.Female_H_1 = Female Head of Households Regional Avg. Female Head of Households regional average.Female_H_2 = Female Head of Households BG Avg. Female Head of Households Census Block Group average.Female_H_3 = Female Head of Households Above Avg (Yes/No). Female Head of Households above the regional average. No = Not an EJ Community, Yes = EJ Community.FemaleHoH_ = Female Head of Households Numeric Value (0/1). Female Head of Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.Food_Stamp = Food Stamps Total. Total Households receiving Food Stamps. Households that received Supplemental Nutrition Assistance Program (SNAP) or Food Stamps.Food_Sta_1 = Food Stamps Region Avg. Food Stamps Households regional average.Food_Sta_2 = Food Stamps BG Avg. Food Stamps Households Census Block Group average.Food_Sta_3 = Food Stamps Above Avg (Yes/No). Food Stamps Households above the regional average. No = Not an EJ Community, Yes = EJ Community.FoodStamps = Food Stamps Numeric Value (0/1). Food Stamps Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.Limited_En = Limited English Proficiency Total. Total Limited English Proficiency (LEP) Populations. Population 5 years or over who speak English less than "very well".Limited_1 = Limited English Proficiency Regional Avg. LEP Population regional average.Limited_2 = Limited English Proficiency BG Avg. LEP Populations Census Block group average.Limited_3 = Limited English Proficiency Above Avg (Yes/No). LEP Population above the regional average. No = Not an EJ Community, Yes = EJ Community.LEP_Num = Limited English Proficiency Numeric Value (0/1). LEP Population numerical value. 0 = Not an EJ Community, 1 = EJ Community.Minority_T = Minority Total. Total Minority Populations. A person who is Black, Hispanic, American Indian, Alaskan Native or Asian American.Minority_R = Minority Regional Average. Minority Population regional average.Minority_B = Minority BG Average. Minority Population Census Block Group average.Minority_A = Minority Above Average (Yes/No). Minority Population above the regional average. No = Not an EJ Community, Yes = EJ Community.Minority_N = Minority Numeric Value (0/1). Minority Population numerical value. 0 = Not an EJ Community, 1 = EJ Community.Total_Ho_1 = Total Households for Poverty. Census Total Low-Income/Poverty Households.Poverty_To = Poverty Total. Total Poverty Households. A low-income household is one who income is low, relative to other households of the same size.Poverty_Re = Poverty Regional Avg. Poverty Households regional average.Poverty_BG = Poverty BG Avg. Poverty households Census Block Group average.Poverty_Ab = Poverty Above Avg (Yes/No). Poverty Households above the regional average. No = Not an EJ Community, Yes = EJ Community.Poverty_Num = Poverty Numeric Value (0/1). Poverty Households numerical value. 0 = Not an EJ Community, 1 = EJ Community.EJCommunit = EJ Community (Yes/No). The Census Block Group contains at least one EJ Community (>=1 = Yes). If the Census Block Group does not

This paper presents data collected in July 2016 to assess the consumption patterns and dietary quality among vulnerable urban consumers at the Base of Pyramid (BoP). The data was collected within the project ‘Making Value Chains Work for Food and Nutrition Security of Vulnerable Populations in East Africa’ which was funded by the German Federal Ministry for Economic Cooperation and Development (BMZ). The project was led by the Bioversity International and the International Center for Tropical Agriculture and implemented in partnership with KALRO, NARO, Goettingen University and UHOH. The project was under the CGIAR flagship program “Food Systems for Healthier Diets” under the Research Program on Agriculture for Nutrition and Health (A4NH) A cross-sectional survey was conducted to collect data with the goal of assessing critical and sensible ways in which market systems work to improve the consumption of more diverse, safe and nutrient-dense foods. The questionnaire had five sections. Section A captured the geographical location of the households and interview day details. Section B captured household demographic details. Section C focused on household nutritious porridge consumption and preferences. In Section D, household access to nutrition information was captured while Section E details household assets and their nominal values. The anonymized data is arranged into six files; 01Identifier16 file contains all the data from section A. Similarly, household demographic information is in file 02Demography16. 03Consumption16, 04Flourattributes16, 05Assets16 and 06Text16 contain household nutritious porridge consumption and sources of the flour, important porridge flour quality attributes, household assets and their values, and crosscutting general household level data respectively. Metodology:Data collection site The data was collected in Nairobi, Kenya and Kampala, Uganda. Nairobi is Kenya’s capital city. Projections by the Kenya Bureau of Statistics (KNBS) indicate that the county’s population will rise from 3.14 million recorded in the 2009 census to 5.96 million by 2022 with an inter-censual growth rate of 3.8 per cent (County Government of Nairobi, 2018). The city has the largest slum in East and Central Africa; Kibera slum, and others such as Kawangware, Mathare, Kangemi, Korogocho, Majengo, Kitui village and Kiambiu. Poverty levels are high in the city with the most affected groups being the unemployed youth, women, persons with disabilities, female and child-headed households, slum dwellers and the aged (County Government of Nairobi, 2018). Poor access to basic infrastructure is also a common characteristic of the many slums in Nairobi. On the other hand, Kampala is Uganda's administrative and commercial capital city with a population of approximately 1.2 million inhabitants (Robinah et al., 2013). Kampala is also a rapidly growing city and is home to Slums such as Bwaise, Katwe, Kisenyi, Kibuli, Katanga, Nabulabye, Naguru2 and Nsambya (Association of Physicians of Uganda, 2018). In Nairobi, Kibera, Embakasi, Mathare and Dagoreti slums were selected as the study site while Bwaise, Kawempe, Kamwokya and Kasubi parishes were the study areas in KampalaA multi-stage sampling strategy was used to select respondents. First, we used the national statistics (Emwanu et al., 2004; KNBS, 2015) and information from the administrative offices to identify four urban BoP locations with the highest poverty levels in each of the two cities. In Nairobi, the selected locations were Kibera, Embakasi, Mathare and Dagoreti while in Kampala data collection was done in Bwaise, Kawempe, Kamwokya and Kasubi parishes. Second, households from these locations were randomly selected, using a systematic random sampling technique. We interviewed a total of 600 households, 300 from Kenya and 300 from Uganda. Survey preparation involved several activities. First, survey tool development, design and programming into SurveyCTO. Second, enumerator recruitment and training. We selected enumerators from a pool of recent graduate applicants with sufficient experience in carrying out household surveys and a good knowledge of the two cities (Nairobi and Kampala). The selected enumerators were then intensively trained for 3 days (11th – 13th July 2016). The training covered each question in the questionnaire, the purpose of each question and a suitable means of handling each question. Enumerators were additionally trained on Computer Aided Personal Interview (CAPI) tools and using tablets in data collection. Prior to the actual fieldwork, the teams held a pretest of the survey in non-sampled villages in Nairobi and Kampala. Actual data collection took 15 days (16th – 30th July 2016) under the guidance of team leaders in collaboration with local authorities and village elders. During the survey, a research associate from the Bioversity International and the International Center for Tropical Agriculture checked for inconsistencies, patterns...