The share of the indigenous population in Panama living in poverty reached 47.9 percent in 2023. In comparison to the previous year recorded, this share increased by about 0.5 percentage points. The share of indigenous people living in extreme poverty in Latin America stood at 17 percent that year.

Abstract

Objectives of the ENV 2008

• To assess the impact of economic and social policies on the well-being of citizens.
• To estimate the levels of poverty and inequality in the population, and their determinants, in order to formulate public policies, and to design strategies and programmes to address these problems.
• Establish the changes that have occurred in living conditions, income, expenditure and the different social indicators of the population in relation to the years 1997 and 2003.
• Generate information to update the instruments for the prioritisation and targeting of public spending.

Geographic coverage

National coverage. Domains: Urban/rural; Panama City; Rest of Panama District; San Miguelito; West Panama; East Panama; Colon; Cocle; Herrera; Los Santos; Veraguas; Bocas del Toro; Chiriqui; Darien; Indigenous Area

Analysis unit

Households, Individuals, Community

Universe

The living standards survey considered as the study universe the total population residing in the country's occupied private dwellings, according to the 2000 Population and Housing Census. The universe, according to the objective of the research, was divided into a non-indigenous sub-universe and an indigenous sub-universe, the latter consisting of the country's comarcas and the indigenous rural areas outside the comarcal areas, which maintain their socio-cultural patterns.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE FRAME

Based on the census information, as well as the Census 2000 cartography, the sampling frame consisted of the census segments of the country.

PRIMARY SAMPLING UNITS

The constitution of the primary sampling units arises from the application of an association rule, which would allow at the moment of selection the union of the census segments, having a size of 20 or more private dwellings, both in the non-indigenous and indigenous universe. The secondary sampling units are the private dwellings in the country, registering the entire population residing in the selected occupied dwellings.

The primary sampling units, once selected, were sent to the Cartography section of the Directorate of Statistics and Census, in order to proceed with the cartographic update, drawing up a list or register of dwellings for each of the units, which would allow the second stage sample to be obtained; in other words, the dwellings that would be investigated in the survey.

DOMAIN OF STUDY

Taking the 1997 and 2003 studies as a reference, and with the purpose of making the results of the research comparative, it was determined that the study domains, for which reliable estimates are desired, would be represented in the first instance at the level of the Republic by urban and rural area; non-indigenous sample by area and indigenous sample. The eight provinces of the country also constitute study domains.

The province of Panama, due to its importance, is broken down into the following sub-domains: a. District of Panama b. Panama City c. Rest of Panama District d. District of San Miguelito e. West Panama: made up of the District of Arraiján, La Chorrera, Capira, Chame and San Carlos. f. Rest of the province of Panama (East Panama).

STRATIFICATION

The design of the non-indigenous and indigenous sample of the Living Standards Survey was based on implicit stratification, that is, on a geographic criterion, by domain of study, with the urban and rural areas of the country constituting strata.

A. Non-indigenous sample

1. Province: urban and rural
2. Panama City: urban
3. Rest of Panama District: urban and rural
4. San Miguelito District: urban
5. West Panama:
6. Arraiján: urban, rural
7. La Chorrera: urban, rural
8. Rest of West Panama: urban, rural
9. Rest of Panama province
10. East Panama: urban, rural

SAMPLE SIZE

Considering the requirements of disaggregation of the results at the province level and the desired comparability, it was determined to maintain the same sample size investigated in the 2003 survey. The calculation of the sample size was applied at the level of the study domains and is based on the assumption of simple random sampling and the requirements of precision and confidence required for the research.

The mathematical model of simple random sampling allows an initial estimate of the required sample size to be obtained:

no = (k^2 * p * q) / E^2

Where: no = Initial sample size estimate. K = Required confidence level. The value specified for 1 - a, usually 95%, where k = 1.96. P = Value of the actual percentage or proportion of the study when unknown, so P = 0.50 Q = 1 - P = 0.50 E = Maximum sampling error determined, representing the maximum acceptable difference between the actual value, P , and its estimate, p , from the survey.

The initial estimate of the sample size per domain must be corrected, as the sample design for the research corresponds to a stratified sample in two stages of selection. The sample correction is derived from the design effect (DEFF) and represents the ratio of the variance of the parameter estimated by cluster sampling to the variance of the same parameter resulting from simple random sampling.

A conservative correction, used in the previous survey, was to apply a DEFF = 2.25 to the sample sizes obtained by simple random sampling and an additional correction as a response rate of approximately 80%.

nF = (n0 * DEFF) / TR

Where nF = Final sample size of private dwellings

It is important to indicate that within the selected primary sampling units, an average of 10 private dwellings would be investigated, representing the second stage units of the sample design. The sample sizes obtained for the study domains considered allow us to obtain estimates of percentages or proportions with a level of precision of less than or equal to 10%.

The total sample by design amounted to 8,000 private dwellings, with 4,165 in urban areas and 3,835 in rural areas. In the non-indigenous sample, the private dwellings selected amounted to 7,450 housing units and in the indigenous sample to 550 units. The total sample to be investigated allowed, according to the sample design criteria, the selection of 800 primary sampling units, of which 745 belonged to the non-indigenous sample and 55 to the indigenous sample.

DISTRIBUTION AND SELECTION OF THE SAMPLE

The size of the sample of private dwellings by urban and rural area for the non-indigenous and by comarcas for the indigenous, was made by means of a proportional distribution to the number of dwellings in the universe. The primary sampling units were selected by probability sampling proportional to the total number of private dwellings; while in the urban area, in secondary sampling units, five clusters of two private dwellings were selected by the systematic method, and in the rural area, one cluster of ten dwellings, respectively.

The final probability of sample selection by domain of study and area is a function of the probabilities of selection of the first and second stages, which have been calculated with the results of the Population and Housing Census of May 2000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Community Questionnaire
• Living Standards Survey
• Price Questionnaire

Response rate

The coverage of the collection was recorded by Stage and by province, and then a summary of National Coverage was consolidated.

At the end of the ENV-08 Field Operation, a total of 7,274 dwellings were visited, and 6,977 were found to be fully occupied. Complete information was obtained in 7,045 households where 26,162 persons were interviewed. A total of 781 community meetings and 618 price surveys were conducted.

SUMMARY OF SURVEY COVERAGE STATUS (Percentage)

Dwellings Visited: 100.0 a. Fully Occupied: 95.9 b. Incomplete Occupied: 1.0 c. Refusals: 1.4 d. Temporarily absent: 1.7

Sampling error estimates

The quality indicators of the survey estimates will be calculated with the statistical package SPSS 15; considering the main variables of the survey.

Abstract

In October 2000, the Statistics and Census Office (Dirección de Estadística y Censo), together with the International Labour Office, carried out the Child Labour Survey, in order to provide information which would allow an evaluation of the impact of children’s labour market participation, in order to determine the characteristics and conditions under which this labour market participation occurs, its possible causes, and the existence or not of exploitative relationships. The Survey provides crucial information for preparing specific policies for the population between the ages of 5 and 17 years, as well as for monitoring and evaluation of programmes being carried out by different social agencies attempting to eradicate the worst forms of child labour.

The Child Labour Survey was carried out guided by the following objectives: - Ascertain demographic and socio-economic characteristics of the general population and especially the child population - Provide information that will allow studies of the magnitude, distribution, characteristics, consequences and causes of child labour - Ascertain characteristics of economic sectors where minors are working - Ascertain injuries sustained by the employed population - Ascertain safety mechanisms available to the employed population - Ascertain parents’ perceptions and those of children regarding child labour - Provide a database on child labour that permits formulation of policies and programmes based on reality - Provide information allowing cross-country comparisons

Geographic coverage

Nationwide

Universe

The study universe is the population 5 to 17 years of age residing in private occupied dwellings throughout the country. The interim results from the Population and Housing Census of May 2000 found a preliminary total population of 2,815,644 persons for the country; of these, 766,903 constitute the population from 5 to 17 years of age (see Table 3 in the report provided as external resources),which implies a percentage relationship of 27.2%. This population is divided 56.9% urban and 43.1% rural. Furthermore, the non-indigenous universe contains a 5 to 17 year old population of 693,704 persons and the indigenous one has 73,199, which represent 90.5% and 9.5% of the study population respectively. Private occupied dwellings numbered 667,284 units at the national level,with urban areas representing 64.3% and rural areas 35.7%. In non-indigenous areas, private occupied dwellings numbered 638,565 units, while indigenous areas had 28,719, for a percentage relationship of 95.7% and 4.3%, respectively. The average number of persons aged 5 to 17 years per private occupied dwelling in the country was 1.15 persons per dwelling, 1.02 in urban areas and 1.39 in rural areas, while for the non-indigenous universe this was 1.09 and for the indigenous 2.54.

Sampling procedure

Sampling Frame: With preliminary data from the Population and Housing Census as a reference, as well as the full census organisation and maps from May 2000, the sampling frame was made up of the enumeration area units where population aged 5 to 17 years was recorded.

Sampling units: The sampling units constitute the sample selection unit. In this case, the Primary Sampling Unit is the census segment.

Study domains: The study domains were identified,with a view to type of study and user requirements regarding utility and utilization of information. The country's main province, Panama, was subdivided into the following study domains: Panama and San Miguelito districts and rest of Panama province. The indigenous study domain is integrated at the national level by each one of the legally established comarcas and the indigenous communities outside the comarcas that carry out their activities according to their socio-cultural behaviour patterns.

Stratification: Study universe stratification is based on geographic criteria in accordance with the country's political-administrative coding and takes into consideration the division between urban and rural areas. Stratification by socio-economic variables was not possible, since the census information was not yet ready, as complete processing was expected for March 2001. It is important to note that in Panama implicit stratification has been used in several studies such as, for example, the Quality of Life Survey, the Income and Expense Survey, the Labour Force Survey, etc., obtaining adequate results with regard to the existing socio-economic structure, supported primarily by the particularities occurring in geographic distribution of the country's population.

Sample size: Sample size computation was carried out using the mathematical model for simple random sampling.The critical variable for obtaining sample size is represented by proportion of population aged 5 to 17 years. For Darién and Los Santos provinces and Panama and San Miguelito districts sample size calculations were independent for urban and rural areas, due primordially to the fact that sizes obtained by proportional distribution in some areas were very small, leading to a sampling error much larger than desired.

Sample selection: Probabilistic cluster sampling was applied. Sample design responds to a one-stage design, selecting primary sampling units (census segments) by systematic selection with probability proportional to size. The statistical inference process for the nonindigenous sample selected was carried out by area, by applying the ratio method, using as the exogenous variable demographic projections of the population 5 to 17 years of age.

Pages 6-8 of the study report (available as external resources) provide detailed explanation, formulae and tables on sample design and implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is divided into the following sections: I. Location of the dwelling II. Information on the dwelling III. Household income and expenses IV. List of occupants V. General Characteristics VI. Sociodemographic Characteristics VII. Educational Characteristics VIII. Economic Characteristics IX. Job related injuries or diseases X. Parent perceptions of persons aged 5-12 years who are employed XI. Perceptions of persons aged 5-12 years who are employed.

Spanish and English versions of the questionnaire are provided as external resources.

Cleaning operations

Generalities In order to facilitate development of the different systems, the following documentation was available: list of interviewers and supervisors by province and code; recode list for conditions of employment; list of validations and inconsistencies; interviewer's manual; final questionnaire; file of segments covered by the Survey and a file of indigenous segments. Six systems were developed for the Child Labour Survey, to wit: Data Entry System, Coverage Control System, Recode System, Tabulation System, Expansion Factor System and Data Dictionary System. The Data Entry System was developed using Visual FoxPro (Release 5.0). This system is divided into 4 Sub-Systems: Addition, Query, Modification and Elimination.Validations and inconsistency correction were carried out on-line. This means that the System would not allow data entry personnel to continue if they had not made the due corrections. For greater security and data integrity, once the data entry period was over, batch verifications were carried out, using the same package mentioned above.

Data Entry The Data Entry System was developed under Client-Server architecture. This means that the executable system was server-based, along with its different components, including the 5 databases which were receiving information from questionnaire contents. The Client was a data entry person using a PC as a terminal to access the Server. Every afternoon a backup was carried out from the server to the PC of the Child Labour Survey Information Systems Administrator. Data entry personnel for this Child Labour Survey were chosen from a larger group of data entry personnel from the Household Survey, in addition to having had fieldwork experience. Data entry personnel were limited to consulting specialised personnel or the Programmer-Analyst for the different Systems for this Child Labour Survey. The data were collected in Spanish. For the Statistics and Census Office, any information requested from a citizen is confidential material. Thus, for data entry personnel, the questionnaire and everything related to it was sensitive information. There were controls to determine which data entry person was entering which folder and on which date. The questionnaires could not be removed from the work area.

Each data entry person (of the 8 selected) removed a folder from the shelves, after writing it down in the control list. Each folder was made up of 4 to 6 segments, depending on their size. Data entry began on March 28 and ended on June 12 of the same year, 2001. During the first month, data entry worked in two shifts, from 7:00 a.m. to 3:00 p.m. and from 3:00 p.m. to 10:00 p.m. Later, a single shift was put into effect, from 8:30 a.m. to 4:30 p.m. Initial verification of data entry for a questionnaire was the responsibility of a database called Coverage, which verified that a particular segment was valid (Province + District + Corregimiento + Segment). If the foregoing was correct, data entry could continue for Questionnaire + Household + Person Number (this last number was excluded for the Dwelling database). These data, together with that from Coverage, were verified in the other 5 databases to avoid duplicating keys. Data for a total of 9,261 questionnaires were entered.

Data Processing It should be mentioned that the data processing directives followed are those established by

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Any place or premises structurally separate or independent, that has been built, made, or converted for use as permanent or temporary housing or lodging of persons or also any class of lodging, fixed or mobile, occupied by persons as a living quarters on the date of the census. - Households: All persons who reside habitually in this dwelling.

Universe

All persons who slept in the dwelling the night before, including domestic servants and their relatives, guests, etc. if they slept there. In addition, all persons who habitually reside in the dwelling even if they didn't sleep there the night before because of their job, recreation, or other transitory cause.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Centro Latinoamericano de Demografia (CELADE)

SAMPLE DESIGN: Systematic sample of every 10th household.

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 195,577

Mode of data collection

Face-to-face [f2f]

Research instrument

Enumeration forms: (1) A family form that requested information on the dwelling and individuals, (2) A collective dwellings form, and (3) An indigenous population form

Response rate

COVERAGE: 92%

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

About this dataset

Context

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

Key observations

The percent distribution of Panama City population by race (across all racial categories recognized by the U.S. Census Bureau): 63.18% are white, 21.04% are Black or African American, 1.01% are American Indian and Alaska Native, 1.02% are Asian, 0.25% are Native Hawaiian and other Pacific Islander, 4.14% are some other race and 9.35% 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 Panama City
• Population: The population of the racial category (excluding ethnicity) in the Panama City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Panama City 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 Panama City Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

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

Key observations

The percent distribution of Panama City population by race (across all racial categories recognized by the U.S. Census Bureau): 65.48% are white, 21.67% are Black or African American, 1.21% are American Indian and Alaska Native, 1.27% are Asian, 0.23% are Native Hawaiian and other Pacific Islander, 3.26% are some other race and 6.89% are multiracial.

https://i.neilsberg.com/ch/panama-city-fl-population-by-race.jpeg" alt="Panama City population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Panama City
• Population: The population of the racial category (excluding ethnicity) in the Panama City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Panama City 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 Panama City Population by Race & Ethnicity. You can refer the same here