The Census Tree is the largest-ever database of record links among the historical U.S. censuses, with over 700 million links for people living in the United States between 1850 and 1940. These links allow researchers to construct a longitudinal dataset that is highly representative of the population, and that includes women, Black Americans, and other under-represented populations at unprecedented rates. Each .csv file consists of a crosswalk between the two years indicated in the filename, using the IPUMS histids. For more information, consult the included Read Me file, and visit https://censustree.org.

PLURAL (Place-level urban-rural indices) is a framework to create continuous classifications of "rurality" or "urbanness" based on the spatial configuration of populated places. PLURAL makes use of the concept of "remoteness" to characterize the level of spatial isolation of a populated place with respect to its neighbors. There are two implementations of PLURAL, including (a) PLURAL-1, based on distances to the nearest places of user-specified population classes, and (b) PLURAL-2, based on neighborhood characterization derived from spatial networks. PLURAL requires simplistic input data, i.e., the coordinates (x,y) and population p of populated places (villages, towns, cities) in a given point in time. Due to its simplistic input, the PLURAL rural-urban classification scheme can be applied to historical data, as well as to data from data-scarce settings. Using the PLURAL framework, we created place-level rural-urban indices for the conterminous United States from 1930 to 2018. Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. We developed a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. Based on these methods, we constructed indices of urbanness for 30,000 places in the United States from 1930 to 2018. We call these indices the place-level urban-rural index (PLURAL), enabling long-term, fine-grained analyses of urban and rural change in the United States. The method paper has been peer-reviewed and is published in "Landscape and Urban Planning". The PLURAL indices from 1930 to 2018 are available as CSV files, and as point-based geospatial vector data (.SHP). Moreover, we provide animated GIF files illustrating the spatio-temporal variation of the different variants of the PLURAL indices, illustrating the dynamics of the rural-urban continuum in the United States from 1930 to 2018. Apply the PLURAL rural-urban classification to your own data: Python code is fully open source and available at https://github.com/johannesuhl/plural. Data sources: Place-level population counts (1980-2010) and place locations 1930 - 2018 were obtained from IPUMS NHGIS, (University of Minnesota, www.nhgis.org; Manson et al. 2022). Place-level population counts 1930-1970 were digitized from historical census records (U.S. Census Bureau 1942, 1964). References: Uhl, J.H., Hunter, L.M., Leyk, S., Connor, D.S., Nieves, J.J., Hester, C., Talbot, C. and Gutmann, M., 2023. Place-level urban–rural indices for the United States from 1930 to 2018. Landscape and Urban Planning, 236, p.104762. DOI: https://doi.org/10.1016/j.landurbplan.2023.104762 Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 16.0 [dataset]. Minneapolis, MN: IPUMS. 2021. http://doi.org/10.18128/D050.V16.0 U.S. Census Bureau (1942). U.S. Census of Population: 1940. Vol. I, Number of Inhabitants. U.S. Government Printing Office, Washington, D.C. U.S. Census Bureau (1964). U.S. Census of Population: 1960. Vol. I, Characteristics of the Population. Part I, United States Summary. U.S. Government Printing Office, Washington, D.C.

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

Abstract

For more than 150 years, the U.S. Department of Commerce, Bureau of the Census, conducted the census of agriculture. However, the 1997 Appropriations Act transferred the responsibility from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture for Guam is the second census to be conducted by the National Agricultural Statistics Service. The census of agriculture is taken to obtain agricultural statistics for each county, State (including territories and protectorates), and the Nation. The first U.S. agricultural census data were collected in 1840 as a part of the sixth decennial census. From 1840 to 1920, an agricultural census was taken as a part of each decennial census. Since 1920, a separate national agricultural census has been taken every 5 years.

The 2007 census is the 14th census of agriculture of Guam. The first, taken in 1920, was a special census authorized by the Secretary of Commerce. The next agriculture census was taken in 1930 in conjunction with the decennial census, a practice that continued every 10 years through 1960. The 1964 Census of Agriculture was the first quinquennial (5-year) census to be taken in Guam. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data-reference year to coincide with other economic censuses. After 1982, the agriculture census reverted to a 5-year cycle for the years ending in 2 and 7.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the farm defined as any place that raised or produced any agricultural products for sale or home consumption.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The census was a complete enumeration of all farm operators registered in the list compiled by the Guam Department of Agriculture. It was conducted by means of face to face interview filling paper questionnaires. The census frame was a list of farm operators compiled by the Guam Department of Agriculture.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

Processing: The processing of the 2007 Census of Agriculture for Guam was done by NASS. Each report form was reviewed and coded prior to data keying. Report forms not meeting the census farm definition were voided. The remaining report forms were examined for accuracy, consistency, and completeness. Reporting errors in computations, units of measures, data inconsistencies, and misplaced entries were corrected. Missing information was derived using reported data for similar type and size farms in nearby areas. After all the report forms had been reviewed and coded, the data were keyed and subjected to a thorough computer edit. The edit performed comprehensive checks for consistency and reasonableness, corrected erroneous or inconsistent data, supplied missing data based on similar farms, and assigned farm classification codes necessary for tabulating the data. All substantial changes to the data generated by the computer edits were reviewed and verified by analysts. Inconsistencies were reviewed, corrected, and keyed to a correction file. The corrected data were then tabulated by the computer and reviewed by analysts. Prior to publication, tabulated totals were reviewed by analysts to identify inconsistencies and potential coverage problems. Comparisons were made with previous census data, as well as other available data. The computer system provided the capability to review up-to-date tallies of all selected data items for various sets of criteria which included, but were not limited to, geographic levels, farm types, and sales levels. Data were examined for each set of criteria and a write-up (criticism) was produced for data that were inconsistent. Each criticism was then researched by examining individual data records contributing to the tabulated total. W hen necessary, data inconsistencies were resolved by carrying corrections to data records.

Data appraisal

No Post Enumeration Survey (PES) was performed. Quality checks included strict field supervision, clerical screening for farm activity, follow-up of non respondents, keying and transmittal of completed report forms, computerized editing of inconsistent and missing data, review and correction of individual records referred from the computer edit, review and correction of tabulated data, and electronic data processing.

The world's Jewish population has had a complex and tumultuous history over the past millennia, regularly dealing with persecution, pogroms, and even genocide. The legacy of expulsion and persecution of Jews, including bans on land ownership, meant that Jewish communities disproportionately lived in urban areas, working as artisans or traders, and often lived in their own settlements separate to the rest of the urban population. This separation contributed to the impression that events such as pandemics, famines, or economic shocks did not affect Jews as much as other populations, and such factors came to form the basis of the mistrust and stereotypes of wealth (characterized as greed) that have made up anti-Semitic rhetoric for centuries. Development since the Middle Ages The concentration of Jewish populations across the world has shifted across different centuries. In the Middle Ages, the largest Jewish populations were found in Palestine and the wider Levant region, with other sizeable populations in present-day France, Italy, and Spain. Later, however, the Jewish disapora became increasingly concentrated in Eastern Europe after waves of pogroms in the west saw Jewish communities move eastward. Poland in particular was often considered a refuge for Jews from the late-Middle Ages until the 18th century, when it was then partitioned between Austria, Prussia, and Russia, and persecution increased. Push factors such as major pogroms in the Russian Empire in the 19th century and growing oppression in the west during the interwar period then saw many Jews migrate to the United States in search of opportunity.

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

In 1800, the region of Germany was not a single, unified nation, but a collection of decentralized, independent states, bound together as part of the Holy Roman Empire. This empire was dissolved, however, in 1806, during the Revolutionary and Napoleonic eras in Europe, and the German Confederation was established in 1815. Napoleonic reforms led to the abolition of serfdom, extension of voting rights to property-owners, and an overall increase in living standards. The population grew throughout the remainder of the century, as improvements in sanitation and medicine (namely, mandatory vaccination policies) saw child mortality rates fall in later decades. As Germany industrialized and the economy grew, so too did the argument for nationhood; calls for pan-Germanism (the unification of all German-speaking lands) grew more popular among the lower classes in the mid-1800s, especially following the revolutions of 1948-49. In contrast, industrialization and poor harvests also saw high unemployment in rural regions, which led to waves of mass migration, particularly to the U.S.. In 1886, the Austro-Prussian War united northern Germany under a new Confederation, while the remaining German states (excluding Austria and Switzerland) joined following the Franco-Prussian War in 1871; this established the German Empire, under the Prussian leadership of Emperor Wilhelm I and Chancellor Otto von Bismarck. 1871 to 1945 - Unification to the Second World War The first decades of unification saw Germany rise to become one of Europe's strongest and most advanced nations, and challenge other world powers on an international scale, establishing colonies in Africa and the Pacific. These endeavors were cut short, however, when the Austro-Hungarian heir apparent was assassinated in Sarajevo; Germany promised a "blank check" of support for Austria's retaliation, who subsequently declared war on Serbia and set the First World War in motion. Viewed as the strongest of the Central Powers, Germany mobilized over 11 million men throughout the war, and its army fought in all theaters. As the war progressed, both the military and civilian populations grew increasingly weakened due to malnutrition, as Germany's resources became stretched. By the war's end in 1918, Germany suffered over 2 million civilian and military deaths due to conflict, and several hundred thousand more during the accompanying influenza pandemic. Mass displacement and the restructuring of Europe's borders through the Treaty of Versailles saw the population drop by several million more.

Reparations and economic mismanagement also financially crippled Germany and led to bitter indignation among many Germans in the interwar period; something that was exploited by Adolf Hitler on his rise to power. Reckless printing of money caused hyperinflation in 1923, when the currency became so worthless that basic items were priced at trillions of Marks; the introduction of the Rentenmark then stabilized the economy before the Great Depression of 1929 sent it back into dramatic decline. When Hitler became Chancellor of Germany in 1933, the Nazi government disregarded the Treaty of Versailles' restrictions and Germany rose once more to become an emerging superpower. Hitler's desire for territorial expansion into eastern Europe and the creation of an ethnically-homogenous German empire then led to the invasion of Poland in 1939, which is considered the beginning of the Second World War in Europe. Again, almost every aspect of German life contributed to the war effort, and more than 13 million men were mobilized. After six years of war, and over seven million German deaths, the Axis powers were defeated and Germany was divided into four zones administered by France, the Soviet Union, the UK, and the U.S.. Mass displacement, shifting borders, and the relocation of peoples based on ethnicity also greatly affected the population during this time. 1945 to 2020 - Partition and Reunification In the late 1940s, cold war tensions led to two distinct states emerging in Germany; the Soviet-controlled east became the communist German Democratic Republic (DDR), and the three western zones merged to form the democratic Federal Republic of Germany. Additionally, Berlin was split in a similar fashion, although its location deep inside DDR territory created series of problems and opportunities for the those on either side. Life quickly changed depending on which side of the border one lived. Within a decade, rapid economic recovery saw West Germany become western Europe's strongest economy and a key international player. In the east, living standards were much lower, although unemployment was almost non-existent; internationally, East Germany was the strongest economy in the Eastern Bloc (after the USSR), though it eventually fell behind the West by the 1970s. The restriction of movement between the two states also led to labor shortages in t...

Data are of numbers of breeding pairs of Adelie penguins(Pygoscelis adeliae)in the Ross Sea region colonies counted from aerial photographs. Photographs were taken from either a helicopter or from a C-130 Hercules. Photos were printed, populations counted, catalogued and filed. Since 2004, digital photography was used and in 2011 semi-automatic penguin counting software was developed to speed up the counting and validation process. Ross Island colonies were censused annually, colonies along the Victoria Land coast were censused as logistics allowed. Data collection started in 1981 in response to a proposed krill fishery and is ongoing. The aim was to locate all Adelie penguin colonies in the Ross Sea Region, and to count the numbers breeding at each from aerial photographs. The work attempts to relate annual changes in numbers of penguins breeding and their breeding success to weather, sea ice and other climate parameters in order to distinguish between responses due to natural events and those induced by commercial exploitation (such as fishing) or human disasters. A total of 39 colonies were found in the Ross Sea. The work is ongoing.

In 1800, the population of the area of modern-day Bangladesh was estimated to be just over 19 million, a figure which would rise steadily throughout the 19th century, reaching over 26 million by 1900. At the time, Bangladesh was the eastern part of the Bengal region in the British Raj, and had the most-concentrated Muslim population in the subcontinent's east. At the turn of the 20th century, the British colonial administration believed that east Bengal was economically lagging behind the west, and Bengal was partitioned in 1905 as a means of improving the region's development. East Bengal then became the only Muslim-majority state in the eastern Raj, which led to socioeconomic tensions between the Hindu upper classes and the general population. Bengal Famine During the Second World War, over 2.5 million men from across the British Raj enlisted in the British Army and their involvement was fundamental to the war effort. The war, however, had devastating consequences for the Bengal region, as the famine of 1943-1944 resulted in the deaths of up to three million people (with over two thirds thought to have been in the east) due to starvation and malnutrition-related disease. As the population boomed in the 1930s, East Bengal's mismanaged and underdeveloped agricultural sector could not sustain this growth; by 1942, food shortages spread across the region, millions began migrating in search of food and work, and colonial mismanagement exacerbated this further. On the brink of famine in early-1943, authorities in India called for aid and permission to redirect their own resources from the war effort to combat the famine, however these were mostly rejected by authorities in London. While the exact extent of each of these factors on causing the famine remains a topic of debate, the general consensus is that the British War Cabinet's refusal to send food or aid was the most decisive. Food shortages did not dissipate until late 1943, however famine deaths persisted for another year. Partition to independence Following the war, the movement for Indian independence reached its final stages as the process of British decolonization began. Unrest between the Raj's Muslim and Hindu populations led to the creation of two separate states in1947; the Muslim-majority regions became East Pakistan (now Bangladesh) and West Pakistan (now Pakistan), separated by the Hindu-majority India. Although East Pakistan's population was larger, power lay with the military in the west, and authorities grew increasingly suppressive and neglectful of the eastern province in the following years. This reached a tipping point when authorities failed to respond adequately to the Bhola cyclone in 1970, which claimed over half a million lives in the Bengal region, and again when they failed to respect the results of the 1970 election, in which the Bengal party Awami League won the majority of seats. Bangladeshi independence was claimed the following March, leading to a brutal war between East and West Pakistan that claimed between 1.5 and three million deaths in just nine months. The war also saw over half of the country displaced, widespread atrocities, and the systematic rape of hundreds of thousands of women. As the war spilled over into India, their forces joined on the side of Bangladesh, and Pakistan was defeated two weeks later. An additional famine in 1974 claimed the lives of several hundred thousand people, meaning that the early 1970s was one of the most devastating periods in the country's history. Independent Bangladesh In the first decades of independence, Bangladesh's political hierarchy was particularly unstable and two of its presidents were assassinated in military coups. Since transitioning to parliamentary democracy in the 1990s, things have become comparatively stable, although political turmoil, violence, and corruption are persistent challenges. As Bangladesh continues to modernize and industrialize, living standards have increased and individual wealth has risen. Service industries have emerged to facilitate the demands of Bangladesh's developing economy, while manufacturing industries, particularly textiles, remain strong. Declining fertility rates have seen natural population growth fall in recent years, although the influx of Myanmar's Rohingya population due to the displacement crisis has seen upwards of one million refugees arrive in the country since 2017. In 2020, it is estimated that Bangladesh has a population of approximately 165 million people.