VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Mali ML: Population in Largest City: as % of Urban Population data was reported at 30.725 % in 2017. This records a decrease from the previous number of 31.237 % for 2016. Mali ML: Population in Largest City: as % of Urban Population data is updated yearly, averaging 36.278 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 37.806 % in 1990 and a record low of 22.291 % in 1961. Mali ML: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

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

Households

UNITS IDENTIFIED: - Dwellings: Yes - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: A housing unit is a structurally separate and independent place of abode which, by way it has been constructed, converted or arranged, is intended for habitation by one household. - Households: A household is a social unit consisting of a person living alone or a group of persons who (1) sleep in the same housing unit and (2) have a common arrangement for the preparation and consumption of food. - Group quarters: Institutional living quarters are those structurally separate places of abode intended for habitation by large groups of individuals. Some examples are in operation such as hotels, motels, dormitories, lodging houses, seminaries, mental hospitals, etc.

Universe

Filipino nationals regardless of whether they are residing in Philipines at the time of the census and citizens of other countires having their usual residence in the Phillipines or those whose temporary residence will exceed a year from the time of their arrival.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Statistics Office

SAMPLE DESIGN: The sampling rate, or the proportion of households to be selected as samples within each enumeration area (EA), varies from one city /municipality to another. It can be either 100%, 20% or 10% depending on the 1990 expected population of the municipality or city.

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE UNIVERSE: Microdata are available for 100% samples

SAMPLE SIZE (person records): 6,013,913

Mode of data collection

Face-to-face [f2f]

Research instrument

Three forms were used; the nonsample households were interviewed using the Common Household Questionnaire (CPH Form 2) while the Sample Household Questionnaire (CPH Form 3) was used for the sample households, Institutional households were enumerated using the Institutional Population Questionnaire (CPH Form 4)

Response rate

COVERAGE: 100%

London was by far the largest urban agglomeration in the United Kingdom in 2023, with an estimated population of 9.65 million people, more than three times as large as Manchester, the UK’s second-biggest urban agglomeration. The agglomerations of Birmingham and Leeds / Bradford had the third and fourth-largest populations respectively, while the biggest city in Scotland, Glasgow, was the fifth largest. Largest cities in Europe Two cities in Europe had larger urban areas than London, with the Russian capital Moscow having a population of almost 12.7 million. The city of Paris, located just over 200 miles away from London, was the second-largest city in Europe, with a population of more than 11.2 million people. Paris was followed by London in terms of population-size, and then by the Spanish cities of Madrid and Barcelona, at 6.75 million and 5.68 million people respectively. Russia's second-biggest city; St. Petersburg had a population of 5.56 million, followed by Rome at 4.3 million, and Berlin at 3.5 million. London’s population growth Throughout the 1980s, the population of London fluctuated from a high of 6.81 million people in 1981 to a low of 6.73 million inhabitants in 1988. During the 1990s, the population of London increased once again, growing from 6.8 million at the start of the decade to 7.15 million by 1999. London's population has continued to grow since the turn of the century, reaching a peak of 8.96 million people in 2019, and is forecast to reach 9.8 million by 2043.

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning, and maternal and child health.

The 1990 Jordan Population and Family Health Survey (JPFHS) was carried out as part of the Demographic and Health Survey (DHS) program. The Demographic and Health Surveys is assisting governments and private agencies in the implementation of household surveys in developing countries.

The JPFIS was designed to provide information on levels and trends of fertility, infant and child mortality, and family planning. The survey also gathered information on breastfeeding, matemal and child health cam, the nutritional status of children under five, as well as the characteristics of households and household members.

The main objectives of the project include: a) Providing decision makers with a data base and analyses useful for informed policy choices, b) Expanding the international population and health data base, c) Advancing survey methodology, and d) Developing skills and resources necessary to conduct high quality demographic and health surveys in the participating countries.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the JPFHS survey was selected to be representative of the major geographical regions, as well as the nation as a whole. The survey adopted a stratified, multi-stage sampling design. In each governorate, localities were classified into 9 strata according to the estimated population size in 1989. The sampling design also allowed for the survey results to be presented according to major cities (Amman, Irbid and Zarqa), other urban localities, and the rural areas. Localities with fewer than 5,000 people were considered rural.

For this survey, 349 sample units were drawn, containing 10,708 housing units for the individual interview. Since the survey used a separate household questionnaire, the Department of Statistics doubled the household sample size and added a few questions on labor force, while keeping the original individual sample intact. This yielded 21,172 housing units. During fieldwork for the household interview, it was found that 4,359 household units were ineligible either because the dwelling was vacant or destroyed, the household was absent during the team visit, or some other reason. There were 16,296 completed household interviews out of 16,813 eligible households, producing a response rate of 96.9 percent.

The completed household interviews yielded 7,246 women eligible for the individual interview, of which 6,461 were successfully interviewed, producing a response rate of 89.2 percent.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 1990 JPFIS utilized two questionnaires, one for the household interview and the other for individual women. Both questionnaires were developed first in English and then translated into Arabic. The household questionnaire was used to list all members of the sample households, including usual residents as well as visitors. For each member of the household, basic demographic and socioeconomic characteristics were recorded and women eligible for the individual interview were identified. To be eligible for individual interview, a woman had to be a usual member of the household (part of the de jure population), ever-married, and between 15 and 49 years of age. The household questionnaire was expanded from the standard DHS-II model questionnaire to facilitate the estimation of adult mortality using the orphanhood and widowhood techniques. In addition, the questionnaire obtained information on polygamy, economic activity of persons 15 years of age and over, family type, type of insurance covering the household members, country of work in the summer of 1990 which coincided with the Gulf crisis, and basic data for the calculation of the crude birth rate and the crude death rate. Additional questions were asked about deceased women if they were ever-married and age 15-49, in order to obtain information for the calculation of materoal mortality indices.

The individual questionnaire is a modified version of the standard DHS-II model "A" questionnaire. Experience gained from previous surveys, in particular the 1983 Jordan Fertility and Family Health Survey, and the questionnaire developed by the Pan Arab Project for Child Development (PAPCHILD), were useful in the discussions on the content of the JPFHS questionnaire. A major change from the DHS-II model questionnaire was the rearrangement of the sections so that the marriage section came before reproduction; this allowed the interview to flow more smoothly. Questions on children's cause of death based on verbal autopsy were added to the section on health, which, due to its size, was split into two parts. The first part focused on antenatal care and breastfeeding; the second part examined measures for prevention of childhood diseases and information on the morbidity and mortality of children loom since January 1985. As questions on sexual relations were considered too sensitive, they were replaced by questions about the husband's presence in the household during the specified time period; this served as a proxy for recent sexual activity.

The JPFHS individual questionnaire consists of nine sections: - Respondent's background and household characteristics - Marriage - Reproduction - Contraception - Breastfeeding and health - Immunization, morbidity, and child mortality - Fertility preferences - Husband's background, residence, and woman's work - Height and weight of children

Response rate

For the individual interview, the number of eligible women found in the selected households and the number of women successfully interviewed are presented. The data indicate a high response rate for the household interview (96.9 percent), and a lower rate for the individual interview (89.2 percent). Women in large cities have a slightly lower response rate (88.6 percent) than those in other areas. Most of the non-response for the individual interview was due to the absence of respondents and the postponement of interviews which were incomplete.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the JPFHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of standard error of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which one can reasonably assured that, apart from nonsampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the JPFI-IS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the report which is presented in this documentation.

This datalayer displays the Urbanized Areas (UAs) for the state based on a January 1, 1990 ground condition. Note that the Census Bureau made significant changes in Urban/Rural designations for the Census 2000 data layers. Some of these delineations and definitions are explained below. 1990 Urban/Rural The U.S. Census Bureau defined urban for the 1990 census as consisting of all territory and population in urbanized areas (UAs) and in the urban portion of places with 2,500 or more people located outside of the UAs. The 1990 urban and rural classification applied to the 50 states, the District of Columbia, and Puerto Rico. 1990 Urbanized Areas A 1990 urbanized area (UA) consisted of at least one central place and the adjacent densely settled surrounding territory that together had a minimum population of 50,000 people. The densely settled surrounding territory generally consisted of an area with continuous residential development and a general overall population density of at least 1,000 people per square mile. 1990 Extended Cities For the 1990 census, the U.S. Census Bureau distinguished the urban and rural population within incorporated places whose boundaries contained large, sparsely populated, or even unpopulated area. Under the 1990 criteria, an extended city had to contain either 25 percent of the total land area or at least 25 square miles with an overall population density lower than 100 people per square mile. Such pieces of territory had to cover at least 5 square miles. This low-density area was classified as rural and the other, more densely settled portion of the incorporated place was classified as urban. Unlike previous censuses where the U.S. Census Bureau defined extended cities only within UAs, for the 1990 census the U.S. Census Bureau applied the extended city criteria to qualifying incorporated places located outside UAs. 1990 Urbanized Area Codes Each 1990 UA was assigned a 4-digit numeric census code in alphabetical sequence on a nationwide basis based on the metropolitan area codes. Note that in Record Type C, the 1990 UA 4-digit numeric census code an d Census 2000 UA 5-digit numeric census code share a 5-character field. Because of this, the 1990 4-digit UA code, in Record Type C only, appears with a trailing blank. For Census 2000 the U.S. Census Bureau classifies as urban all territory, population, and housing units located within urbanized areas (UAs) and urban clusters (UCs). It delineates UA and UC boundaries to encompass densely settled territory, which generally consists of: - A cluster of one or more block groups or census blocks each of which has a population density of at least 1,000 people per square mile at the time - Surrounding block groups and census blocks each of which has a population density of at least 500 people per square mile at the time, and - Less densely settled blocks that form enclaves or indentations, or are used to connect discontiguous areas with qualifying densities. Rural consists of all territory, population, and housing units located outside of UAs and UCs. For Census 2000 this urban and rural classification applies to the 50 states, the District of Columbia, Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, and the Virgin Islands of the United States. Urbanized Areas (UAs) An urbanized area consists of densely settled territory that contains 50,000 or more people. The U.S. Census Bureau delineates UAs to provide a better separation of urban and rural territory, population, and housing in the vicinity of large places. For Census 2000, the UA criteria were extensively revised and the delineations were performed using a zero-based approach. Because of more stringent density requirements, some territory that was classified as urbanized for the 1990 census has been reclassified as rural. (Area that was part of a 1990 UA has not been automatically grandfathered into the 2000 UA.) In addition, some areas that were identified as UAs for the 1990 census have been reclassified as urban clusters. Urban Clusters (UCs) An urban cluster consists of densely settled territory that has at least 2,500 people but fewer than 50,000 people. The U.S. Census Bureau introduced the UC for Census 2000 to provide a more consistent and accurate measure of the population concentration in and around places. UCs are defined using the same criteria that are used to define UAs. UCs replace the provision in the 1990 and previous censuses that defined as urban only those places with 2,500 or more people located outside of urbanized areas. Urban Area Title and Code The title of each UA and UC may contain up to three incorporated place names, and will include the two-letter U.S. Postal Service abbreviation for each state into which the UA or UC extends. However, if the UA or UC does not contain an incorporated place, the urban area title will include the single name of a census designated place (CDP), minor civil division, or populated place recognized by the U.S. Geological Survey's Geographic Names Information System. Each UC and UA is assigned a 5-digit numeric code, based on a national alphabetical sequence of all urban area names. For the 1990 census, the U.S. Census Bureau assigned as four-digit UA code based on the metropolitan area codes. Urban Area Central Places A central place functions as the dominant center of an urban area. The U.S. Census Bureau identifies one or more central places for each UA or UC that contains a place. Any incorporated place or census designated place (CDP) that is in the title of the urban area is a central place of that UA or UC. In addition, any other incorporated place or CDP that has an urban population of 50,000 or an urban population of at least 2,500 people and is at least 2/3 the size of the largest place within the urban area also is a central place. Extended Places As a result of the UA and UC delineations, an incorporated place or census designated place (CDP) may be partially within and partially outside of a UA or UC. Any place that is split by a UA or UC is referred to as an extended place.

Serbia RS: Population in Largest City: as % of Urban Population data was reported at 35.197 % in 2017. This records an increase from the previous number of 34.931 % for 2016. Serbia RS: Population in Largest City: as % of Urban Population data is updated yearly, averaging 32.321 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 35.197 % in 2017 and a record low of 29.475 % in 1991. Serbia RS: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Croatia HR: Population in Largest City: as % of Urban Population data was reported at 30.288 % in 2024. This records an increase from the previous number of 30.260 % for 2023. Croatia HR: Population in Largest City: as % of Urban Population data is updated yearly, averaging 29.743 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 34.420 % in 1960 and a record low of 28.819 % in 1990. Croatia HR: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Croatia – Table HR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Serbia RS: Population in Largest City data was reported at 1,183,409.000 Person in 2017. This records an increase from the previous number of 1,182,686.000 Person for 2016. Serbia RS: Population in Largest City data is updated yearly, averaging 1,132,217.000 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 1,183,409.000 Person in 2017 and a record low of 1,120,558.000 Person in 2001. Serbia RS: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

In 2023, the population of Gwangju, South Korea amounted to around 1.46 million, a slight decrease from the previous year. Gwangju is the sixth-largest city in South Korea. Over the last decade, the population has stayed at just under 1.5 million.

The population of London was approximately *** million in 2023, an increase of over *** million people when compared with the early 1980s. Throughout the 1980s, the population of the United Kingdom's capital grew at a relatively slow rate, before accelerating to a much faster rate in the 1990s. London is by far the largest city / urban agglomeration in the United Kingdom, more than three times larger than the next largest cities of Manchester and Birmingham. London’s forecasted population is expected to continue growing at much the same pace it has been growing since the mid-1990s and reach almost *** million by 2042. London boroughs As of 2022, the London borough with the highest population was Croydon, at approximately *******, followed by Barnet at *******. Overall, London is divided into 33 different boroughs, with London's historic center, the City of London, having by far the smallest population, at just ******. Residents of the City of London, however, have the highest average median weekly earnings among all of London's boroughs, at ***** pounds per week, compared with just *** pounds per week in Redbridge, the lowest average weekly earnings among London boroughs. While the overall unemployment rate for London was *** percent in early 2023, this ranged from *** percent in Brent, to just *** percent in Kingston upon Thames.
Economic imbalance Aside from being the UK's largest city in terms of population, London is also undoubtedly the UK's cultural, political and economic center. As of 2021, the GDP of Greater London was approximately ***** billion British pounds, just over ** percent of the UK's overall GDP. In the same year, GDP per person in London was ****** pounds compared with the UK average of ****** pounds. Additionally, productivity in London is far higher than the UK average. As measured by output per hour worked, London was **** percent more productive than the rest of the UK.

In 2023, the population of Daejeon, South Korea amounted to around **** million, slightly down from about **** million in 2021. Daejeon is the fifth-largest city in South Korea.

Abstract

The Labor Force Survey is a nationwide survey of households conducted regularly to gather data on the demographic and socio-economic characteristics of the population. It is primarily geared towards the estimation of the levels of employment in the country.

The Labor Force Survey aims to provide a quantitative framework for the preparation of plans and formulation of policies affecting the labor market.

Geographic coverage

National coverage, the sample design has been drawn in such a way that accurate lower level classification would be possible. The 74 provinces, 24 cities and eight key municipalities are covered.

Analysis unit

• Person/ individual

Universe

The survey covered all persons 10 years old and over. Persons who reside in institutions are not covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Labor Force Survey adopts that of the Integrated Survey of Households (ISH), which uses a stratified two-stage sampling design. It is prepared by the NEDA Technical Committee on Survey Design and first implemented in 1984. It is the same sampling design used in the ISH modules starting in 1986.

The urban and rural areas of each province are the principal domains of the survey. In addition, the urban and rural areas of cities with a population of 150,000 or more as of 1990 are also made domains of the survey with urban and rural dimensions. These include the four cities and five municipalities of Metro Manila (Manila, Quezon City, Pasay and Caloocan; Valenzuela, Paranaque, Pasig, Marikina and Makati), and other key cities such as Baguio, Angeles, Cabanatuan, Olongapo, Batangas, Lipa, Lucena, San Pablo, Bacolod, Iloilo, Cebu, Mandaue, Zamboanga, Butuan, Cagayan de Oro, Davao, General Santos, and Iligan and key municipalities such as San Fernando, Pampanga and Tarlac, Tarlac.

The rest of Metro Manila, i.e., the remaining municipalities are treated as separate domains. In the case of Makati, six exclusive villages are identified and samples are selected using a different scheme. These villages are Forbes Park, Bel-Air, Dasmarinas, San Lorenzo, Urdaneta and Magallanes.

Because of the creation of the Autonomous Region of Muslim Mindanao (ARMM), this, defining its areas of coverage, Marawi City and Cotabato Cfity are likewises treated as domains.

Sampling Units and Sampling Frame The primary sampling units (PSUs) under the sample design are the barangays and the households within each sample barangay comprise the secondary sampling units (SSUs). The frame from which the sample barangays are drawn is obtained from the 1990 Census of Population and Housing (CPH). Hence, all the approximately 40,000 barangays covered in the 1990 CPH are part of the primary sampling frame. The sampling frame for the SSUs, that is, the households, is prepared by listing all households in each of the selected sample barangays. The listing operation is conducted regularly in the sample barangays to update the secondary sampling frame from where the sample households are selected.

Sample Size and Sampling Fraction The size of the sample is envisioned to meet the demand for fairly adequate statistics at the domain level. Taking this need into account and considering cost constraints as well, the decision reached is for a national sample of about 26,000 households. In general, the sample design results in self-weighting samples within domains, with a uniform sampling fraction of 1:400 for urban and 1:600 for rural areas. However, special areas are assigned different sampling fractions so as to obtain "adequate" samples for each. Special areas refer to the urban and rural areas of a province or large city which are small relative to their counterparts.

Selection of Samples For the purpose of selecting PSUs, the barangay in each domain are arranged by population size (as of the 1990 Census of Population) in descending order and then grouped into strata of approximately equal sizes. Four independent PSUs are drawn with probability proportional to size with complete replacement.

Secondary sampling units are selected systematiclally with a random start.

Sampling deviation

Replacement of non-responding or transferred sample households is allowed although it is still possible to have non-response cases due to critical peace and order situation or inaccessibility of the selected sample households. If there are unenumerated barangays or sample households, non-response adjustments are utilized.

Mode of data collection

Face-to-face [f2f]

Research instrument

The items of information presented in the July 1991 Quarterly Labor Force Survey questionnaire were derived from a structured questionnaire covering the demographic and economic characteristics of individuals. The demographic characteristics include age, sex, relationship to household head, marital status, and highest grade completed. The economic characteristics include employment status, occupation, industry, nomal working hours, total hours worked, class of worker, etc.

Cleaning operations

Data processing involves two stages: manual processing and machine processing. Manual processing refers to the manual editing and coding of questionnaires. This was done prior to machine processing which entailed code validation, consistency checks as well as tabulation.

Enumeration is a very complex operation and may happen that accomplished questionnaires may have some omissions and implausible or inconsistent entries. Editing is meant to correct these errors.

For purposes of operational convenience, field editing was done. The interviewers were required to review the entries at the end of each interview. Blank items, which were applicable to the respondents, were verified and filled out. Before being transmitted to the regional office, all questionnaires were edited in the field offices.

Coding, the transformation of information from the questionnaire to machine readable form, was likewise done in the field offices.

Machine processing involved all operations that were done with the use of a computer and/or its accessories, that is, from data encoding to tabulation. Coded data are usually in such media as tapes and diskettes. Machine editing is preferred to ensure correctness of encoded information. Except for sample completeness check and verification of geographic identification which are the responsibility of the subject matter division, some imputations and corrections of entries are done mechanically.

Response rate

The response rate for January 1992 LFS was 99.94 percent. The non-response rate of 0.06 percent was due to crticial peace and order situation or inaccessibility of the selected sample or sample households.

Sampling error estimates

Standard Error (SE) and Coefficient of Variation (CV) for the selected variables of the Labor Force Survey (LFS) for July 1991 survey round was computed using the statistical package IMPS. The selected variables referred to include the employment, unemployment and labor force population levels and rates.

A sampling error is usually measured in terms of the standard error for a particular statistic. A standard error is a measure of dispersion of an estimate from the expected value.

The SE can be used to calculate confidence intervals within which the true value for the population can be estimated, while the CV is a measure of relative variability that is commonly used to assess the precision of survey estimates.

The CV is defined as the ratio of the standard error and the estimate. An estimate with CV value of less than 10 percent is considered precise.

Abstract

The Pakistan Demographic and Health Survey (PDHS) was fielded on a national basis between the months of December 1990 and May 1991. The survey was carried out by the National Institute of Population Studies with the objective of assisting the Ministry of Population Welfare to evaluate the Population Welfare Programme and maternal and child health services. The PDHS is the latest in a series of surveys, making it possible to evaluate changes in the demographic status of the population and in health conditions nationwide. Earlier surveys include the Pakistan Contraceptive Prevalence Survey of 1984-85 and the Pakistan Fertility Survey of 1975.

The primary objective of the Pakistan Demographic and Health Survey (PDHS) was to provide national- and provincial-level data on population and health in Pakistan. The primary emphasis was on the following topics: fertility, nuptiality, family size preferences, knowledge and use of family planning, the potential demand for contraception, the level of unwanted fertility, infant and child mortality, breastfeeding and food supplementation practices, maternal care, child nutrition and health, immunisations and child morbidity. This information is intended to assist policy makers, administrators and researchers in assessing and evaluating population and health programmes and strategies. The PDHS is further intended to serve as a source of demographic data for comparison with earlier surveys, particularly the 1975 Pakistan Fertility Survey (PFS) and the 1984-85 Pakistan Contraceptive Prevalence Survey (PCPS).

MAIN RESULTS

Until recently, fertility rates had remained high with little evidence of any sustained fertility decline. In recent years, however, fertility has begun to decline due to a rapid increase in the age at marriage and to a modest rise in the prevalence of contraceptive use. The lotal fertility rate is estimated to have fallen from a level of approximately 6.4 children in the early 1980s to 6.0 children in the mid-1980s, to 5.4 children in the late 1980s. The exact magnitude of the change is in dispute and will be the subject of further research. Important differentials of fertility include the degree ofurbanisation and the level of women's education. The total fertility rate is estimated to be nearly one child lower in major cities (4.7) than in rural areas (5.6). Women with at least some secondary schooling have a rate of 3.6, compared to a rate of 5.7 children for women with no formal education.

There is a wide disparity between women's knowledge and use of contraceptives in Pakistan. While 78 percent of currently married women report knowing at least one method of contraception, only 21 percent have ever used a method, and only 12 percent are currently doing so. Three-fourths of current users are using a modem method and one-fourth a traditional method. The two most commonly used methods are female sterilisation (4 percent) and the condom (3 percent). Despite the relatively low level of contraceptive use, the gain over time has been significant. Among married non-pregnant women, contraceptive use has almost tripled in 15 years, from 5 percent in 1975 to 14 percent in 1990-91. The contraceptive prevalence among women with secondary education is 38 percent, and among women with no schooling it is only 8 percent. Nearly one-third of women in major cities arc current users of contraception, but contraceptive use is still rare in rural areas (6 percent).

The Government of Pakistan plays a major role in providing family planning services. Eighty-five percent of sterilised women and 81 percent of IUD users obtained services from the public sector. Condoms, however, were supplied primarily through the social marketing programme.

The use of contraceptives depends on many factors, including the degree of acceptability of the concept of family planning. Among currently married women who know of a contraceptive method, 62 percent approve of family planning. There appears to be a considerable amount of consensus between husbands and wives about family planning use: one-third of female respondents reported that both they and their husbands approve of family planning, while slightly more than one-fifth said they both disapprove. The latter couples constitute a group for which family planning acceptance will require concerted motivational efforts.

The educational levels attained by Pakistani women remain low: 79 percent of women have had no formal education, 14 percent have studied at the primary or middle school level, and only 7 percent have attended at least some secondary schooling. The traditional social structure of Pakistan supports a natural fertility pattern in which the majority of women do not use any means of fertility regulation. In such populations, the proximate determinants of fertility (other than contraception) are crucial in determining fertility levels. These include age at marriage, breastfeeding, and the duration of postpartum amenorrhoea and abstinence.

The mean age at marriage has risen sharply over the past few decades, from under 17 years in the 1950s to 21.7 years in 1991. Despite this rise, marriage remains virtually universal: among women over the age of 35, only 2 percent have never married. Marriage patterns in Pakistan are characterised by an unusually high degree of consangninity. Half of all women are married to their first cousin and an additional 11 percent are married to their second cousin.

Breasffeeding is important because of the natural immune protection it provides to babies, and the protection against pregnancy it gives to mothers. Women in Pakistan breastfeed their children for an average of20months. Themeandurationofpostpartumamenorrhoeais slightly more than 9 months. After tbebirth of a child, women abstain from sexual relations for an average of 5 months. As a result, the mean duration of postpartum insusceptibility (the period immediately following a birth during which the mother is protected from the risk of pregnancy) is 11 months, and the median is 8 months. Because of differentials in the duration of breastfeeding and abstinence, the median duration of insusceptibility varies widely: from 4 months for women with at least some secondary education to 9 months for women with no schooling; and from 5 months for women residing in major cities to 9 months for women in rural areas.

In the PDHS, women were asked about their desire for additional sons and daughters. Overall, 40 percent of currently married women do not want to have any more children. This figure increases rapidly depending on the number of children a woman has: from 17 percent for women with two living children, to 52 percent for women with four children, to 71 percent for women with six children. The desire to stop childbearing varies widely across cultural groupings. For example, among women with four living children, the percentage who want no more varies from 47 percent for women with no education to 84 percent for those with at least some secondary education.

Gender preference continues to be widespread in Pakistan. Among currently married non-pregnant women who want another child, 49 percent would prefer to have a boy and only 5 percent would prefer a girl, while 46 percent say it would make no difference.

The need for family planning services, as measured in the PDHS, takes into account women's statements concerning recent and future intended childbearing and their use of contraceptives. It is estimated that 25 percent of currently married women have a need for family planning to stop childbearing and an additional 12 percent are in need of family planning for spacing children. Thus, the total need for family planning equals 37 percent, while only 12 percent of women are currently using contraception. The result is an unmet need for family planning services consisting of 25 percent of currently married women. This gap presents both an opportunity and a challenge to the Population Welfare Programme.

Nearly one-tenth of children in Pakistan die before reaching their first birthday. The infant mortality rate during the six years preceding the survey is estimaled to be 91 per thousand live births; the under-five mortality rate is 117 per thousand. The under-five mortality rates vary from 92 per thousand for major cities to 132 for rural areas; and from 50 per thousand for women with at least some secondary education to 128 for those with no education.

The level of infant mortality is influenced by biological factors such as mother's age at birth, birth order and, most importantly, the length of the preceding birth interval. Children born less than two years after their next oldest sibling are subject to an infant mortality rate of 133 per thousand, compared to 65 for those spaced two to three years apart, and 30 for those born at least four years after their older brother or sister.

One of the priorities of the Government of Pakistan is to provide medical care during pregnancy and at the time of delivery, both of which are essential for infant and child survival and safe motherhood. Looking at children born in the five years preceding the survey, antenatal care was received during pregnancy for only 30 percent of these births. In rural areas, only 17 percent of births benefited from antenatal care, compared to 71 percent in major cities. Educational differentials in antenatal care are also striking: 22 percent of births of mothers with no education received antenatal care, compared to 85 percent of births of mothers with at least some secondary education.

Tetanus, a major cause of neonatal death in Pakistan, can be prevented by immunisation of the mother during pregnancy. For 30 percent of all births in the five years prior to the survey, the mother received a tetanus toxoid vaccination. The differentials are about the same as those for antenatal care generally.

Eighty-five percent of the

London was by far the largest urban agglomeration in the United Kingdom in 2025, with an estimated population of 9.8 million people, more than three times as large as Manchester, the UK’s second-biggest urban agglomeration. The agglomerations of Birmingham and Leeds / Bradford had the third and fourth-largest populations, respectively, while the biggest city in Scotland, Glasgow, was the fifth largest. Largest cities in Europe Two cities in Europe had larger urban areas than London, with Istanbul having a population of around 16.2 million and the Russian capital Moscow having a population of over 12.7 million. The city of Paris, located just over 200 miles away from London, was the second-largest city in Europe, with a population of more than 11.3 million people. Paris was followed by London in terms of population size, and then by the Spanish cities of Madrid and Barcelona, at 6.8 million and 5.7 million people, respectively. The Italian capital, Rome, was the next largest city at 4.3 million, followed by Berlin at 3.6 million. London’s population growth Throughout the 1980s, the population of London fluctuated from a high of 6.81 million people in 1981 to a low of 6.73 million inhabitants in 1988. During the 1990s, the population of London increased once again, growing from 6.8 million at the start of the decade to 7.15 million by 1999. London's population has continued to grow since the turn of the century, and despite declining between 2019 and 2021, it reached 8.9 million people in 2023 and is forecast to reach almost ten million by 2047.

In 2025, the female population in France amounted to more than ** million. Like most of other European countries, France has a female population larger than its male population. Female population in France According to the source, the female population in France has been increasing since 2004. That year, there were more than ** million women in France, compared to **** million ten years later. Surprisingly, the total number of male births has always been higher than the total number of female births. However, life expectancy in the country is higher for women, and the proportion between men and women in France appears to stabilize over time. Women live longer than men Studies have shown that the life expectancy at birth is higher for females than for males. In 2023, a baby boy born in France had a life expectancy of 80 years, while it reached **** years for a baby girl. In Europe, as well as in France, the life expectancy gap between men and women is a consistent trend. Health issues and a riskier lifestyle could explain why women outlive men. In 2018, Madrid was the European city where both men and women had the longest life expectancy. It reached **** years for females and **** for males.

Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions.

 This project has provided a population database depicting the
 worldwide distribution of population in a 1X1 latitude/longitude grid
 system. The database is unique, firstly, in that it makes use of the
 most recent data available (1990). Secondly, it offers true
 apportionment for each grid cell that is, if a cell contains
 populations from two different countries, each is assigned a
 percentage of the grid cell area, rather than artificially assigning
 the whole cell to one or the other country (this is especially
 important for European countries). Thirdly, the database gives the
 percentage of a country's total population accounted for in each
 cell. So if a country's total in a given year around 1990 (1989 or
 1991, for example) is known, then population in each cell can be
 calculated by using the percentage given in the database with the
 assumption that the growth rate in each cell of the country is the
 same. And lastly, this dataset is easy to be updated for each country
 as new national population figures become available.

Abstract

The survey is part of the project work on "Strengthening Central Bureau of Statistics in Socio-economic Statistics and National Accounts," supported by UNDP.

In spite of the efforts made by varioius agencies in producing statistics, a number of key areas are still faced with critical data gaps. While activities of the Central Bureau of Statistics (CBS) remained tied up for a long time in conducting periodic censuses and their analyses, the demand for current economic statistics for development planning and policy formulation are being felt to be ever increasing. it was realised that the existing critical data gaps, could be fulfilled only by conducting sample surveys in different areas on a regular basis.

The Multipurpose Production Survey is indeed a step forward in thsi direction initiated by the CBS to reduc the critical data gaps gradually. The undertaken survey had envisaged to generate statistics to augment the task of improving National Accounts estimates and serve other users in various fields.

The Multipurpose Production Survey (Urban) includes altogether 1500 sub wards (to be considered as an enumeration block) from the entire 33 towns. Sub-wards consisted of 150-200 dwellings. On the basis of level of urbanization of towns, towns were categorized in three levels - Urban, Semi Urban and Partly Urban. Reference period was the average of the twenty months stretching from April 1989 to November 1990.As shown by the survey result, percentage of households engaged in these type of economic activities is higher (26.3%) in the towns under the category "Urban" and are lower in other categories with 21.9% in the "Semi-urban" and 20.7% in the "Partly urban".

Limitations of the survey

As any statistical investigation, the MPS (Urban) has its own limitations.

Despite the importance of mapping operation carried out for this survey it should be realised that the operation was more experimental and accurate results can not be expected from a firsthand attempt like this. Besides the survey was seriously affected by the poor state of maps and unavialiability of auxiliary informations required. Moreover, there was no clarity of ward boundaries even in among local authorities and residents, as the boundaries were delineated not strictly on a scientific way and were frequently changed for political reas ons. pnder the circumstances, initiation by CBS to prepare subsequent maps for survey purpose faced serious problems . Possibility of omission of some households especially in the large cities cannot be rejected.

The aspect of reference period regarding the necessity of sub-sampling of time over a year for data collection was mentioned in the Report on the MPS (Rural). Situation could not be improved in the MPS (Urban) too. In order to avoid the possibility of seasonal effect, intensity characteristics like number of months worked during the year, number of working days during the month etc. were used while estimating the annual aggregates.

Geographic coverage

National urban areas only

Analysis unit

Households.

Universe

All households in Urban areas of all 33 towns of Nepal.

Though this is basically a household survey in nature, some parts of the investigation necessarily had to be done through establishments and hence an overlap between the two. This was true especially in the case of manufacturing and retail trade.

Kind of data

Sample survey data [ssd]

Sampling procedure

The complexity in the sampling design of the MPS (Urban) was further simplified by the formation of smaller enumeration blocks from the large municipality wards. A single stage stratified sampling was adopted by maintaining the sample fraction of 1/10 of urban enumeration blocks for all strata. Sub-division of wards was done by distributing the number of dwellings in each ward into blocks consisting of 150-200 dwellings.

For an appropriate area sampling it is necessary that enumeration area be more or less equal in size in population and the characteristics to be investigated be homogeneous to the extent possible. However, existing size of municipality wards are not fit to be considered as an enumeration unit due to various reasons. First of all, high variation in the size of population among the wards is noteworthy. Secondly, most of the urban wards, belonging to big towns like Kathmandu, Biratnagar, Birgunj, etc. are too large and are not manageable even for field operation. So it was decided to form the sub-wards with 15-200 dwellings through an intensive field work in order to prepare a sampling frame for the MPS (Urban). Despite several constraints of resources and lack of experience in such activity, altogether 1500 sub wards (to be considered as an enumeration block) from the entire 33 towns were formed and subsequently maps for these blocks were prepared.

Different level of urbanization of towns was another aspect to be considered for sampling design. In Nepal, some of the towns seem nothing more than an administrative center or major district settlement and are either partly urbanized or yet to be urbanized. The others possess urban characteristics to a large extent but still include some rural type of settlements. Only Kathmandu and Lalitpur can be considered as urbanized municipalities. Hence, the towns were divided into three groups for stratification purposes.

1. Urban - Kathmandu, Lalitpur (2)
2. Semi Urban - (towns with more than 40,000 population according to 1981 census) i.e. Biratnagar, Birgunj, Bhaktapur, Dharan, Pokhara and Mahendranagar (4)
3. Partly urban - Ilam, Bhadrapur, Damak, Dhankuta, Inarwa, Rajbiraj, Malangwa, Kalaiya, Lahan, Janakpur, Jaleswore, Hetauda, Bharatpur, Banepa, Dhulikel, Bidur, Tansen, Bhairawa, Butwal, Taulihawa, Trivhuvan Nagar, Nepalgunj, Birendra Nagar, Dhangadi, Dipayal (25)

After formation of sub-wards and subsequent mapping operation it became possible to avoid complicated sampling procedure. A single stage sampling of enumeration areas was adopted for all three strata. The details of the sampling scheme are found in the Report.

Selection of sub-wards was made according to the method for linear systematic sampling where the towns were arranged in the order of economically active population.

The stratification adopted here has been vindicated by results of the survey also. Percentage of households engaged in these four sectors of economic activities is higher (26.3%) in the towns under the category "Urban" and are lower in other categories with 21.9% in the "Semi-urban" and 20.7% in the "Partly urban" as shown by the survey results.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Multipurpose Production Survey (Urban) employed a different questionnaire for each of the three sectors covered:

Questionnaire No. 11 - Small-scale Manufacturing and Cottage Industry Questionnaire No. 12 - Retail Trade Questionnaire No. 13 - Non-mechanised Transport

For a copy of the Questionnaires in Nepali please refer to the attached file of the Report: Questionnaire No. 10 - Listing Sheet Questionnaire No. 11 - Small-scale Manufacturing and Cottage Industry Questionnaire No. 12 - Retail Trade Questionnaire No. 13 - Non-mechanised Transport

Cleaning operations

All the questionnaires were edited thoroughly prior to processing in the computer. Number of rejections, i.e. those that did not fall within the scope was negligible. Efforts were made to make the classification and tabulation as much comparable to those as presented in the report of the MPS (Rural).

In 2024, approximately 67 percent of the total population in China lived in cities. The urbanization rate has increased steadily in China over the last decades. Degree of urbanization in China Urbanization is generally defined as a process of people migrating from rural to urban areas, during which towns and cities are formed and increase in size. Even though urbanization is not exclusively a modern phenomenon, industrialization and modernization did accelerate its progress. As shown in the statistic at hand, the degree of urbanization of China, the world's second-largest economy, rose from 36 percent in 2000 to around 51 percent in 2011. That year, the urban population surpassed the number of rural residents for the first time in the country's history.The urbanization rate varies greatly in different parts of China. While urbanization is lesser advanced in western or central China, in most coastal regions in eastern China more than two-thirds of the population lives already in cities. Among the ten largest Chinese cities in 2021, six were located in coastal regions in East and South China. Urbanization in international comparison Brazil and Russia, two other BRIC countries, display a much higher degree of urbanization than China. On the other hand, in India, the country with the worlds’ largest population, a mere 36.3 percent of the population lived in urban regions as of 2023. Similar to other parts of the world, the progress of urbanization in China is closely linked to modernization. From 2000 to 2024, the contribution of agriculture to the gross domestic product in China shrank from 14.7 percent to 6.8 percent. Even more evident was the decrease of workforce in agriculture.