Patrick Moehrke's HMD.jl

(mortality, geographic, historical, data and materials, Julia package)

From Patrick Moehrke, a Julia package that helps users easily access data from the a Human Mortality Database (HMD). From its linked page, the package:
  - Downloads and creates a DataFrame object for a given country, type of table, and interval.
  - Provides the functionality to save output as CSVs.
  - Supports full country and table names.
  - Displays available countries and tables.
A terrific resource for Julia users to access the HMD, and for HMD users to work with Julia. –February 2024 ^

Jerônimo O. Muniz' mslt: Multistate Life Tables Using Stata

(mortality, texts and instruction, Stata package)

From the abstract of the above-linked Stata Journal article for the package, "The mslt command calculates the functions of a multistate life table and plots a graph of conditional and unconditional life expectancies by time. The command provides linear and exponential solutions to estimate the number of individuals, transitions, probabilities, person-years, and years of life in a given cohort and state of occupancy. The input data are time-specific transition rates (or survivorship proportions) between nonabsorbing and at most one absorbing state. In addition to the mean age at transfer between states, mslt calculates the following summary measures: the mean age, the probability of dying, the average duration, and the proportion of life spent in a specific state." This work by Professor Jerônimo O. Muniz of the Federal University of Minas Gerais may be an ideal resource for many demographers who are learning about or developing multi-state life tables. –March 2023 (updated February 2024) ^

US Census Bureau's Global Population Mapping and Spatial Analysis Products

(population estimates, population projections, geographic, data and materials, web interface, Excel spreadsheets, mapping files)

From the linked page, "The U.S. Census Bureau has supplied global subnational data for five decades. The essential role of population data in humanitarian relief and disaster planning was described in a 2007 National Research Council report. Our most recent efforts have sought to fulfill the needs described in this report. "Our products provide data on global population patterns according to both administrative areas and grid cells. Data inputs include census data from every country and territory that conducts a census, administrative boundary maps from national and international mapping agencies, and high- and medium-resolution satellite imagery. Population data can be projected forward to the present and linked to administrative boundary maps to allow mapping of population density and other variables." Resources include the very neat Subnational Population Data by Geographic Area with "5-year age/sex group population estimates and projections for various years from 2000 through 2030 for countries sharing bilateral efforts with the U.S. Government as part of the United States President’s Emergency Plan for AIDS Relief (PEPFAR)" and Gridded Population Mapping (Demobase), "an interagency project, led by the U.S. Census Bureau, to develop methods for creating high-resolution gridded population maps based on a combination of census data and satellite image analysis." –January 2023 ^

Damien Forthommme's ipfn: Iterative Proportional Fitting with N Dimensions

(population estimates, surveys, migration, Python package)

From the README, "Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions)." The package includes NumPy and pandas versions of the algorithm. Here is a link to application and comparison of it in a Jupyter Notebook. –January 2023 ^

Riffe, Boe, and Hilton's HMDHFDplus: Read HMD and HFD Data from the Web

(mortality, fertility, geographic, historical, data and materials, R package)

From the package description, HMDHFDplus offers "Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<https://www.fertilitydata.org>), The Japanese Mortality Database (<https://www.ipss.go.jp/p-toukei/JMD/index-en.html>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized." This package by Tim Riffe, Carl Boe, and Jason Hilton, with contributions from Joshua Goldstein and Stephen Holzman, provides tools to readily access and manage data from some premier demographic research databases. –August 2022 ^

The Human Life Table Database

(mortality, geographic, historical, data and materials, web interface)

From the front page of the site: "The Human Life-Table Database (HLD) is a collection of population life tables for a multitude of countries covering many years. Most of the HLD life tables are for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Some of the HLD life tables are non-official life tables produced by researchers. The HLD documents the evolution of human mortality by providing a quantitative life-table description of mortality patterns. The database also supplies information on calculation techniques as well as on ways of publishing mortality data used in different countries in different times. The HLD is part of the Human Mortality Database (HMD) project." –June 2022 ^

Guy Abel's migest: Tools for Estimating, Measuring and Working with Migration Data

(migration, R package)

From the reference manual for the above-linked package by Guy Abel, a professor and research scholar with Shanghai University and the International Institute for Applied Systems Analysis: "The migest package contains a collection of R functions for indirect methods to estimate bilateral migration flows in the presence of partial or missing data. Methods might be relevant to other categorical data situations on non-migration data, where for example, marginal totals are known and only auxiliary bilateral data is available." This R package includes numerous functions to estimate and describe migration. –June 2022 ^

Gupta and Sudharsanan's Replication Code for Life Expectancy Disparity Analysis with the Indian NFHS

(mortality, surveys, Stata code, R code)

This Stata and R code from Aashish Gupta (a David E. Bell Fellow at the Harvard Center for Population and Development Studies) and Nikkil Sudharsanan (Assistant Professor of Behavioral Science for Disease Prevention and Health Care at the Technical University of Munich) replicates materials for their Population and Development Review article "Large and Persistent Life Expectancy Disparities between India's Social Groups" (preprint version). The work uses the National Family and Health Surveys - India's nationally representative Demographic and Health Survey - and includes use and comparison of household-survey data for life table estimation, along with Arriaga decomposition of life expectancy, and statistical uncertainty estimation by a cluster-bootstrap approach. Replication materials for a related PNAS article by Vyas, Hathi, and Gupta (preprint version) also offer resources for estimating age-specific mortality rates from questions on recent deaths in the household, using the Government of India's Annual Health Survey, and allow a demographic standardization using micro data. Useful resources for guidance on this important research, advanced mortality analysis, and survey materials. –May 2022 ^

US Census Bureau's Population and Housing Unit Estimates

(population estimates, geographic, historical, data and materials, web interface, Excel spreadsheets)

From the related About page: "The Census Bureau's Population Estimates Program (PEP) produces estimates of the population for the United States, its states, counties, cities, and towns, as well as for the Commonwealth of Puerto Rico and its municipios. Demographic components of population change (births, deaths, and migration) are produced at the national, state, and county levels of geography. Additionally, housing unit estimates are produced for the nation, states, and counties." The Census Bureau's PEP data include demographic charactersitics for all US counties, states, and the nation, and "...are used in federal funding allocations, as survey controls, as denominators for vital rates and per capita time series, and as indicators of recent demographic changes." –March 2022 ^

The Latin American Human Mortality Database

(mortality, geographic, historical, data and materials, web interface)

Description from the site's home page: "The web-site aims at disseminating human mortality data and literature of human mortality in Latin America, in order to provide detailed information for researchers, students, policy makers and the general public interested in knowing trends and developments in the study of mortality in the region. The project is inspired by The Human Mortality Database (www.mortality.org) and is the result of joint work of Prof. B. Urdinola of the National University of Colombia, Bogota Department of Statistics and Professor Bernardo Lanza Queiroz Depto of Demography and CEDEPLAR, UFMG, Belo Horizonte-Brazil. The project now is funded by our own resources. In the past, we received funding from the Population Association of America (www.popassoc.org) and the Research Direction of Universidad Nacional de Colombia-Bogotá (DIB www.dib.unal.edu.co). At present the database contains detailed information on mortality for seven countries in Latin America: Argentina, Brazil, Colombia, Chile, Ecuador, Mexico and Peru. All information is broken down by age, sex, region and cause of death. Additionally there is information on the academic literature on the study of mortality for these same countries." A fantastic resource to access mortality statistics and information for Latin American countries. –February 2022 (updated November 2023) ^

Carl Schmertmann's R Code for Kannisto Extrapolation of Mortality Rates to Oldest Ages

(mortality, R code)

Described in the explanatory notes (2019) by Prof. Schmertmann, and in Thatcher, Kannisto, and Vaupel (1998), the Kannisto model of oldest-age mortality makes use of Kannisto's observation that a logit function can provide a good fit for oldest-age mortality rates in modern populations. This can be useful for extrapolating mortality to oldest ages. –February 2022 ^

Yeung, Alexander, and Riffe's rcbayes: Rogers-Castro Migration Age Schedules with Bayesian Models

(migration, R package)

From the reference manual for the above-linked package by Jessie Yeung, Monica Alexander, and Tim Riffe: "A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>". The rcbayes package is a very useful resource to work with the highly non-linear Rogers-Castro model, and is further described and evaluated in a related Demographic Research article. –February 2022 (updated December 2023) ^

Kyle Walker's Analyzing US Census Data: Methods, Maps, and Models in R

(surveys, geographic, data and materials, texts and instruction, R code, web interface)

From the preface for the above-linked online version of Prof. Walker's book: "Census data are widely used in the United States across numerous research and applied fields, including education, business, journalism, and many others. Until recently, the process of working with US Census data has required the use of a wide array of web interfaces and software platforms to prepare, map, and present data products. The goal of this book is to illustrate the utility of the R programming language for handling these tasks, allowing Census data users to manage their projects in a single computing environment." –February 2022 ^

United Nations' World Population Prospects

(population estimates, population projections, fertility, migration, mortality, review tools, geographic, historical, data and materials, web interface, Excel spreadsheets, R packages)

From the main page: "The 2022 Revision of World Population Prospects is the twenty-seventh edition of official United Nations population estimates and projections that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. "The main results are presented in a series of Excel files displaying key demographic indicators for each UN development group, World Bank income group, geographic region, Sustainable Development Goals (SDGs) region, subregion and country or area for selected periods or dates within [1950 to 2100]. For advanced users who need to use these data in a database form or statistical software, we recommend to use the CSV format for bulk download. Special Aggregates also provide additional groupings of countries." There are also related R packages (hosted by CRAN) that can be used to explore World Population Prospects data, including packages wpp2022, wpp2019, wpp2017, wpp2015, wpp2012, and wpp2010, as well as the wppExplorer package (authored and maintained by Hana Ševčíková), which provides a Shiny interface to explore the data. Here is a version of the wppExplorer interface that's hosted online by the BayesPop research group at the University of Washington. –April 2021 (updated August 2022) ^

Ševčíková, Li, and Gerland's MortCast: Estimation and Projection of Age-Specific Mortality Rates

(mortality, R package)

This R package by Hana Ševčíková, Nan Li, and Patrick Gerland applies age-specific mortality rate estimation and prediction models used with the related book-chapter (pre-print). The package includes functions for application of the Kannisto (with Coherent Kannisto), Lee-Carter, pattern of mortality decline (PMD), Coale-Demeny, and Log-Quadratic models - each useful and influential. –April 2021 ^

Raftery et al's BayesPop: Probabilistic Population Projection Software

(population projections, fertility, migration, mortality, R package)

From the project's About page: "We are developing a statistical framework for probabilistic projection and estimation of population, fertility, mortality and migration for all countries, in collaboration with the United Nations Population Division. This extends the deterministic methods that have been standard since the 1940s, to account for uncertainty and measurement error in data." From the Software page, the R packages include bayesTFR (for probabilistic projection of the total fertility rate), bayesLife (for probabilistic projection of life expectancy), and bayesPop (for probabilistic projection of population, using bayesTFR and bayesLife). Each package is also available on CRAN, and development versions (along with many related works) are available on the associated GitHub page. The GitHub page also includes the bayesLifeHIV package, for probabilistic projection of life expectancy for countries with generalized HIV/AIDS epidemics. Coauthors of the suite of packages include Adrian Raftery, Hana Ševčíková, Leontine Alkema, Peiran Liu, Bailey Fosdick, Patrick Gerland, Jennifer Chunn, and Thomas Buettner. Top-notch applied demography resources to both learn about and apply Bayesian population projections. –April 2021 (updated March 2022) ^

Kyle Walker's tidycensus

(surveys, fertility, migration, geographic, data and materials, R package)

From Prof. Walker's tidycensus webpage (links added): "tidycensus is an R package that allows users to interface with the US Census Bureau’s decennial Census and five-year American Community Survey APIs and return tidyverse-ready data frames, optionally with simple feature geometry included." A fantastic resource for accessing, handling, mapping US Census Bureau data. –July 2020 ^

US Census Bureau's data.census.gov

(surveys, population estimates, fertility, migration, geographic, data and materials, web interface)

If you get a request for demographic data in the US, or if you have your own request for demographic data in the US, there is a good chance that you can make use of the US Census Bureau's data.census.gov. Data.census.gov has detailed tables from the Census Bureau's American Community Survey as well as the 2010 Census of the United States. I am linking here to directly to the platform's "Advanced Search" page, which I think is the most helpful place to start. –July 2020 ^

César Cristancho's Population Pyramid Code

(age measures, R code)

Population pyramids are graphs that show population by age and sex groupings, and are a key resource for demographic analysis. Assessment of an additional grouping is frequently needed as well, and this R code by César Cristancho, a professor with the Universidad Externado de Colombia, offers just that. There is an option for a barchart displayed within each of the pyramid's age-by-sex groupings (prg_ppyr_vdodge.r), as well as stacked bars for each grouping (prg_ppyr_stack.r). Prof. Cristancho's GitHub page also includes R code for population pyramids with dual y-axis labels to specify year of birth. Immensely useful for analysis of age structure. –July 2020 (updated April 2023) ^

Tom Wilson's Model Migration Schedule Fitting Example

(migration, Excel spreadsheet)

Human migration has distinctive age patterns, and parametric models for these patterns have been developed and described over the past few decades (a principal reference: Rogers and Castro, 1981). Tom Wilson of the University of Melbourne has extended and improved the Rogers-Castro migration model, offering both a student curve and an alternative post-retirement curve, and linked above is a related Excel workbook to apply the model. I've found the workbook is immensely useful for understanding and applying the new features, and model migration schedules in general. –July 2020 ^

Eddie's Hamilton-Perry Projection with Components and Stable Population Information Code*

(population projections, review tools, R code, Excel spreadsheet)

The Hamilton-Perry method, which uses ratios of change in cohort sizes between two points in time (cohort change ratios) to project population, is a useful population analysis resource, but the core method doesn't include information on the components of population change. This R code for population projections employs cohort change ratios with optional inputs for fertility (iTFR – Hauer and Schmertmann (2019) and Hauer, Baker, and Brown (2013)), mortality (Brass' relational logit model – information here and here), and net migration (information here, here, and here) as well as gross migration, and also returns estimates of stable population growth and age structure. It's useful for projecting population, and also for evaluating population estimates. To make the code work, you should be able to simply select-all, copy, and paste into an R command line (the example input data is linked through the Internet, and can be simply downloaded for review). Here is a Shiny for R application for the code. And more information, including formulas, a spreadsheet demonstration to replicate results, slides for a related talk, as well as R code with input files for several examples, including an errors review version, a single-year-of-age version, and a stochastic version, is all available in a related GitHub repository. –May 2020 (updated June 2022) ^

Ruiz-Santacruz and Garcés' migraR: Rogers and Castro Migration Models Schedule Optimization

(migration, R package)

From the README: "R package for migration analysis focused now on the Rogers and Castro multi exponential model and the estimation of the parameters using a bayesian simulation based on uniform a priori distributions for each parameter between 0 and 1. "The migraR package provides functions for estimate the parameters for Rogers and Castro Models: rc.7, rc.9, rc.11, rc.13 are created in the example using the Migramodel class." This neat R package is a prototype developed by J. Sebastian Ruiz-Santacruz and Jackson Garcés between June and February 2018. Here is a link to a related working paper by the package's coauthor, J. Sebastian Ruiz-Santacruz, a researcher with the Autonomous University of Barcelona's Centre for Demographic Studies (CED). –September 2019 ^

Hauer and Byar's IRS County-to-County Migration Data, 1990 to 2010, Replication Code

(migration, data and materials, R code)

The related paper's abstract explains:
"
Background: The county-to-county migration data of the Internal Revenue Service's (IRS) is an incredible resource for understanding migration in the United States. Produced annually since 1990 in conjunction with the US Census Bureau, the IRS migration data represents 95% to 98% of the tax-filing universe and their dependents, making the IRS migration data one of the largest sources of migration data. However, any analysis using the IRS migration data must process at least seven legacy formats of this public data across more than 2000 data files - a serious burden for migration scholars.
Objective: To produce a single, flat data file containing complete county-to-county IRS migration flow data and to make the computer code to process the migration data freely available.
Methods: This paper uses R to process more than 2,000 IRS migration files into a single, flat data file for use in migration research.
Contribution: To encourage and facilitate the use of this data, we provide a single, standardized, flat data file containing county-to-county one-year migration flow for the period 1990-2010 (containing 163,883 dyadic county pairs resulting in 3.2 million county-year observations totaling over 343 million migrants) and provide the full R script to download, process, and flatten the IRS migration data.
"
A very neat and especially valuable work, by Matt Hauer of Florida State University and James Byars of the University of Georgia. –September 2019 (updated February 2022) ^

Riffe et al's DemoTools: Tools for the Evaluation, Adjustment, and Standardization of Demographic Data

(population estimates, surveys, fertility, migration, mortality, age measures, R package)

Tim Riffe, a demographer at the University of the Basque Country, is leading development of this terrific collection of R functions. From the README, "DemoTools is an R package that contains simple functions often used in demographic analysis. It is in active development. "This project is commissioned by the UN Population Division, and financed by the Bill and Melinda Gates Foundation, as part of the Making Family Planning Count, project. Work is also done in collaboration with Sean Fennell, José Manuel Aburto, Ilya Kashnitsky, Marius Pascariu, Jorge Cimentada, Monica Alexander, and with minor contributions from several more (thank you!)." "The idea behind DemoTools is to provide a common set of functions that can be easily used by analysts and scientists working on demographic analysis and modelling." –September 2019 (updated September 2023) ^

Sam Clark's Get USA Mortality Data

(mortality, R code)

From the linked-repository's README, this R code by Sam Clark—demographer and professor of sociology at the Ohio State University—is to:
-download ZIP file with all data from usa.mortality.org
-read and parse the desired life table type into an R list
-extract a given column from all the sex-specific life tables and store them in a numeric matrix
A terrific resource to help access and work with the amazing United States Mortality Database. –September 2019 ^

The Canadian Human Mortality Database

(mortality, geographic, historical, data and materials, web interface)

Information from the front page of the site: "The Canadian Human Mortality Database (CHMD) was created to provide detailed Canadian mortality and population data to researchers, students, journalists, policy analysts, and others interested in the history of human longevity." "The CHMD is a 'satellite' of the Human Mortality Database (HMD), an international database which currently holds detailed data for 41 countries or regions. Consequently, the CHMD’s underlying methodology corresponds to the one used for the HMD." "The CHMD gathers all required data (deaths counts, births counts, population size, exposure-to-risk, death rates) to compute life tables for Canada, its provinces and its territories. One of the great advantages of the database is to include data that is validated and corrected, when required, and rendered comparable, if possible, for the period ranging from 1921 thru 2019." Like the below-linked United States Mortality Database, an amazing and immensely useful and important compilation of data - am so grateful for these projects. –September 2019 (updated February 2022) ^

The United States Mortality Database

(mortality, geographic, historical, data and materials, web interface)

From the front page of the site: "The United States Mortality Database (USMDB) brings you the first clearly documented historical set of complete state-level life tables, updated to the latest year of available data, designed to foster research on geographic variations in mortality across the United States and to monitor trends in health inequalities. This data set currently includes complete and abridged life tables by sex for each of the US 9 Census Divisions, 4 Census Regions, 50 States and the District of Columbia, for each year since 1959 with mortality values up to age 110. The data are available for free to all interested upon registration. "The USMDB is an outgrowth of the Human Mortality Database (HMD) project. It was developed to provide detailed mortality and population data to researchers, students, journalist, policy analysists, and others interested in state-level mortality trends and geographic variations in the length of life within the United States." An amazing and immensely useful and important compilation of data. –September 2019 ^

US Department of Housing and Urban Development's Zip Code Crosswalk Files

(geographic, data and materials, web interface, Excel spreadsheets)

From the home page: "One of the many challenges that social science researchers and practitioners face is the difficulty of relating United States Postal Service (USPS) ZIP codes to Census Bureau geographies. There are valuable data available only at the ZIP code level that, when combined with demographic data tabulated at various Census geography levels, could open up new avenues of exploration. "While some acceptable methods of combining ZIP codes and Census geography exist, they have limitations. To provide additional avenues for merging these data, PD&R has released the HUD-USPS Crosswalk Files. These unique files are derived from data in the quarterly USPS Vacancy Data. They originate directly from the USPS; are updated quarterly, making them highly responsive to changes in ZIP code configurations; and reflect the locations of both business and residential addresses. The latter feature is of particular interest to housing researchers because many of the phenomena that they study are based on housing unit or address. By using an allocation method based on residential addresses rather than by area or by population, analysts can take into account not only the spatial distribution of population, but also the spatial distribution of residences. This enables a slightly more nuanced approach to allocating data between disparate geographies." –September 2018 (updated May 2023) ^

Eddie's R Code for Fitting the Multi-Exponential Model Migration Schedule*

(migration, R code)

Model migration age schedules have been developed and described over the past few decades (a central resource: Rogers and Castro, 1981), and they are useful for understanding human migration, which has some recurrent patterns for childhood, labor force ages, retirement ages, and old ages. Tom Wilson of the University of Melbourne has extended and improved-upon the Rogers-Castro migration model, adding both a student curve and alternative post-retirement curve, and also provided terrific guidance and instruction on the overall model. This R code uses the guidance and instruction from Dr. Wilson's model migration schedule articles, and their related Excel workbooks, along with R's random number generator, and sampling (Monte Carlo) from recursively smaller uniform distributions of model parameters to locate a best fit for each of the components. The sampling-based fitting process is just a "DIY" solution (I haven't been able to find a reference for fitting-by-sampling done this way, or an evaluation of it - please let me know if you know one, or if you have insights that might be instructive or generally helpful), and it hasn't been carefully tested at all, but it's neat it appears so far to do the job pretty well. When I developed the code, I thought that using uncertain bounds as the parameters' starting inputs for fitting Rogers-Castro was novel, but soon after I learned of a neat and helpful R package developed months before this code that uses uncertain bounds, indeed uniform distributions, for these: migraR, by J. Sebastian Ruiz-Santacruz and Jackson Garcés Hernández, also listed on this page. I think relying on uniform distribution bounds rather than single values for starting-parameter selection can make the overall process of fitting the model easier. To make the code work, you should be able to simply select-all, copy, and paste into an R command line (the example input data is linked through the Internet, and can be simply downloaded for review). Copies of the code with some application of and comparison to R's nls() and lm() functions, and to more plain Monte Carlo, are available here. Here is a Shiny for R interface with linked code to view the model fitted to American Community Survey Public Use Microdata Sample data (accessed via IPUMS) for US states and California counties. –August 2018 (updated August 2022) ^

Google Earth Pro

(geographic, stand-alone software)

Google Earth Pro is free (as of 2015), and in addition to all the features of Google Earth, it includes resources such as an automated address-locating (geocoding) tool, ability to import many types of spatial files, additional layers of data and information, and additional measurement tools. –May 2017 ^

Tom Wilson's Checklist for Reviewing Draft Population Projections

(population projections, review tools, texts and instruction, PDF file)

This checklist from Tom Wilson of the University of Melbourne provides very helpful and clear insights and guidance on what-to-review for anyone publishing population projections. The related abstract: "The literature on population projections says very little about one crucial step of the projections production process - checking draft projection outputs for errors and plausibility. Unfortunately, it is quite possible to obtain implausible projections even when using the latest projection models and carefully prepared input data. This paper presents a checklist to assist with checking draft population projection outputs for plausibility. Applying the checklist does not guarantee accurate projections, but it should help identify serious errors and embarrassingly implausible projections prior to publication." –May 2017 (updated June 2023) ^

Baker, Swanson, Tayman, and Tedrow's Cohort Change Ratio Files*

(population estimates, population projections, data and materials, Excel spreadsheets)

Cohort change ratios are a powerful tool for analysis and use of age structure, particularly to create population estimates and projections. This recent textbook by Jack Baker, David Swanson, Jeff Tayman, and Lucky Tedrow helps immensely to understand them in depth. The linked zip file holds a number of supporting files that go along with the book. –May 2017 (updated March 2022) ^

US Census Bureau's TIGER: Topologically Integrated Geographic Encoding and Referencing

(geographic, data and materials, web interface, mapping files)

TIGER geographic boundary and feature data are used for countless research projects and programs in the United States. Products include:
-TIGER/Line Shapefiles
-TIGER/Line Geodatabases
-TIGER/Line with Selected Demographic and Economic Data
-Cartographic Boundary Shapefiles
-KML - Cartographic Boundary Files
-TIGERweb
The Census Bureau also provides a terrific document/interface on the history of TIGER. Boundary and feature data for the entire United States - a key (and terrific) resource for US population research. –April 2017 (updated July 2020) ^

United Nations' Demographic Manuals

(population estimates, population projections, fertility, migration, mortality, texts and instruction, web interface, PDF files, Excel spreadsheets)

From the main page: "This section presents a series of 23 manuals and guides of demographic methods and techniques issued by the United Nations over a long period of time. These manuals and guides fall into six clusters. For each cluster the opening page contains a short introduction and a hyperlinked list of manuals. The pages for each manual include a short introduction, chapter-level table of contents hyperlinked to respective files in pdf format and links to thematically related manuals. "The manuals on this site are concerned with formal descriptive, analytical and projection methods with an emphasis on techniques adapted to or specially designed for the countries with insufficient, unreliable or untimely data. The behavioural aspects or their socio-economic determinants are outside the scope of this series." I am currently linking to an archived page (many thanks Internet Archive). The publications listed on the archived Manuals page are still available through the United Nations Population Division's Publications page (all but one under the Manuals filter), but I don't know of a currently-maintained site just for them. The United Nations Population Division also hosts a page with spreadsheet templates for projection of the total fertility rate, life expectancy at birth, and net number of migrants - each useful and informative. –April 2017 (updated May 2023) ^

US Census Bureau's Geocoder

(geographic, review tools, web interface)

From the related documentation page: "The Census Geocoder is an address look-up tool that allows the user to submit a single address, batch of up to 10,000 addresses, or location coordinates for geocoding. Geocoding results are derived from address ranges within the Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database. Address ranges within TIGER include all possible structure numbers even though actual structures may not exist. Geocoding results are returned if the address range match score meets or exceeds the threshold for single address geocoding. Coordinate results are interpolated, or approximated, based on the physical location that the address geocoded on the TIGER address ranges. The geocoder is available as a web interface and as an API (Representational State Transfer - REST - web-based service)." "The overall goal of the Census Geocoder is to provide the public with the capability to geocode an address efficiently and accurately." –March 2017 (updated September 2023) ^

Eddie's Next Year Population Possibilities Reviewer*

(population projections, web interface, R code)

From the related Google+ post: "Demographers are sometimes asked what-to-expect for population next year, and total population (rather than births or deaths, or general migration or aging trends, etc) is often the most difficult part to provide helpful information on..." This Shiny interface allows one to view a distribution of possibilities for next year's total population, based on best guesses for uniform distributions (high and low bounds) of each component of change, with no correlation between components. The Google+ post also gives the R code for it (which could be modified to use any sort of distribition(s)) and information on how to very easily make high/low bounds for the components uncertain. –March 2017 ^

The United Nations' Software Package for Mortality Measurement (MORTPAK)

(mortality, stand-alone software)

From the above-linked page: "MORTPAK is a software package for demographic measurement in developing countries, with special emphasis on mortality measurement." The original link is currently inaccessible, so I am connecting above to an archived copy of it (thanks Internet Archive). Here is a video by YouTube user Chayanika deka that demonstrates installation (Windows) of MORTPAK and how to generate a life table with it. –March 2017 (updated May 2023) ^

US Census Bureau's Subnational Projections Toolkit (SPToolkit)

(population projections, Excel spreadsheets)

From the main page: "The Subnational Projections Toolkit is a set of Excel workbooks, associated step-by-step guides and methods documentation, and a User's Guide to using the tools included in the Toolkit. These products support preparation of subnational population projections following cohort-component and/or non-cohort-component (mathematical extrapolation) methods." This is one of several useful resources listed on the US Census Bureau's International Programs Center Software page. –March 2017 (updated January 2023) ^

US Census Bureau's DAPPS: Demographic Analysis and Population Projection System

(population projections, stand-alone software)

From the main page: "DAPPS is a program designed to help users analyze and produce population projections with ease. It accomplishes this through a user-friendly spreadsheet interface for data entry and the projection power of RUP." "In order to create a population projection, DAPPS requires at least three inputs: a base population, by age and sex (usually based on a census or estimate); a mortality structure, by age and sex (usually a life table or deaths, by age and sex); and a fertility pattern, by age of mother (births or age-specific fertility rates). A fourth input, a pattern of net migration (by age and sex of migrant), is optional but recommended." "The data for these components can originate from either a RUP input file or a spreadsheet-based program, such as Microsoft Excel or United Nations' MORTPAK." This is one of several resources listed on the US Census Bureau's International Programs Center Software page. –March 2017 (updated January 2023) ^

Alaska Department of Labor's PFD-Based Migration Data

(migration, data and materials, web interface, Excel spreadsheets)

Alaska has a statewide Permanent Fund Dividend (PFD) program, to which any resident (meeting certain eligibility requirements) may apply each year to receive a dividend payment. The number of applicants statewide is typically around 90 percent of the estimated usual resident population. (PFD annual reports.) Alaska Department of Labor and Workforce Developent staff geocode and use PFD application data for development of population estimates, and also to track migration. PFD-based migration data by age and sex are published for each borough and census area (these are county-equivalents) in Alaska for 1990-95 through 2015-20, and for Alaska statewide 1985-90 through 2015-20. Annual data without demographic characteristics are published for each borough, census area, city, and census designated place in the state. Here are the methods for development of the data. And here is a Shiny for R viewer for an extract of the migration by age and sex data. Huge thanks to demographers Eric Sandberg and David Howell for development and production of this amazing migration data resource. –March 2017 (updated March 2023) ^

The Huntington's Early California Population Project

(historical, data and materials, web interface)

From the home page: "The Early California Population Project (ECPP) provides public access to all the information contained in California's historic mission registers, records that are of unique and vital importance to the study of California, the American Southwest, and colonial America." "Within the baptism, marriage, and burial records of each of the California missions sits an extraordinary wealth of unique information on the Indians, soldiers, and settlers of Alta California from 1769 - 1850." Amazing! –March 2017 ^

US Census Bureau's American Community Survey Statistical Testing Tool

(surveys, Excel spreadsheet)

From the Census Bureau website: "The Statistical Testing Tool is a spreadsheet that tests whether American Community Survey (ACS) estimates are statistically different from one another. Simply copy or download ACS estimates and their margins of error (MOE) into the tool to get instant results of statistical tests." –July 2016 (updated April 2017) ^

Chuck Coleman's Constraining Arrays Website and SAS Code

(population estimates, surveys, migration, SAS code)

From the related paper: "Many applications require constraining arrays of numbers to controls in one or two dimensions. Example applications include survey estimates, disclosure avoidance, input-output tables, and population and other estimates and projections. If the results are allowed to take on any nonnegative values, raking (a.k.a. scaling) solves the problem in one dimension and two-way iterative raking solves it in two dimensions. Each of these raking macros has an option for the user to output a dataset containing the rakes. The problem is more complicated in one dimension if the data can be of any sign, the so-called "plus-minus" problem, as simple raking may produce unacceptable results. This problem is addressed by generalized raking, which preserves the structure of the data at the cost of a nonunique solution. Often, results are required to be rounded so as to preserve the original totals. The Cox-Ernst algorithm accomplishes an optimal controlled rounding in two dimensions. In one dimension, the Greatest Mantissa algorithm is a simplified version of the Cox-Ernst algorithm." Chuck also notes that the gras.sas macro implements the Generalized RAS algorithm, which rakes matrices of mixed sign. A great resource. –July 2016 (updated March 2022) ^

Cornell PAD's County Characteristics Explorer

(population estimates, review tools, web interface)

Each year the US Census Bureau creates population estimates by age, sex, race and Hispanic origin for all counties in the United States. To review these very detailed "characteristic" data, Jan Vink of the the Cornell Program on Applied Demographics (with inspiration from David Howell's R code) put together a useful web interface (as of this writing, the latest data are the 2010 to 2015 population estimates). A very useful resource. –July 2016 (updated September 2023) ^

UK Office for National Statistics' Life Table Template

(mortality, Excel spreadsheet)

Above is a link to a UK Office for National Statistics abridged life table spreadsheet template (via this statistical bulletin site), including use of the revised Chiang method (Chiang II) for calculation of variance. Very neat and useful. A note, for variance estimation, Lo, Vatnik, Benedetti, and Bourbeau (2016) provided an important improvement to Chiang's method for variance calculation - to include the last age interval - that isn't included in this resource. –January 2016 (updated January 2022) ^

David Howell's County Characteristics Graphing Code*

(population estimates, review tools, R code)

Each year the US Census Bureau creates population estimates by age, sex, race and Hispanic origin for all counties in the United States. To review these very detailed "characteristic" data, David Howell a researcher with the State of Alaska put together R code that creates many useful graphs. Linked above is a .zip file with the easy-to-update code and supporting files for the latest release (2010 to 2016 as of this writing), as well as example output. To make it work, users should unzip the folder and follow the instructions in the "CountyCharacteristicsGraphing_v2016.txt" file. A very useful resource. –December 2014 (updated December 2018) ^

Hauer, Baker, and Brown's Implied Total Fertility Rate Calculator*

(fertility, age measures, Excel spreadsheet)

From the related abstract: "Indirect estimation methodologies of the total fertility rate (TFR) have a long history within demography and have provided important techniques applied demographers can use when data is sparse or lacking. However new methodologies for approximating the total fertility rate have not been proposed in nearly 30 years. This study presents a novel method for indirectly approximating the total fertility rate using an algebraic rearrangement of the general fertility rate (GFR) through the known relationship between GFR and TFR. It then compares the proposed method to the well-known Bogue-Palmore method. These methods are compared in 196 countries and include overall errors as well as characteristics of the countries that contribute to fertility behavior. Additionally, these methods were compared geographically to find any geographical patterns. We find this novel method is not only simpler than the Bogue-Palmore method, requiring fewer data inputs, but also has reduced algebraic and absolute errors when compared with the Bogue-Palmore method and specifically outperforms the Bogue-Palmore method in developing countries. We find that our novel method may be useful estimation procedure for demographers." Matt Hauer, Jack Baker, and Warren Brown put the project together and it's very useful. –March 2014 (updated February 2022) ^

Gergely Daróczi's Migration Indices

(migration, R package)

From the reference manual: "This package provides various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration." R functions for a very useful set of indices, by Gergely Daróczi. –March 2014 ^

Eddie's Employment-Based Population and Housing Projection Code*

(population projections, R code)

Planners are often required to prepare or acquire long-term population, employment and housing projections for their areas, and population projections with integrated employment or housing can be a heavy lift. I had the thought that it shouldn't be too tricky though to just modify R code for Basic Population Projection and make net-migration employment-based with some broadly used parameters (that economists have strong familiarity with or ability to access quickly), and then simply calculate housing ex-post, and in fact it wasn't too tricky. Total migration counts are scaled based on working-age migration counts. To see the code work, and a selection of output-graphics, you should be able to simply select-all of the code, copy, and paste into an R command line (input data (csv format) is linked through the Internet, and can be simply downloaded for review). I don't know of other simple and readily available code to do this (please let me know if you have some). The economic and housing inputs are broad, so anyone (regional economists, local experts, etc.) could provide them. The example input data were not carefully considered and are not meant to be useful for any certain area. Here are the employment and housing variables: TOTAL CIVILIAN EMPLOYMENT, LABOR FORCE PARTIPATION RATE, UNEMPLOYMENT RATE, COMMUTER RATE, WOMENS SHARE OF TOTAL CIVILIAN EMPLOYMENT, PERSONS PER HOUSEHOLD, and VACANCY RATE. Causal and more-complex models such as this aren't known to generally improve population projection accuracy (informative article by Smith (1997)), but having more variables to work with and assess can be useful. Here is an implementation of it with Shiny. If you have any questions, don't hesitate to shoot me an email at edyhsgr@protonmail.com. –March 2014 (updated May 2022) ^

Minnesota Population Center's National Historical Geographic Information System (NHGIS)

(surveys, fertility, migration, mortality, geographic, historical, data and materials, web interface, mapping files)

From the front page: "The National Historical Geographic Information System (NHGIS) provides easy access to summary tables and time series of population, housing, agriculture, and economic data, along with GIS-compatible mapping files, for years from 1790 through the present and for all levels of U.S. census geography, including states, counties, tracts, and blocks." Awesome. –April 2013 (updated March 2024) ^

Texas A&M Transportation Institute's Census SF1-ACS Master Index Files

(surveys, data and materials, web interface)

From the front page: "This Webpage and accompanying materials document the process to create master index files capable of linking various U.S. Census Bureau (Census) data products. Systematically linking various datasets together will allow Census data users to assemble information for statistical and geographic analysis." –March 2013 ^

The Association of Religion Data Archives

(surveys, geographic, historical, data and materials, web interface)

From the 'About the ARDA page': "The Association of Religion Data Archives (ARDA) strives to democratize access to the best data on religion. Founded as the American Religion Data Archive in 1997 and going online in 1998, the initial archive was targeted at researchers interested in American religion. The targeted audience and the data collection have both greatly expanded since 1998, now including American and international collections submitted by the foremost religion scholars and research centers in the world." –March 2013 (updated September 2023) ^

Carl Schmertmann's Calibrated Spline Fertility Estimation

(fertility, R code)

From the related paper: "I develop and explain a new method for interpolating detailed fertility schedules from age-group data. The method allows estimation of fertility rates over any fine grid of ages, from either standard or non-standard age groups. The new method, called the calibrated spline (CS) estimator, expands an abridged fertility schedule by finding the smooth curve that minimizes a squared error penalty." This is a more nuanced version of the method and tool presented here. –March 2013 (updated March 2022) ^

IRS Tax Statistics' Migration Data

(migration, data and materials, web interface, Excel spreadsheets)

From the site: "The data present migration patterns by state or by county for the entire United States and are available for inflows-the number of new residents who moved to a county or state and where they migrated from, and outflows-the number of residents leaving a county or state and where they went." –March 2013 ^

Klára Hulíková's Life Table Smoothing Macro*

(mortality, SAS code)

Life table estimation is a fundamental part of demographic research, so this SAS macro by Klára Hulíková to provide life table smoothing options (including Kannisto, Gompertz-Makeham, modified Gompertz-Makeham, Thatcher, and Coale-Kisker functions) should be of great use. More information is available in Chapter 5 of the related doctoral thesis, and a 64-bit version is available as well. Terrific! –July 2012 (updated December 2018) ^

US Census Bureau's Historical Census Publications

(surveys, historical, data and materials, web interface, PDF files)

From a past US Census History page (archived, thanks Internet Archive) describing the collection of scanned publications: "Census of Population and Housing data present here ranges from our most recent census to the historical decennial census conducted throughout the decades. Some of the data were scanned as an effort to make historical census information available to the public." –June 2012 (updated March 2022) ^

US Census Bureau's Census Questionnaire Archive

(surveys, historical, data and materials, web interface, PDF files)

Decennial census questionnaires are essential tools for applied demographers, and the Census Bureau has the US ones all in one place. –June 2012 (updated April 2018) ^

US Census Bureau's American Community Survey Questionnaire Archive

(surveys, fertility, migration, historical, data and materials, web interface, PDF files)

From a Census Bureau Website: "The American Community Survey (ACS) is an ongoing statistical survey that samples a small percentage of the population every year - giving communities the current information they need to plan investments and services." This site provides the questionnaires for the survey, an essential tool for understanding the data. –June 2012 (updated March 2017) ^

Michail Agorastakis and Zacharoula Michou's Direct and Indirect Standardization*

(mortality, Excel spreadsheet)

Age standardization of death rates is an important procedure that allows better comparisons of death rates for different populations. This Excel spreadsheet by Michail Agorastakis and Zacharoula Michou of the University of Thessaly provides examples of both direct and indirect age standardization which can be used for review and instruction, or as templates for other data. A very useful tool. –March 2012 (updated December 2018) ^

Michail Agorastakis and Zacharoula Michou's Age Preference Measures*

(surveys, age measures, Excel spreadsheet)

"Age heaping" is systematic misreporting of age in a survey, with preference for certain ages, often due to rounding. This spreadsheet by Michail Agorastakis and Zacharoula Michou of the University of Thessaly can be used to calculate Myers' and Whipple's indices (as well as an extension of Whipple's index) which each measure age heaping. A great resource. –March 2012 (updated December 2018) ^

Michail Agorastakis and Zacharoula Michou's Population Pyramids and Age Measures*

(age measures, Excel spreadsheet)

Population pyramids are graphs that show population by age and sex, and neatly summarize many aspects of a population. This Excel workbook by Michail Agorastakis and Zacharoula Michou of the University of Thessaly can be used to make a nice population pyramid simply and quickly. And provided along with the pyramid: mean age, median age, sex ratio, dependency ratio, an aging index, and proportions of total population for selected age groups. Terrific! –March 2012 (updated December 2018) ^

Michail Agorastakis and Zacharoula Michou's Recode In Excel*

(surveys, Excel spreadsheet)

From the linked workbook: "Even though recoding a variable into a new one, is a simple task when using statistical analysis software in Excel is not a straight foreword procedure. We present here a simplified example of creating population size bands for Greek Municipalities (1034, in 2001)." –March 2012 (updated December 2018) ^

Stat Canada's Modgen (Model generator) (microsimulation)

(population projections, stand-alone software)

From the Modgen Website: "Modgen (Model generator) is a generic microsimulation programming language supporting the creation, maintenance and documentation of dynamic microsimulation models. Several types of models can be accommodated, be they continuous or discrete time, with interacting or non-interacting populations." –February 2012 ^

US Census Bureau's Calculating Migration Expectancy Using ACS Data

(migration, Excel spreadsheet)

From the Census Bureau Website: "The example below [on the linked Website] of migration expectancy is calculated using the population and number of nonmovers in the previous year by age estimated from the 2007 American Community Survey (ACS). These numbers are used to calculate an average mobility rate for each age group for the period selected (step 1). The number of movers appears in column L and the mobility rate (Rx) is automatically calculated in column B (step 2). A standard life table provides the expected population at the beginning of the age interval per 100,000 births (lx). The stationary population (Lx) is the total number of people still living in the age interval per 100,000 births as of the date of the NCHS - Life Tables, in this case, 2004 (step 3). The average mobility rate (Rx) is multiplied by the stationary population (Lx) to obtain the expected number of moves for each age interval (column E). These expected movers are cumulated across each age group from oldest to youngest (TMx), and then divided by the population still living per 100,000 born (lx), to obtain the average expected number of moves remaining for people in that age group (step 5)." –February 2012 (updated March 2017) ^

IUSSP's Tools for Demographic Estimation

(fertility, migration, mortality, texts and instruction, web interface, PDF file, Excel spreadsheets)

From the front page: "This site represents the major output arising from a joint IUSSP and UNFPA project to produce a single volume containing updated tools for demographic estimation from limited, deficient and defective data. The material here follows in a direct line of descent from Manual X and subsequent works (for example, the 2002 UN Manual of Adult Mortality Estimation). The principal aspect of this Website is a series of (mostly) static Webpages describing and documenting the tools and methods of demographic estimation from limited, deficient and defective data. The material is organised thematically first, and then by the kinds of data that may be available. Where appropriate, downloadable spreadsheets are provided that allow users to apply the methods to their own data. Forums are available to discuss and debate methods and results, and FAQs describe how to use the site in more detail. Links to both, as well as forms to contact us, are at the top right hand side of each page." –February 2012 ^

University of Augsburg's BAZI: Calculation of Allocations by Apportionment Methods in the Internet

(population estimates, geographic, stand-alone software)

From the BAZI Manual: "BAZI is a freely available Java-program implementing various apportionment methods for proportional representation systems. It offers divisor methods as well as quota methods. The extensive database permits to investigate the merits of the different apportionment methods on the basis of empirical data. A particular feature of BAZI is that it offers three options for multiple electoral districts. The user may choose between (1) separate evaluations for each district, (2) biproportional apportionments using divisor methods, and (3) a variant of the latter that is specifically tailored to the needs of the new Zurich electoral law of 2003. For these matrix apportionment methods thirteen algorithms and hybrid combinations of them are available. The BAZI program also offers to include minimum and maximum restrictions for vector and matrix apportionments. We present several empirical examples where these restrictions are essential." –February 2012 (updated May 2020) ^

Kim Dunstan's Percentile (Median) Calculation Spreadsheet*

(age measures, Excel spreadsheet)

Percentile calculation is an often-necessary task for a research office, and depending on the type of data (grouped, complete, big, small, etc.), it's not always straight-forward. Luckily, Kim Dunstan (Senior Demographer at Statistics New Zealand) has developed and shared the above-linked Excel spreadsheet macro to make it very easy for everyone. It also provides mean and standard deviation. A terrific resource. –October 2011 (updated December 2018) ^

Doig and Hoyers' Median Calculation Spreadsheet*

(age measures, Excel spreadsheet)

Given continuous data that is aggregated into groups (such as age groups or income groups), the median is a little tricky to calculate. Linked above is an Excel spreadsheet that was developed by Steve Doig, Bob Hoyer and Meghan Hoyer to do it simply. There are a couple equivalent calculators on this page, but because different people like different formats, I'm very glad it was sent my way to share. –October 2011 (updated December 2018) ^

Tom Bryan's MAPE-R Spreadsheet*

(review tools, Excel spreadsheet)

Measures of estimate and forecast accuracy are essential tools for applied demography, because they provide context for what level of accuracy should be expected, and benchmarks for improvement in accuracy. The Mean Absolute Percent Error (MAPE) is one of the most often used measures of cross-sectional demographic estimate and forecast accuracy, but it can have values that are influenced too heavily by outliers. In response to this shortcoming, several people (including David Swanson, Jeff Tayman, Charles Barr, Chuck Coleman and Tom Bryan) have worked to develop and review a measure called MAPE-R, which preserves information from outliers, but normalizes that information through a Box-Cox transformation. Above is a link to an Excel spreadsheet macro by Tom Bryan, which can be used to calculate MAPE-R for a given collection of Absolute Percent Errors (APE's). Here is a link to an early (1999) paper describing MAPE-R, and here is a link to a recent (2011) paper describing it. –September 2011 (updated September 2023) ^

Webb Sprague's Iterative Proportional Fitting Macro*

(population estimates, surveys, migration, SAS code)

Iterative proportional fitting (IPF) (also called raking, sample balancing, rim weighting, iterative proportional scaling, etc.) is a simple and handy technique that is used for adjusting a table of data cells such that they add up to selected totals for both the columns and rows (in the two-dimensional case) of the table. Among other things, it is commonly used in survey research and analysis. Linked above is a great SAS macro for two-dimensional iterative proportional fitting, developed by Webb Sprague, a researcher with the State of Washington. Please note this code was developed recently and has only been tested with some sample data. A related Website with links to informal explanations of IPF, some more code and some published papers, is available here. –September 2011 (updated December 2018) ^

Carl Schmertmann's Quadratic Spline Fertility Schedules

(fertility, R code, Excel spreadsheet)

The abstract for Prof. Schmertmann's (2003) Demographic Research paper describing this neat work: "I propose and examine a new family of models for age-specific fertility schedules, in which three index ages determine the schedule's shape. The new system is based on constrained quadratic splines. It has easily interpretable parameters, is flexible enough to fit a variety of "noiseless" schedules well, and is inflexible enough to avoid implausible estimates from noisy data. Across a set of over two hundred contemporary ASFR schedules, the new model fits a majority better, and in some cases much better, than the Coale-Trussell model. When fit to a recent Swedish time series, model parameters exhibit simple, regular changes over time, suggesting utility in forecasting applications. In simulated small-sample data the new model produces plausible ASFR estimates, with errors similar to Coale-Trussell." Here is a link to a version that uses R's nls() function, and a link to the code's GitHub repository. This neat Shiny application by Prof. Schmertmann provides an informative demonstration of the model. And here is a link to Demographic Research's home for the paper, which also hosts a helpful Microsoft Excel implementation of the model. –August 2011 (updated April 2023) ^

Potter et al's Modeling and Mapping of Brazil's Fertility Transition

(fertility, geographic, WinBUGS, R code, mapping files)

This work by Joseph Potter, Carl Schmertmann, Renato Assunção and Suzana Cavenaghi to analyze the "timing, pace and scale" of Brazil's fertility transition includes R and WinBUGS code with data for mapping and Bayesian modeling. Links to terrific mapping output are included. –August 2011 (updated April 2023) ^

Schmertmann et al's Knox Meets Cox

(geographic, R code, mapping files)

This work by Carl Schmertmann, Renato Assunção and Joseph Potter provides R code for "adapting space-time epidemiological statistics to demographic studies." Some cool mapping output is included. Here is a link to a paper that was published on the work in the renowned journal Demography. –August 2011 (updated April 2023) ^

The Human Fertility Database

(fertility, geographic, historical, data and materials, web interface)

From the front page of the site: "The Human Fertility Database (HFD) is a joint project of the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany and the Vienna Institute of Demography (VID) in Vienna, Austria, based at MPIDR. We seek to provide free and user-friendly access to detailed, well-documented and high-quality data on period and cohort fertility and thus to facilitate research on changes and inter-country differences in fertility in the past and in the modern era." Similar to the Human Mortality Database. Beautiful! –August 2011 ^

US Census Bureau's Datasets

(population estimates, surveys, data and materials, web interface)

A Web address with directory trees for complete US Census Bureau datasets (decennials back to 1980, American Community Surveys, economic censuses, etc.). People will sometimes refer to it simply as "the ftp site." –August 2011 (updated March 2022) ^

US Census Bureau's RUP: Rural and Urban Projection Software

(population projections, stand-alone software)

From the main page: "The Census Bureau's Rural and Urban Projection Program (RUP) performs cohort component projections for an unlimited number of populations or areas. The number of subpopulations for which projections may be calculated has been expanded with the addition of a second program, RUPAGG. RUPAGG aggregates RUP output files to produce consistent sets of projections at multiple geographic levels (e.g., national and subnational projections). A third program, RUPCombine, allows for the isolation and exact allocation of demographic shocks (e.g., deaths from famine or civil conflict) to one half of a projection year. An updated user interface for RUP, RUPAGG, and RUPCombine–called RUPEX–has been written in Microsoft Excel and includes expanded program functionality using Visual Basic macros, Excel charts and graphs, and a series of Excel-based input-output utility programs. The RUPEX dashboard can be used to review, edit, run, debug, and extract a full list of user-defined output tables and charts in Excel." This is one of several resources listed on the US Census Bureau's International Programs Center Software page. –August 2011 (updated January 2023) ^

US Census Bureau's CSPro: Census and Survey Processing System

(surveys, stand-alone software)

From the main page: "The Census and Survey Processing System (CSPro) is a public domain software package used by hundreds of organizations and tens of thousands of individuals for entering, editing, tabulating, and disseminating census and survey data. CSPro is designed to be as user-friendly as possible, yet powerful enough to handle the most complex applications. It can be used by a wide range of people, from non-technical staff assistants to senior demographers and programmers. CSPro supports data collection on android devices (phones and tablets). The CSEntry Android App works in collaboration with the desktop version of of CSPro. CSPro supports smart data transfer from Android or Windows devices to a server running CSWeb." One of several useful resources listed on the US Census Bureau's International Programs Center Software page. –August 2011 (updated January 2023) ^

CDC's WONDER Databases

(population estimates, fertility, mortality, geographic, data and materials, web interface)

From the CDC WONDER FAQ page: "What is CDC WONDER? Wide-ranging OnLine Data for Epidemiologic Research (WONDER) - is an easy-to-use internet system that makes the information resources of the Centers for Disease Control and Prevention (CDC) available to public health professionals and the public at large. It provides access to a wide array of public health information. CDC WONDER furthers CDC's mission of health promotion and disease prevention by speeding and simplifying access to public health information for state and local health departments, the Public Health Service, and the academic public health community. CDC WONDER is valuable in public health research, decision making, priority setting, program evaluation, and resource allocation." The site includes detailed and easy-to-extract population, mortality, morbidity and fertility data. CDC vital statistics data are also available through the National Center for Health Statistics' Vital Statistics Online Data Portal. –July 2011 (updated March 2022) ^

Carl Boe's HMD2R: Human Mortality Database Data Grabber*

(mortality, geographic, historical, data and materials, R code)

The Human Mortality Database (HMD) is extensively useful, neatly organized and well maintained, and now there is an R function developed by Carl Boe (research demographer with the HMD and CEDA at the UC Berkeley Department of Demography) that allows users to easily pull its data directly from the Web into R. This function was updated in June 2022 to manage changes to the HMD website and access. –June 2011 (updated August 2022) ^

Tim Chapin's Cohort Component Model Spreadsheet*

(population projections, Excel spreadsheet)

The cohort component method is a standard and essential procedure for demographic research, and Tim Chapin of Florida State University has developed this neat and easy-to-use Excel workbook to carry it out. Many people are looking for a good cohort component projection model spreadsheet, and I think this is an outstanding one - very glad that Prof. Chapin put this resource together for his students, and that he's provided it to share here. –May 2011 (updated March 2022) ^

Avenir Health's Spectrum: Policy Models (including "DemProj")

(population projections, stand-alone software)

Spectrum is a suite of great and easy-to-use models for policy research that includes DemProj, which carries out cohort-component population projections. From the Spectrum Website: "DemProj projects the population for an entire country or region by age and sex, based on assumptions about fertility, mortality, and migration. A full set of demographic indicators can be displayed for up to 50 years into the future. Urban and rural projections can also be prepared. A companion model, EasyProj, supplies the data needed to make a population projection from the estimates produced by the Population Division of the United Nations." While data is provided for many nations, users can also put in their own detailed data. –April 2011 (updated September 2018) ^

Germán Rodríguez' Demographic Methods Course Materials

(population projections, fertility, mortality, age measures, texts and instruction, Stata code, R code)

Described on the home page of Germán Rodríguez, Senior Research Demographer, Emeritus, Princeton University: "...a core course on demographic methods, covering rates and standardization, life tables, nuptiality, fertility rates and birth intervals, tempo effects, population projections, and stable populations." Dr. Rodríguez' website also includes teaching materials for Generalized Linear Models, Multilevel Models, and Survival Analysis, as well as a Stata Tutorial and an introduction to R. Amazing resources. –April 2011 (updated January 2023) ^

Oklahoma Department of Commerce American Community Survey Calculator*

(surveys, Excel spreadsheet)

From the Oklahoma Department of Commerce (2011): "The Census Bureau's American Community Survey (ACS) is a great source for detailed population and household characteristics, giving estimates and margins of error for many aspects of the American population. However, sometimes users must blend ACS results in order to arrive at the exact measure needed - hard to do correctly if users are not familiar with statistics. This Excel based file contains several 'calculators' that let users enter official ACS results from the Census Bureau and come away with approximations of the true measure needed." Calculators like this terrific one developed by Steve Barker are essential tools for people who are regularly combining American Community Survey (ACS) estimates. The calculator was previously hosted by the Oklahoma Department of Commerce but I'm unable to find it there anymore (January 2016), so I'm linking to a copy. –March 2011 (updated December 2018) ^

Adapted MAPE-R Calculation Code*

(review tools, SAS code)

Measures of estimate and forecast accuracy are essential tools for applied demography, because they provide context for what level of accuracy should be expected, and benchmarks for improvement in accuracy. The Mean Absolute Percent Error (MAPE) is one of the most often used measures of cross-sectional demographic estimate and forecast accuracy, but it can have values that are influenced too heavily by outliers. In response to this shortcoming, several people (including David Swanson, Jeff Tayman, Charles Barr, Chuck Coleman and Tom Bryan) have worked to develop and review a measure called MAPE-R, which preserves information from outliers, but normalizes that information through a Box-Cox transformation. Above is a link to a SAS macro adapted from this (thanks Internet Archive) paper by Chuck Coleman, which can be used to calculate MAPE-R for a given collection of Absolute Percent Errors (APE's). Here is a link to an early (1999) paper describing MAPE-R, and here is a link to a recent (2011) paper describing it. –March 2011 (updated March 2022) ^

NIDI's LIPRO: Multistate Projection Model

(population projections, stand-alone software)

From an archived Netherlands Interdisciplinary Demographic Institute page (thanks Internet Archive): "Changes in household structure may have profound consequences for a wide range of areas in demography and social policy. Household projection models developed in demography over the past few decades are primarily of the headship rate type, in which the dynamic processes of household formation and dissolution which underlie changes in household structure, essentially are treated as a black box. Back in 1988, NIDI started a pioneering study in order to develop a dynamic household projection model which explicitly focusses on the flows underlying household changes. The model, called LIPRO ('LIfestyle PROjections'), is based on the methodology of multistate demography, but includes several extensions to solve the particular problems of household modelling. Although originally developed for household projections, the LIPRO computer program can in fact be used for a wide range of calculations in multistate demography. And indeed, LIPRO has been extensively used for various applications, in the Netherlands as well as in many other countries." I have not used the above-linked LIPRO software myself, but am confident that it is very valuable due to the many projects that it has been used for (listed on the LIPRO site). I don't know of any other publicly shared/free software that offers direct steps for multistate population projection. –March 2011 (updated July 2020) ^

Tom Wilson's Median Age Calculation Spreadsheet*

(age measures, Excel spreadsheet)

Given population data that is aggregated by age groups, the median age is a little tricky to calculate. Linked above is an Excel spreadsheet that was developed to show students how to do it in simple steps. The spreadsheet was made by Tom Wilson, a demographer with the University of Melbourne who has contributed a great deal to advanced work in applied demography. –March 2011 (updated March 2022) ^

XlXtrFun: Extra Functions for Microsoft Excel

(fertility, migration, mortality, Excel add-on)

Eddie's PRB Population Pyramid Spreadsheets*

(age measures, Excel spreadsheets)

Population pyramids display population by age and sex, and thus are a favorite tool for demographers. Several years back I took a class in which we used instructions from the Population Reference Bureau (PRB) to create population pyramids in Excel. I have since used the template I made many times, and linked above is a .zip file with versions of it (one for Excel 2003, and one for Excel 2007). No citation is necessary for use of these pyramid spreadsheets. (Note there is also the R function developed by Carl Mason for population pyramids in the "Stochastic Population Forecast Code" listed on this page.) –February 2011 (updated December 2018) ^

Eddie's Basic Population Projection Code*

(population projections, R code)

Population projection with the "cohort-component" method is a key part of demographic research. Linked above is R code that I developed for a basic cohort-component projection/forecast (applied to Alaska). To see the code work, and a selection of output-graphics, you should be able to simply select-all of the code, copy, and paste into an R command line (input data (csv format) is linked through the Internet, and can be simply downloaded for review). This code is based on the Stochastic Population Forecast Code, also listed on this page. A description of the work for the Stochastic Population Forecast Code is available here. If you would like to apply this basic projection code to another area (probably not hard at all), and have any questions, don't hesitate to shoot me an email at edyhsgr@protonmail.com. If you have access to JSTOR, here is a neat (and historically significant) article from 1895 on population projection by age (and check out the graphic on page 509). –February 2011 (updated June 2020) ^

CEDA's LCFIT: The Lee-Carter Fitter

(mortality, web interface)

The "Lee-Carter Model" is the most useful and well-known innovation in mortality modeling and forecasting in recent decades. It is noted for (among other things) finding an index of mortality data (called k(t)) that usually has a (roughly-)linear time-trend. A drawback to it is that steps to estimate and forecast its parameters are not simple. Fortunately, the great people at the Center on the Economics and Demography of Aging (CEDA), under the supervision of Ron Lee, have developed a web-based program (with Python) to estimate, forecast and graph Lee-Carter parameters and life expectancy from mortality data that users paste in (just need mortality rates by age). Webb Sprague, a demographer who is now working with the State of Washington, deserves a lot of credit for this work. If you would like to use LCFIT (not hard or time consuming to make it work, and they provide example data), and/or have any questions, don't hesitate to send the LCFIT developers an email at lcfit@demog.berkeley.edu. –February 2011 ^

Rob J Hyndman's Demography Package for R

(population projections, fertility, migration, mortality, R package)

From the linked page: "The R package demography provides functions for demographic analysis including: lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting." Authored by Rob J Hyndman, with contributions from Heather Booth, Leonie Tickle and John Maindonald. –February 2011 (updated March 2022) ^

Minnesota Population Center's IPUMS

(surveys, geographic, historical, data and materials, web interface)

I think this is the best single source for socio-economic survey microdata, which are necessary for countless projects. From the front page: "IPUMS-International: Harmonized data for 1960 forward, covering 326 million people in 159 censuses from around the world." "IPUMS-USA: Harmonized data on people in the U.S. census and American Community Survey, from 1850 to the present." "IPUMS-CPS: Harmonized data on people in the Current Population Survey, every March from 1962 to the present." Fantastic. –February 2011 ^

US Census Bureau's Building Permit Database

(surveys, data and materials, web interface)

Residential construction data can be very useful for local total population and housing unit estimates, which are useful for innumerable things. Above is a link to a database of monthly and annual building permits by US place (municipality) and county, from the US Census Bureau's "Survey of Residential Construction." –February 2011 (updated March 2017) ^

Cornell PAD's American Community Survey Calculator

(surveys, web interface)

Standard errors are an important component of estimates from the American Community Survey, but using, combining and interpreting them is often not intuitive. Fortunately, people at the Cornell Program on Applied Demographics have created an ACS calculator that can be used to test for significant difference between two values, and to combine two values. I'm sure that Jan Vink, a Research Support Specialist at PAD, deserves much credit for these great tools. –February 2011 ^

Cornell PAD's Mapper

(geographic, review tools, web interface)

Mapping of points (lat/long points, such as those that a county assessor might have for housing units) is useful for a lot of applied demography projects, and the Cornell Program on Applied Demographics Mapper makes it very easy. If you paste a table of lat/long coordinates into the Mapper window, you get back a map of those points, The Cornell PAD Mapper was useful to me when working on the 2010 Census "Group Quarters Count Review" program. As with the Cornell PAD's ACS Calculator (described just above this text block), I'm sure that Jan Vink deserves much credit for this great tool. –February 2011 ^

The Human Mortality Database

(mortality, geographic, historical, data and materials, web interface)

You can't beat this. From the front page of the site: "The Human Mortality Database (HMD) was created to provide detailed mortality and population data to researchers, students, journalists, policy analysts, and others interested in the history of human longevity. The project began as an outgrowth of earlier projects in the Department of Demography at the University of California, Berkeley, USA, and at the Max Planck Institute for Demographic Research in Rostock, Germany. It is the work of two teams of researchers in the USA and Germany, with the help of financial backers and scientific collaborators from around the world." Here is a Shiny for R interface with linked code from December 2021, to view the HMD's data for Italy, covering 1872 to 2018. The linked code includes application of Carl Boe's HMD2R, which is also listed on this page. –February 2011 (updated January 2022) ^

Stanford Formal Demography Workshop's Period Life Table Code*

(mortality, R code, Python code)

Life tables are the foundation of demography, and above is a link to a reproduced period life table R function/script (not developed by me) that was used at a 2006 Stanford Formal Demography Workshop (linking to page on Internet Archive), and that I've used as a template for constructing my own life tables in R. To make it work, you should be able to simply select-all of the code, copy, and paste into an R command line. The example input data in csv format is linked through the Internet, and can be simply downloaded for review. If you have any questions, feel free to send me an email at edyhsgr@protonmail.com. Here is a link to an archived original posting of the function - I reproduced it to allow an immediate link to input data so that potential users can quickly review results, but have made a couple of small modifications to the original over the years. And here is a Python notebook (using Jupyter) version of the same process. –February 2011 (updated October 2022) ^

Iterative Proportional Fitting R Code*

(population estimates, surveys, migration, R code)

Iterative proportional fitting (IPF) (also called raking, sample balancing, rim weighting, iterative proportional scaling, etc.) is a simple and handy technique that is used for adjusting a table of data cells such that they add up to selected totals for both the columns and rows (in the two-dimensional case) of the table. Among other things, it is commonly used in survey research and analysis. Linked above is a .zip file with R code, supporting files and instruction/documentation to perform two-, three- and four-dimensional IPF. Though I did pieces of the development, the main parts (the bulk of the 2D and 3D functions) were prepared by Nels Tomlinson, my predecessor at the Alaska Department of Labor and Workforce Development. A related website with links to informal explanations of IPF, some more code and some published papers, is available here. –February 2011 (updated December 2018) ^

Shyrock and Siegel's Methods and Materials of Demography

(population estimates, population projections, fertility, migration, mortality, surveys, age measures, texts and instruction, PDF file)

Instruction and description from the classic text by Shyrock and Siegel. I am linking above to Volume 2 of the two volume text, which covers methods and "population dynamics"—here is a link to Volume 1 of the text. Note there is also the great and updated later edition by Siegel and Swanson. –February 2011 (updated January 2023) ^

David Swanson's Population Estimation Bibliography and Glossary*

(population estimates, texts and instruction, PDF file)

A great annotated population estimation bibliography shared by Prof. David Swanson, a demographer who has been a leader and primary contributor for applied demography over recent decades. –February 2011 (updated March 2022) ^

Michael Hartmann's Demographic Methods for the Statistical Office

(population projections, fertility, migration, mortality, texts and instruction, PDF file)

Michael Hartmann led Statistics Sweden's demographic research, and provided a wealth of high quality products. I really enjoy this general demography text that he created, for at least a couple of reasons: (1) I think the instruction is both detailed and clear, and (2) it leads with some history, and context and perspective for the field, which I think are essential for users of demographic methods (so that we understand the breadth (and limits) and goals of our work). –February 2011 (updated September 2019) ^

Eddie's Brass Relational Gompertz Fertility Model Code*

(fertility, R code, Python code)

In addition to the Brass Relational Logit Model for mortality (which has some description here), William Brass made many lasting contributions to demography. One of these was the Brass Relational Gompertz Model for fertility. The Brass Relational Gompertz Model is used to adjust the earliness and width of a proportional (sum to 1) age specific fertility rates profile, based on the earliness and width of another age specific fertility rates profile. I believe this ability for adjustment might be useful for many things, including (1) fertility profile estimation with limited data, and (2) fertility profile forecasting. It should be noted that (1) there is "[no] behavioral interpretation" (from Demography: measuring and modeling population processes by Preston, Heuveline, Guillot) for the Brass Relational Gompertz model, and (2) a third parameter for a fertility profile would simply be the TFR, which would be multiplied by a proportional fertility profile to change the overall level of fertility. Here is a link to an early paper on the Brass Relational Gompertz model, by Heather Booth a professor at the the Australian National University. Linked above is an R function/script to apply the Brass Relational Gompertz model, and make selected output graphics. No care was taken in the selection of the standard profile, and the code has not been tested (please let me know any problems you find, of course). To make it work, you should be able to simply select-all of the code, copy, and paste into an R command line (example input data (csv format) is linked through the Internet, and can be simply downloaded for review). And here is a Python notebook (using Jupyter) version of the same process. If you would like to plug in data for another proportional fertility profile (simple), and have any questions, you can send an email to me at edyhsgr@protonmail.com. –February 2011 (updated October 2022) ^

Eddie's Brass Relational Logit Mortality Model Code*

(mortality, R code, Python code)

I think William Brass' most famous contribution to demography (he had many, and is surely among the most productive and revered demographers ever) is the Brass Relational Logit Model for mortality. The Brass Relational Logit Model is used to simply and reasonably adjust the level and shape of a life table's lx (survivorship) curve, based on the level and shape of another life table's lx curve. This ability for a reasonable adjustment by two parameters is useful for many things, including (1) life table estimation with limited data, where a life table with very large margins of error and an unreliable pattern across age can be modeled with a life table that has very small margins of error, and (2) life table forecasting, in which the the level and shape terms (alpha and beta, respectively) can be projected over time. Brass alpha in particular has been a subject of interest for mortality forecasting. Linked above is an R function/script to apply the Brass Relational Logit model, and make selected output graphics. To make it work, you should be able to simply select-all of the code, copy, and paste into an R command line (example input data (csv format) is linked through the Internet, and can be simply downloaded for review). And here is a Python notebook (using Jupyter) version of the same process. If you would like to plug in data for another life table (simple), or to consider Brass' mortality model for a particular project, and have any questions, don't hesitate to send me an email at edyhsgr@protonmail.com. –February 2011 (updated August 2022) ^

US Census Bureau's PAS: Population Analysis System

(population projections, fertility, migration, mortality, age measures, geographic, texts and instruction, Excel spreadsheets, PDF files)

From the PAS website: "PAS is a set of Microsoft Excel workbooks developed by the U.S. Census Bureau containing frequently used procedures and methods in basic demographic analysis. The purpose of the workbooks is to facilitate analysis of available data for the following topics: age structure, mortality, fertility, migration, distribution of population, urbanization, and population projections. The workbooks are distributed with two manuals describing the demographic methods they implement and the procedures they perform." Just a few of the (~40) tools are: an open ended age group distribution estimator, an indirect infant mortality estimator, and a relational-Gompertz fertility modeler. You can't beat that. One of several resources listed on the US Census Bureau's International Programs Center Software page. –January 2011 (updated February 2023) ^

Eddie's Stochastic Population Forecast Code*

(population projections, R code)

Stochastic population forecasts typically use a large collection of randomized, realistic future scenarios for births, deaths and migration to estimate a probability distribution for future population. Time series models are usually used to create the random scenarios, but, for many places, data to empirically develop reliable time series models are unavailable. Linked above is R code that I developed for an expert-based stochastic population forecast for Alaska, using time series models from reasoned ranges of random coefficients. To see the code/forecast work, and a selection of output-graphics, you should be able to simply select-all of the code, copy, and paste into an R command line (input data (csv format) is linked through the Internet, and can be simply downloaded for review). The related working paper with full description of the work is available here. If you would like to apply this forecast code to another area (probably not hard), and have any questions, don't hesitate to shoot me an email at edyhsgr@protonmail.com. –January 2011 (updated February 2022) ^

Eddie's Ratio-Correlation Population Estimates Code*

(population estimates, R code)

There are three often-used methods for annually estimating the total population of local areas in the United States: (1) Administrative Records (including Component Method II), (2) Housing Unit, and (3) Ratio-Correlation (a regression-based method). Linked above is simple R code to create Ratio-Correlation population estimates for Colorado counties. To run the code for Colorado, and see selected output-graphics, you should be able to simply select-all, copy and paste into an R command line (the input data (csv format) is linked through the Internet, and can be simply downloaded for review). If you would like to plug in data for another area (simple), and have any questions, don't hesitate to shoot me an email at edyhsgr@protonmail.com. The related paper with full description of the Ratio-Correlation Method and its application to Colorado counties is available here. Here (archived copy, many thanks Internet Archive) is a link to a draft chapter on Ratio-Correlation for the forthcoming book Subnational Population Estimates by Swanson and Tayman - can't beat that. Also, if you have access to JSTOR, here is an amazing paper from 1911, by a researcher named E.C. Snow, that uses multiple correlation (probably for the first time) to estimate population, and here is a terrific paper by Schmitt and Crosetti (1954), which gives one of the first descriptions of the actual Ratio-Correlation method for total population estimation. –January 2011 (updated September 2023) ^

Eddie's Median Age Calculation Code*

(age measures, R code)

The median age of a population is an important, often-used, and tricky-to-calculate figure. One day I searched the Internet for typical steps used to calculate the median age for a given age profile, and came across this terrific site with instruction. I then made the above-linked R function/script to carry the steps out automatically for fixed age-group-sizes. Looking back at it, I'm not sure if it's as efficient as it should be, but it was fun to make and seems to do the job (empirically tested). To see the function/script work, you should be able to simply select-all of the code, copy, and paste into an R command line (example input data (csv format) is linked through the Internet, and can be simply downloaded for review). If it's somewhat confusing, that's my fault, and you can certainly send me an email at edyhsgr@protonmail.com if you have any questions. –January 2011 (updated December 2018) ^