The Population online databases contain data from the US Census Bureau. The Census Estimates online database contains county-level population counts for years 1970 - 2000. The data comprise the April 1st Census counts for years 1970, 1980, 1990 and 2000, the July 1st intercensal estimates for years 1971-1979 and 1981-1989, and the July 1st postcensal estimates for years 1991-1999. The Census Projections online database contains population projections for years 2004-2030 by year, state, age, race and sex, produced by the Census Bureau in 2005. The data are produced by the United States Department of Commerce, U.S. Census Bureau, Population Division.

County Business Patterns (CBP) is an annual series that provides economic data by industry at the U.S., State, County and Metropolitan Area levels. This series includes the number of establishments, employment during the week of March 12, first quarter payroll, and annual payroll. CBP provides statistics for businesses with paid employees for the U.S., Puerto Rico, and the Island Areas. Census Bureau staff identified a processing error that affects selected data from the 2014 County Business Patterns (CBP). As a result, we suppressed 2014 employment and payroll totals in the Health Care and Social Assistance sector (Sector 62) for the following geographies: U.S.; Michigan; Battle Creek, MI metro area; Calhoun County, MI; and the 3rd congressional district of Michigan. This processing error did not affect other sectors. While suppressed values can be derived by subtraction, we do not recommend using the derived values in any analyses. The Census Bureau plans to release revised statistics at a later date.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

{"definition": "% change in participation between 2000 and 2005", "availableYears": "2000-2005", "name": "% change in participation between 2000 and 2005", "units": "% change", "shortName": "PRG00_05", "geographicLevel": "County", "dataSources": "SNAP participation and population estimates at the county level are provided the U.S. Census Bureau."}

© % change This layer is a component of ERS SNAP Data System.

This map service contains maps and data relevant to SNAP (Supplemental Nutrition Assistance Program) participation and benefits

© Detailed documentation on data sources used in the ERS SNAP Data System map services is available here: http://www.ers.usda.gov/data-products/supplemental-nutrition-assistance-program-(snap)-data-system/documentation.aspx

The Census Bureau's Small Area Health Insurance Estimates (SAHIE) program produces estimates of health insurance coverage for states and all counties. These data are 2005 estimates of health insurance coverage by age, sex, race, Hispanic origin, and income categories at the state level and by age, sex, and income categories at the county level.

Population: Sichuan: Leshan: Emeishan data was reported at 433.000 Person th in 2014. This records a decrease from the previous number of 433.600 Person th for 2013. Population: Sichuan: Leshan: Emeishan data is updated yearly, averaging 433.600 Person th from Dec 2004 (Median) to 2014, with 11 observations. The data reached an all-time high of 436.000 Person th in 2008 and a record low of 430.000 Person th in 2005. Population: Sichuan: Leshan: Emeishan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: County Level Region.

VITAL SIGNS INDICATOR
Income (EC4)

FULL MEASURE NAME
Household income by place of residence

LAST UPDATED
January 2023

DESCRIPTION
Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 4Pb (1970)
Form STF3 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B19001 (2005-2021; household income by place of residence)
Form B19013 (2005-2021; median household income by place of residence)
Form B08521 (2005-2021; median worker earnings by place of employment)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Income derived from the decennial Census data reflects the income earned in the prior calendar year, whereas income derived from the American Community Survey (ACS) data reflects the prior 12 month period; note that this inconsistency has a minor effect on historical comparisons (see Income and Earnings Data section of the ACS General Handbook - https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch09.pdf). ACS 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

Quintile income for 1970-2000 is imputed from decennial Census data using methodology from the California Department of Finance. Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index (CPI) for 2021 specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data uses national CPI for 1970. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

Courtesy of MetroPulse

Abstract (en): This file contains an array of county characteristics by which researchers can investigate contextual influences at the county level. Included are population size and the components of population change during 2000-2005 and a wide range of characteristics on or about 2005: (1) population by age, sex, race, and Hispanic origin, (2) labor force size and unemployment, (3) personal income, (4) earnings and employment by industry, (5) land surface form topography, (6) climate, (7) government revenue and expenditures, (8) crimes reported to police, (9) presidential election results (10) housing authorized by building permits, (11) Medicare enrollment, and (12) health profession shortage areas. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. All counties in the United States Smallest Geographic Unit: county 2008-01-24 A number of minor edits were made to the codebook, none of which substantially affected the variable descriptions. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (2003-IJ-R-004). United States Department of Health and Human Services. National Institutes of Health (5 R01 HD042564-05).

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning from 1999 to 2015.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

Abstract (en): The Census of Jail Inmates is the eighth in a series of data collection efforts aimed at studying the nation's locally-administered jails. Beginning in 2005, the National Jail Census was broken out into two collections. The 2005 Census of Jail Inmates (CJI) collects data on the facilities' supervised populations, inmate counts and movements, and persons supervised in the community. The forthcoming 2006 Census of Jail Facilities collects information on staffing levels, programming, and facility policies. Previous censuses were conducted in 1970, 1972, 1978, 1983, 1988, 1993, and 1999. The 2005 CJI enumerated 2,960 locally administered confinement facilities that held inmates beyond arraignment and were staffed by municipal or county employees. Among these were 42 privately-operated jails under contract to local governments and 65 regional jails that were operated for two or more jail authorities. In addition, the census identified 12 facilities maintained by the Federal Bureau of Prisons that functioned as jails. These 12 facilities, together with the 2,960 nonfederal facilities, brought the number of jails in operation on June 30, 2005, to a nationwide total of 2,972. The CJI supplies data on characteristics of jails such as admissions and releases, growth in the number of jail facilities, changes in their rated capacities and level of occupancy, crowding issues, growth in the population supervised in the community, and changes in methods of community supervision. The CJI also provides information on changes in the demographics of the jail population, supervision status of persons held, and a count of non-United States citizens in custody. The data are intended for a variety of users, including federal and state agencies, local officials in conjunction with jail administrators, researchers, planners, and the public. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. All locally-administered jails in the United States and facilities maintained by the Federal Bureau of Prisons that function as jails. The 2005 Census of Jail Inmates enumerated 2,960 locally-administered jails that held inmates beyond arraignment (usually 72 hours) and were staffed by municipal or county employees. Among these were 42 privately-operated jails under contract to local governments and 65 regional jails that were operated for two or more jail authorities. In addition, the census identified 12 facilities maintained by the Federal Bureau of Prisons that functioned as jails. These 12 facilities, together with the 2,960 nonfederal facilities, brought the number of jails in operation on June 30, 2005, to a nationwide total of 2,972. Excluded from the census were temporary holding facilities, such as drunk tanks and police lockups, that do not hold persons after they are formally charged in court (usually within 72 hours of arrest). Also excluded were state-operated facilities in Alaska, Connecticut, Delaware, Hawaii, Rhode Island, and Vermont, which have combined jail-prison systems. Fourteen independently operated jails in Alaska were included.

Population: Rural: Sichuan: Yibin: Pingshan data was reported at 267.000 Person th in 2012. This records a decrease from the previous number of 270.000 Person th for 2011. Population: Rural: Sichuan: Yibin: Pingshan data is updated yearly, averaging 268.000 Person th from Dec 2004 (Median) to 2012, with 9 observations. The data reached an all-time high of 270.000 Person th in 2011 and a record low of 260.000 Person th in 2005. Population: Rural: Sichuan: Yibin: Pingshan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: Rural: County Level Region.

This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

VITAL SIGNS INDICATOR
Income (EC4)

FULL MEASURE NAME
Household income by place of residence

LAST UPDATED
January 2023

DESCRIPTION
Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 4Pb (1970)
Form STF3 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B19001 (2005-2021; household income by place of residence)
Form B19013 (2005-2021; median household income by place of residence)
Form B08521 (2005-2021; median worker earnings by place of employment)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Income derived from the decennial Census data reflects the income earned in the prior calendar year, whereas income derived from the American Community Survey (ACS) data reflects the prior 12 month period; note that this inconsistency has a minor effect on historical comparisons (see Income and Earnings Data section of the ACS General Handbook - https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020_ch09.pdf). ACS 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

Quintile income for 1970-2000 is imputed from decennial Census data using methodology from the California Department of Finance. Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index (CPI) for 2021 specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data uses national CPI for 1970. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

Abstract

Susenas is a survey designed to collect socio-demographic data in large area. The data collected were related to the fields of education, health / nutrition, housing / environmental, socio-cultural activities, consumption and household income, trips, and public opinion about the welfare of household. In 1992, Susenas data collection system has been updated, the information used to develop indicators of welfare (Welfare) contained in the module (information collected once every three years) drawn into the core (group information is collected each year).

In 2005 Susenas implement the module consumption / expenditure and household income. The data collected is the basic ingredient for calculating estimates of poverty based on consumption module Susenas three years (the latest data of 2002). However, given the poverty alleviation is a priority program of the current government; the Central bureau of statistic (BPS) attempted to provide data-poor national estimates on an annual basis. With the collecting data consumption / expenditure details every year it will be estimated annual number of poor people.

To meet the data needs of the government about the development of poor people every year, Panel Susenas collected the consumption and expenditure module data with the total sample of 10,000 households in 2003. The number of samples is only able to estimate the national poverty, while the demands of the availability data of poverty rate up to provincial level is increasing.

Geographic coverage

National coverage, representative to the district level

Analysis unit

Household Members (Individual) and Household

Universe

Implementation Susenas 2005 includes 278,352 households spread across. all geografls regions of Indonesian , with details of 68 288 households sample core-module and 210 064 households core sample (without modules), and 10,640 households sample of Susenas panel that is part of households sample core-module.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. Susenas core

The design of sampling Susenas 2005 and Supas 2005 was conducted in an integrated manner in order to estimates some of the same variable can be done in an integrated manner. Sampling procedures Susenas 2005 for a county / city are as follows:

• Phase 1, from sample frame census block are to be selected census block nh (h = 1, for urban; h = 2, for rural) by probability proportional to size (pps) method whereas size is the number of households from P4B census result (April 2004).

• Phase 2, from nh selected nh census block for Susenas 2005, further referred to as census blocks Susenas. Household listing is conducted to all selected census blocks/sub-blocks.

• Phase 3, selecting m = 16 households in each census block selected systematically, for census block payloads of more than 150 households, it is necessary to selection of a sub-block census in PPS systematically with the size of the number of households P4B enumeration (April 2004).

1. Consumption Module / Household Expenditure and Household income with module sample sizes of consumption / expenditure and household income are designed for presentation at the provincial level. The module sample is section of subsample of selected sample for data estimate in district / city level (Census Block NSES), urban and rural areas. The subsample selected by Systematic Linear Sampling from selected census blocks in each district / city for urban and rural areas. Further census blocks selected (subsample) is the census block core-module, due beside enumerated with questionnaire module, also enumerated the core questionnaire. In other words, the census blocks that will be used to estimates at the provincial level (census block core-module) selected by systematic linear sampling from a list of selected census blocks in each district / city (census block core). Core-module census blocks is not selected 2004 Susenas is core census block.

2. Panel Module consumption /expenditure and household income in addition to the design of the sample selection core-module consumption / expenditure and household income above, in Susenas 2005 was also designed to perform the method of survey panel module consumption / expenditure and household income, where sample census block and panel sample of households (repetition) Susenas 2005 (the implementation in February 2005).

For the presentation of the poverty rate at the national level (February 2005), namely the implementation of the survey panel Susenas 2005 (February 2005), the number of census blocks will be selected from a sample of census blocks Susenas core-module (Susenas 2005, June 2005). The sample selection will be conducted in systematic sampling.

Mode of data collection

Face-to-face

Population: Yunnan: Wenshan: Yanshan data was reported at 475.800 Person th in 2022. This records an increase from the previous number of 473.000 Person th for 2021. Population: Yunnan: Wenshan: Yanshan data is updated yearly, averaging 469.600 Person th from Dec 2004 (Median) to 2022, with 19 observations. The data reached an all-time high of 476.600 Person th in 2020 and a record low of 447.900 Person th in 2005. Population: Yunnan: Wenshan: Yanshan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: County Level Region.

This collection provides information on live births in the United States during calendar year 2005. The natality data in these files are a component of the vital statistics collection effort maintained by the federal government. Birth data is limited to births occurring in the United States to United States residents and nonresidents. Births occurring to United States citizens outside of the United States are not included in this data collection. Part 1 contains data on births occurring within the United States, while Part 2 contains data on births occurring in the United States territories of Puerto Rico, the Virgin Islands, Guam, American Samoa, and the Commonwealth of the Northern Mariana Islands. Beginning in 2005, the United States file no longer includes geographic detail (e.g., mother's state of residence). Geographic variables for the United States Territories file include the territory and county in which the birth occurred and in which the mother resided. Other variables describe the place of delivery, who was in attendance, and medical and health data such as the method of delivery, prenatal care, tobacco and alcohol use during pregnancy, pregnancy history, medical risk factors, and infant health characteristics. Birth and fertility rates and other statistics related to this study can be found in the National Vital Statistics Report in the codebook documentation. Demographic variables include the child's sex and month and year of birth, the parent's age, race, and ethnicity, as well as the mother's marital status, education level, and residency status.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the yehttps://healthdata.gov/d/2sz9-6c59ars 1999-2006. These data are available in two separate data sets: one data set for years 1999-2004 with 3 race groups, and another data set for years 2005-2006 with 4 race groups and 3 Hispanic origin categories. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., state, and county), age group (including infants), race, Hispanic ethnicity (years 2005-2006 only), sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes). The data are produced by the National Center for Health Statistics.

VITAL SIGNS INDICATOR Rent Payments (EC8)

FULL MEASURE NAME Median rent payment

LAST UPDATED August 2019

DESCRIPTION Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE U.S. Census Bureau: Decennial Census 1970-2000 https://nhgis.org Note: Count 1 and Count 2; Form STF1; Form SF3a

U.S. Census Bureau: American Community Survey 2005-2017 http://api.census.gov Note: Form B25058; 1-YR

Bureau of Labor Statistics: Consumer Price Index 1970-2017 http://www.bls.gov/data/ Note: All Urban Consumers Data Table (by metro)

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Rent data reflects median rent payments rather than list rents (refer to measure definition above). Larger geographies (metro and county) rely upon ACS 1-year data, while smaller geographies rely upon ACS 5-year rolling average data. 1970 Census data for median rent payments has been imputed by ABAG staff as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

Population: Fujian: Longyan: Liancheng data was reported at 332.854 Person th in 2023. This records a decrease from the previous number of 335.766 Person th for 2022. Population: Fujian: Longyan: Liancheng data is updated yearly, averaging 334.412 Person th from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 348.400 Person th in 2018 and a record low of 326.087 Person th in 2005. Population: Fujian: Longyan: Liancheng data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: County Level Region.