About this dataset

Context

The dataset tabulates the population of Cape Vincent town by race. It includes the population of Cape Vincent town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cape Vincent town across relevant racial categories.

Key observations

The percent distribution of Cape Vincent town population by race (across all racial categories recognized by the U.S. Census Bureau): 77.08% are white, 11.54% are Black or African American, 0.55% are American Indian and Alaska Native, 1.10% are Asian, 3.31% are some other race and 6.42% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cape Vincent town
• Population: The population of the racial category (excluding ethnicity) in the Cape Vincent town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Vincent town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Vincent town Population by Race & Ethnicity. You can refer the same here

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

About this dataset

Context

The dataset tabulates the population of Cape Elizabeth town by race. It includes the population of Cape Elizabeth town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cape Elizabeth town across relevant racial categories.

Key observations

The percent distribution of Cape Elizabeth town population by race (across all racial categories recognized by the U.S. Census Bureau): 96.16% are white, 0.13% are Black or African American, 0.07% are American Indian and Alaska Native, 0.53% are Asian, 0.23% are some other race and 2.88% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cape Elizabeth town
• Population: The population of the racial category (excluding ethnicity) in the Cape Elizabeth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Elizabeth town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Elizabeth town Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Cape May County, NJ (B03002011E034009) from 2009 to 2023 about Cape May County, NJ; Ocean City; NJ; non-hispanic; estimate; persons; 5-year; population; and USA.

As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

As of 2022, South Africa's population increased and counted approximately 60.6 million inhabitants in total, of which the majority (roughly 49.1 million) were Black Africans. Individuals with an Indian or Asian background formed the smallest population group, counting approximately 1.56 million people overall. Looking at the population from a regional perspective, Gauteng (includes Johannesburg) is the smallest province of South Africa, though highly urbanized with a population of nearly 16 million people.

Increase in number of households

The total number of households increased annually between 2002 and 2022. Between this period, the number of households in South Africa grew by approximately 65 percent. Furthermore, households comprising two to three members were more common in urban areas (39.2 percent) than they were in rural areas (30.6 percent). Households with six or more people, on the other hand, amounted to 19.3 percent in rural areas, being roughly twice as common as those in urban areas.

Main sources of income

The majority of the households in South Africa had salaries or grants as a main source of income in 2019. Roughly 10.7 million drew their income from regular wages, whereas 7.9 million households received social grants paid by the government for citizens in need of state support.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Cape Vincent town by race. It includes the distribution of the Non-Hispanic population of Cape Vincent town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Cape Vincent town across relevant racial categories.

Key observations

Of the Non-Hispanic population in Cape Vincent town, the largest racial group is White alone with a population of 1,957 (82.64% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Cape Vincent town
• Population: The population of the racial category (for Non-Hispanic) in the Cape Vincent town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Vincent town total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Vincent town Population by Race & Ethnicity. You can refer the same here

Abstract

A tender was posted, by the Cape Town City Council, in November 2005 for a socio-economic survey and two focus groups to be conducted in both Khayelitsha and Mitchell's Plain. This tender was awarded to the Unit for Religion and Development at the University of Stellenbosch. The purpose was to update the 2001 Census information as well as to identify key priority issues and needs to inform integrated planning for the areas. In addition, the survey was intended to assess the impact of the Urban Renewal Programme in the respective communities. The objectives of the survey and focus groups were as follows:

• To evaluate the Urban Renewal Programme in the nodes of Khayelitsha and Mitchell’s Plain in order to improve the programme outcomes and the communication thereof;

• To develop a demographic and socio-economic profile of the community in terms of household size and composition, education, income and work status. A socio-economic and demographic profile is important in the identification of community needs to inform planning;

• To measure the communities’ perceptions on the value and importance of various services as well as their level of satisfaction with the delivery of these and other services;

• To identify the key needs of the respective communities in order to inform the City on appropriate investment in facilities, infrastructure and services.

Geographic coverage

Two renewal nodes in the Western Cape: Khayelitsha and Mitchell's Plain

Analysis unit

Households and individuals

Universe

All households and de jure household members within Khayelitsha or Mitchell's Plain.

Kind of data

Sample survey data

Sampling procedure

The survey is a stratified sample of 1 000 households from the study area. The sample was stratified on two levels: first, according to the number of households of the two geographical areas in the study area; and second, according to the number of formal and informal dwelling units in each geographical area (Mitchell’s Plain and Khayelitsha).

Regarding the first level of stratification by the number of households for each nodal area, a sample was selected totalling 453 households for the Mitchell’s Plain area and 547 for Khayelitsha. The second level of stratification by dwelling unit type was done within each nodal area, for Mitchell’s Plain totalling 12 informal dwelling units and 441 formal dwelling units, and for Khayelitsha totalling 311 informal dwelling units and 236 formal dwelling units. Formal and informal households were randomly selected from a small area layer (SAL) data set. This data set was created by combining all enumerated areas (EAs) with a population of less than 500 with adjacent EAs within the same sub-place by Statistics South Africa. Assigned to the SAL are the elected datasets from the 2001 Census, one of which is housing type. Because of the small sample size, comparison between geographic areas and/or different dwelling units within the areas may not be statistically significant.

Mode of data collection

Face-to-face [f2f]

2021 Ethekwini Metro Male and Female Population Demographics Estimates by Stats SA

This statistic shows the total population of South Africa from 2013 to 2023 by gender. In 2023, South Africa's female population amounted to approximately 32.46 million, while the male population amounted to approximately 30.75 million inhabitants.

About this dataset

Context

The dataset tabulates the population of Cape Elizabeth town by race. It includes the population of Cape Elizabeth town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cape Elizabeth town across relevant racial categories.

Key observations

The percent distribution of Cape Elizabeth town population by race (across all racial categories recognized by the U.S. Census Bureau): 95.83% are white, 2.02% are Asian and 2.15% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cape Elizabeth town
• Population: The population of the racial category (excluding ethnicity) in the Cape Elizabeth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Elizabeth town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Elizabeth town Population by Race & Ethnicity. You can refer the same here

In the second quarter of 2024, the unemployment rate among Black South Africans was 36.9 percent, marking a year-on-year change of 0.8 percent compared to the second quarter of 2023. On the other hand, the unemployment rate among white South Africans was 7.9 percent in the second quarter of 2024, with a 0.5 percent year-on-year change. Unemployment prevalent among youth and women The unemployment rate is the share of the labor force population that is unemployed, while the labor force includes individuals who are employed as well as those who are unemployed but looking for work. South Africa is struggling to absorb its youth into the job market. For instance, the unemployment rate among young South Africans aged 15-24 years reached a staggering 60.7 percent in the second quarter of 2023. Furthermore, women had higher unemployment rates than men. Since the start of 2016, the unemployment rate of women has been consistently more than that of men, reaching close to 36 percent compared to 30 percent, respectively. A new minimum wage and most paying jobs      In South Africa, a new minimum hourly wage went into effect on March 1, 2022. The minimum salary reached 23.19 South African rand per hour (1.44 U.S. dollars per hour), up from 21.69 South African rand per hour (1.35 U.S. dollars per hour) in 2021. In addition, the preponderance of employed South Africans worked between 40 and 45 hours weekly in 2021. Individuals holding Executive Management and Change Management jobs were the highest paid in the country, with salaries averaging 74,000 U.S. dollars per year.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Cape Elizabeth town by race. It includes the distribution of the Non-Hispanic population of Cape Elizabeth town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Cape Elizabeth town across relevant racial categories.

Key observations

Of the Non-Hispanic population in Cape Elizabeth town, the largest racial group is White alone with a population of 9,107 (97.48% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Cape Elizabeth town
• Population: The population of the racial category (for Non-Hispanic) in the Cape Elizabeth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Elizabeth town total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Elizabeth town Population by Race & Ethnicity. You can refer the same here

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the Khayelitsha and Mitchell's Plain areas of Cape Town, South Africa.

Analysis unit

The unit of analysis for this survey includes households and individuals.

Universe

The survey covers the African and Coloured populations of the Khayelitsha and Mitchell's Plain areas of Cape Town.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to represent all adults (18 years of age and older) in the Mitchell’s Plain Magisterial district. As discussed above, the most cost-efficient method of interviewing residents of such a large area is to use a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For our clusters of households, we relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. These Enumerator Areas are neighbourhoods of roughly 50 to 200 households. They are drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell’s Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS). The measure of size being the number of households in each Enumerator Area as measured by the 1996 Population Census. This method was chosen as it provides the most efficient way to obtain equal subsample sizes across two stages of selection, i.e. we are able to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available through this site under Other Study Materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire: Was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter a separate set of questions covering education, health and work status was included.

The adult questionnaire: Was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.

IntroductionPolyhydramnios is associated with both maternal and fetal adverse outcomes. Idiopathic polyhydramnios, regardless of its severity category, was considered not to be associated with an increase in adverse outcomes. In contrast, when conditions such as congenital and chromosomal abnormalities or diabetes mellitus are detected, neonatal and maternal adverse outcomes can be up to five times higher. We thus aimed to document the outcomes of patients with mild, moderate and severe polyhydramnios according to whether an underlying cause was found and which management protocol was followed.MethodologyWe conducted a retrospective cohort study of all patients with polyhydramnios on ultrasound examination at our secondary hospital between January 1, 2018 and December 31, 2020. Hospital folders were reviewed. We recorded demographic data, information on underlying causes, management and outcomes. We summarised categorical variables using count (percentage). We tested the association between categorical variables using the chi-square test. Statistical significance was set at p < 0.05.ResultsA total of 136 patients with polyhydramnios (80 mild, 42 moderate, and 14 severe) were included. Most cases of polyhydramnios were idiopathic regardless of category [81.2% (65/80), 78% (32/42) and 78% (11/14) in the mild, moderate and severe groups, respectively]. The likelihood of occurrence of the composite adverse outcome, was higher with increasing severity of polyhydramnios, with 6.75%. 19.05%, and 35.71% in the mild, moderate and severe groups, respectively, having the composite adverse outcome (p =  0.01). Elective delivery before 40 weeks’ gestation for polyhydramnios in patients with idiopathic polyhydramnios was associated with a significant reduction in the occurrence of the composite adverse outcome compared to awaiting spontaneous labor (3.77% versus 15.79%, p =  0.036),.ConclusionsAdverse outcomes were related to severity of the polyhydramnios and were significantly lower in the mild compared to the moderate and severe groups, with the rate of adverse outcome in the mild group comparable to that of the general population. Early delivery before 40 weeks gestation may be associated with benefit in moderate and severe groups of polyhydramnios.

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of black households in the rural Eastern Cape and black and colored households in urban Western Cape.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or colored EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Stats SA) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Colored)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban colored (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban colored households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least

Island populations provide natural laboratories for studying key contributors to evolutionary change, including natural selection, population size, and the colonization of new environments. The demographic histories of island populations can be reconstructed from patterns of genetic diversity. House mice (Mus musculus) inhabit islands throughout the globe, making them an attractive system for studying island colonization from a genetic perspective. Gough Island, in the central South Atlantic Ocean, is one of the remotest islands in the world. House mice were introduced to Gough Island by sealers during the 19th century, and display unusual phenotypes, including exceptionally large body size and carnivorous feeding behavior. We describe genetic variation in Gough Island mice using mitochondrial sequences, nuclear sequences, and microsatellites. Phylogenetic analysis of mitochondrial sequences suggested that Gough Island mice belong to Mus musculus domesticus, with the maternal lineage possibly originating in England or France. Cluster analyses of microsatellites revealed genetic membership for Gough Island mice in multiple coastal populations in Western Europe, suggesting admixed ancestry. Gough Island mice showed substantial reductions in mitochondrial and nuclear sequence variation and weak reductions in microsatellite diversity compared with Western European populations, consistent with a population bottleneck. Approximate Bayesian Computation (ABC) estimated that mice recently colonized Gough Island (~100 years ago) and experienced a 98% reduction in population size followed by a rapid expansion. Our results indicate that the unusual phenotypes of Gough Island mice evolved rapidly, positioning these mice as useful models for understanding rapid phenotypic evolution.

Demographic characteristics of the Bellville South cohort, by the training and test datasets.

About this dataset

Context

The dataset tabulates the Cape Vincent town Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Cape Vincent town, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Cape Vincent town.

Key observations

Among the Hispanic population in Cape Vincent town, regardless of the race, the largest group is of Other Hispanic or Latino origin, with a population of 86 (50.29% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Cape Vincent town
• Population: The population of the specific origin for Hispanic or Latino population in the Cape Vincent town is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Cape Vincent town total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Vincent town Population by Race & Ethnicity. You can refer the same here