A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Chris Briem of the University of Pittsburgh Center for Social and Urban Research has been producing extracts from the Census Bureau's PL 94-171 redistricting file.

On August 12th, the U.S. Census Bureau released the first detailed data from the 2020 Decennial Census of Population and Housing. Before this, the only data that has been released from the 2020 Census has been total population counts by state needed for the reapportionment of congressional seats. The data just released is known as PL 94-171 data and includes the final census enumeration of the population by race and ethnicity for counties, municipalities, and smaller levels of geography.

PL 94-171 data is used in the redrawing of the boundaries for federal, state, and local legislative districts, a process known as redistricting. This data includes housing unit counts, occupancy status for housing units, population totals, and population by race Hispanic/Latino origin, voting-age population (age 18+), and group quarters counts. The Census Bureau will be releasing additional data, including more detailed population and household statistics from the 2020 Census in the future.

Abstract (en): In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the 16th round of the survey (third quarter 2018) include self-reported health status, health insurance coverage, access to and use of health care, out-of-pocket health care costs, health care affordability, work experience, awareness of Medicaid work requirements, experiences with health care and social service providers, and health plan choice. Additional information collected by the survey includes age, gender, sexual orientation, marital status, education, race, Hispanic origin, United States citizenship, housing type, home ownership, internet access, income, employment status, and employer size. This study was conducted to provide information on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status, as well as timely data on important implementation issues under the Affordable Care Act (ACA). The Health Reform Monitoring Survey (HRMS) provides data on health insurance coverage, access to and use of health care, health care affordability, and self-reported health status. Beginning in the second quarter of 2013, each round of the HRMS also contains topical questions focusing on timely ACA policy issues. In the first quarter of 2015, the HRMS shifted from a quarterly fielding schedule to a semiannual schedule. The variables include original survey questions, household demographic profile data, and constructed variables which can be used to link panel members who participated in multiple rounds. 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: Created variable labels and/or value labels.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Response Rates: The HRMS response rate is roughly five percent each round. Datasets:DS0: Study-Level FilesDS1: Public-Use DataDS2: Restricted-Use Data Household population aged 18-64 Smallest Geographic Unit: Census region For each HRMS round a stratified random sample of adults ages 18-64 is drawn from the KnowledgePanel, a probability-based, nationally represented Internet panel maintained by Ipsos. The approximately 55,000 adults in the panel include households with and without Internet access. Panel members are recruited from an address-based sample frame derived from the United States Postal Service Delivery Sequence File, which covers 97 percent of United States households. The HRMS sample includes a random sample of approximately 9,500 nonelderly adults per quarter, including oversamples of adults with family incomes at or below 138 percent of the federal poverty line. Additional funders have supported oversamples of adults from individual states or subgroups of interest. However, the data file only includes data for adults in the general national sample and the income oversample. web-based survey

CLICK ON THE ABOVE IMAGE TO LAUNCH THE MAP - Healthcare access issues vary greatly between urban and rural areas of New Mexico. Launch the map to explore alternate ways to classify geographies as urban or rural. These classifications are often used for food access as well as healthcare access.BIBLIOGRAPHY WITH LINKS:US Census Bureau, Urban Area - Urban Cluster FAQ - https://www2.census.gov/geo/pdfs/reference/ua/2010ua_faqs.pdfAre the problems with Rural areas actually just a result of definitions that change?: "When a rural county grows, it transmutes into an urban one." - The real (surprisingly comforting) reason rural America is doomed to decline, https://www.washingtonpost.com/business/2019/05/24/real-surprisingly-comforting-reason-rural-america-is-doomed-decline/ (See also the complete study - http://programme.exordo.com/2018annualmeeting/delegates/presentation/130/ )Rural Definitions for Health Policy, Harvey Licht, a presentation for the University of New Mexico Center for Health Policy: : http://nmcdc.maps.arcgis.com/home/item.html?id=7076f283b8de4bb69bf3153bc42e0402Rural Definitions for Health Policy, update of 2019, Harvey Licht, a presentation to the NMDOH Quarterly Epidemiology Meeting, November, 2019 - http://www.arcgis.com/home/item.html?id=a60a73f4e5614eb3ab01e2f96227ce4bNew Mexico Rural-Urban Counties Comparison Tables - October 2017, Harvey Licht, A preliminary compilation for the National Conference of State Legislators Rural Health Plan Taskforce : https://nmcdc.maps.arcgis.com/home/item.html?id=d3ca56e99f8b45c58522b2f9e061999eNew Mexico Rural Health Plan - Report of the Rural Health Planning Workgroup convened by the NM Department of Health 2018-2019 - http://nmcdc.maps.arcgis.com/home/item.html?id=d4b9b66a5ca34ec9bbe90efd9562586aFrontier and Remote Areas Zip Code Map - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=56b4005256244499a58f863c17bbac8aHOUSING ISSUES, RURAL & URBAN, 2017 - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=3e3aeabc04ac4672994e25a1ec94df83FURTHER READING:What is Rural? Rural Health Information Hub: https://www.ruralhealthinfo.org/topics/what-is-ruralDefining Rural. Research and Training Center on Disability in Rural Communities: http://rtc.ruralinstitute.umt.edu/resources/defining-rural/What is Rural? USDA: https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/National Center for Health Statistics Urban–Rural Classification Scheme: https://www.cdc.gov/nchs/data_access/urban_rural.htm.Health-Related Behaviors by Urban-Rural County Classification — United States, 2013, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6605a1.htm?s_cid=ss6605a1_wExtending Work on Rural Health Disparities, The Journal of Rural Health: http://onlinelibrary.wiley.com/doi/10.1111/jrh.12241/fullMinority Populations Driving Community Growth in the Rural West, Headwaters Economics: https://headwaterseconomics.org/economic-development/trends-performance/minority-populations-driving-county-growth/ Methodology - https://headwaterseconomics.org/wp-content/uploads/Minorities_Methods.pdfThe Role of Medicaid in Rural America, Kaiser Family Foundation: http://www.kff.org/medicaid/issue-brief/the-role-of-medicaid-in-rural-america/The Future of the Frontier: Water, Energy & Climate Change in America’s Most Remote Communities: http://frontierus.org/wp-content/uploads/2017/09/FUTURE-OF-THE-FRONTIER_Final-Version_Spring-2017.pdfRural and Urban Differences in Passenger-Vehicle–Occupant Deaths and Seat Belt Use Among Adults — United States, 2014, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6617a1.htm

Abstract

A crosswalk matching US ZIP codes to corresponding CBSA (core-based statistical area)

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

CBSA definition

A core-based statistical area (CBSA) is a U.S. geographic area defined by the Office of Management and Budget (OMB) that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by commuting. Areas defined on the basis of these standards applied to Census 2000 data were announced by OMB in June 2003. These standards are used to replace the definitions of metropolitan areas that were defined in 1990. The OMB released new standards based on the 2010 Census on July 15, 2015.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact authors

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

The Index of Relative Rurality (IRR) is a continuous, threshold-free, and unit-free measure of rurality.

The original version of the IRR was proposed by Waldorf (2006, http://ageconsearch.umn.edu/handle/21383) as an alternative to the traditional discrete threshold-based classifications, such as the Rural-urban Continuum Code and the Urban Influence Code. Waldorf and Kim (2015) re-designed measuring the index and applied it to publish improved county-level IRR for 2000 and 2010. IRR 2020 was measured by the same method suggested in 2015 except for the network data (North American Roads*) due to the data availability. (* Bureau of Transportation Statistics (BTS), https://geodata.bts.gov/datasets/usdot::north-american-roads/about).

The IRR has three significant advantages over typology-based rurality measures. (1) It is spatially flexible in that it can be designed for any spatial units; (2) it is a relative measure and thus embeds rurality in the broader system of settlements; (3) it is analytically more easily handled than threshold-based typologies.

The IRR ranges between 0 (low level of rurality, i.e., urban) and 1 (most rural). Four steps are involved in its design:

1. Identifying the dimensions of rurality: population size, density, remoteness, and built-up area.
2. Selecting measurable variables to adequately represent each dimension:
a. Size: logarithm of population size
b. Density: logarithm of population density.
c. Remoteness: network distance.
d. Built-up area: urban area (as defined by the US Census Bureau) as a percentage of total land area.
3. Re-scaling the variables onto bounded scales that range from 0 to 1.
4. Selecting a link function: unweighted average of the four re-scaled variables.

IRR 2020 - County-level Map

Please cite this work:

DOI: 10.5281/zenodo.7675745

For more information:
Waldorf, Brigitte, and Ayoung Kim. 2015. "Defining and Measuring Rurality in the US: From Typologies to Continuous Indices." Commissioned paper prepared for the National Academies of Sciences Workshop on Rationalizing Rural Classifications, April 2015, Washington, DC. http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_168031.pdf

Acknowledgment:

** This project was supported by the Agricultural and Food Research Initiative Competitive Program of the USDA National Institute of Food and Agriculture (NIFA), grant number 2020-67019-30772.

Contact:

Ayoung Kim | a.kim@msstate.edu | Dept. of Agricultural Economics | Mississippi State University

Brigitte Waldorf | bwaldorf@purdue.edu

Abstract

A crosswalk dataset matching US ZIP codes to corresponding census tracts

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

Census tract definition

A census tract, census area, census district or meshblock is a geographic region defined for the purpose of taking a census. Sometimes these coincide with the limits of cities, towns or other administrative areas and several tracts commonly exist within a county. In unincorporated areas of the United States these are often arbitrary, except for coinciding with political lines.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact information

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

Abstract

The Benin EHCVM 2018/19 is implemented by the National institute of Statistics and Economical Analysis (INSAE) with support from the World Bank and the WAEMU Commission. The objective of the program is to strengthen the capacity of its member countries (Benin, Burkina Faso, Cote d’Ivoire, Guinee Bissau, Mali, Niger, Senegal, and Togo) to conduct living conditions surveys that meet harmonized, regional standards and to make the collected micro-data publicly accessible. The EHCVM is a nationally representative survey of 8,000 households, which are also representative of the geopolitical zones (at both the urban and rural level).

The survey uses two main survey instruments: a household/individual questionnaire, and a community-level questionnaire. The surveys took place in two waves with each wave covering half of the sample. The first wave was fielded between October 2018 and December 2018, while the second wave occurred between April 2019 and July 2019. The two-wave approach was chosen to account for seasonality of consumption.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Benin EHCVM 2018/19 used the 2013 Census of Population and Housing (RGPH) as the sampling frame. This frame contains 10032 enumeration areas, is nationally representative, and covers all regions with urban and rural areas surveyed in all regions apart from Littoral, a purely urban region. In Benin, the survey design decided on the sample size using the poverty rate - obtained from the 2011 Integrated Modular Survey on living conditions of households - as a variable of interest. Then the survey design split the decided sample size among regions considering the number of households in the region and the necessity to minimize the relative error. The survey design also defined the domains as country, urban and rural areas, and each of the 12 regions. Taking this into account, 23 explicit sample strata were selected.

Upon deciding on the sample size and repartition, the survey design team implemented a 2-stage sampling methodology. At the first stage, 670 enumeration areas (EAs) were selected with Probability Proportional to Size (PPS) using the 2013 RGPH and the number of households as a measure of size. In the second stage, 12 households were selected in each enumeration area randomly.

The total estimated survey sample size was 8040 households - 3960 from urban areas and 4080 from rural areas. After that, the survey design randomly divided each enumeration area into two equal groups. The survey team interrogated the first group in wave 1 and the other in wave 2. Finally, for various reasons, including availability and quality monitoring, the final sample size comprises 8012 households, including 3940 households from urban areas and 4072 households from rural areas. In wave one, the survey teams interviewed 3997 households (1940 in urban areas and 2057 in rural areas. In wave two, the teams interviewed 4015 households (2000 in urban areas and 2015 in rural areas).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Benin ECHVM 2018/19 consists of two questionnaires for each of the two visits. The Household Questionnaires was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

EHCVM 2018/19 Household Questionnaire: The Households Questionnaire provides information on demographics; education; health; employment (including activity-related information, primary and secondary employments); nonjob revenues; saving and credit (including information for payments due for 15 years old members of the household); food consumption; food security; nonfood consumption; nonagricultural enterprises; housing; household’s assets; transfers (received and sent); shocks and survival strategies; safety nets; agriculture (including information on plots, costs of inputs, and crops); livestock; fishing; agricultural equipment; and a module that provides indicators to helps users situate the household on the poverty spectrum based on subjective considerations and comparative indicators.

EHCVM 2018/19 Community Questionnaire: The Community Questionnaire solicits information on general community’s characteristics; community access to infrastructure and to social services; community agricultural activity; community participation; and local retail price information.