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SOC 3223 Lecture 5: Demography Data Sources and IPUMS/PRB/ACS (Vocabulary Flashcards)

Course and Lecture Details

  • SOC 3223: Population Dynamics and Demographic Techniques
  • Lecture 5
  • Date: September 8, 2025
  • Instructor: Rogelio Sáenz

Outline

  • Recap
  • Quiz 1 taking place today
  • Required readings
  • DEMOGRAPHY MATTERS TODAY
  • Specific data sources
    • American Community Survey (ACS)
    • Student Hometown Demographic and Socioeconomic Data
    • Population Reference Bureau (PRB)
    • Integrated Public Use Microdata Series (IPUMS)
  • Recap

Recap (Lecture context)

  • Review of the preceding lecture on Demographic Data
  • DEMOGRAPHY MATTERS TODAY: “Allentown, PA, a former industrial town reborn” (case/illustrative example)
  • Sources of demographic data include:
    • National censuses
    • Registration systems
    • Population registers
    • Vital statistics
    • Surveys

Don’t Forget

  • Quiz 1 today

Required Readings

  • "How will we measure the accuracy of the 2020 census?" PRB resource (link):
    https://www.prb.org/resources/how-will-wemeasure-the- accuracy-of-the-2020-census/
  • “How Abbott cost Texas a House seat.” (link via Express News):
    https://www.expressnews.com/opinion/commentary/article/Commentary-How-Abbott-cost-Texas-a-Houseseat-16159960.php

Two decades, no headcount: How lack of actual demography is distorting Nigeria's growth (Lecture theme)

  • Article: Philip Ibitoye, September 7, 2025
  • Includes a map-like regional frame (focus on demographic undercount in Nigeria)
  • Illustrates how lack of up-to-date demographic headcounts distorts growth assessments
  • The figure shows political-administrative divisions and population concentration (context for data quality/coverage issues)

DEMOGRAPHY MATTERS TODAY (NYT summary from the reading)

  • Article:
    • "Is the Jobs Data Still Reliable? Yes, at Least for Now." by Ben Casselman, Sept. 5, 2025
    • Context: following a weak jobs report, the President fired the head of the Bureau of Labor Statistics (BLS) and named a loyalist to run the agency’s department
    • Key questions: Can this month's numbers be trusted? What safeguards exist?
  • Bottom line from economists and government statistics experts:
    • Yes, the data remains reliable for now, but with caveats that are always present in statistical data
  • Key actors and terms:
    • Erika McEntarfer (former BLS head) and E.J. Antoni (conservative economist potential appointee)
    • Deputy commissioner William J. Wiatrowski (acting commissioner)
    • Erica Groshen (former BLS head under Obama) described Wiatrowski as a committed
      "B.L.S. lifer" with safeguards in place
    • Aaron Sojourner (economist) notes safeguards; data is highly automated and decentralized
  • Data process and caveats:
    • The monthly payroll figure relies on company-reported data through automated systems
    • The commissioner does not access final numbers until after finalization; political influence is constrained by workflow
    • Preliminary estimates (like the upcoming August figure) will be revised as additional data arrives
    • Revisions can be substantial (e.g., May–June revisions totaling 258{,}000 jobs revised downward in a recent example)
    • Historical revisions: Some downward revisions have occurred, sometimes signaling momentum changes, but not definitive signals of recession
    • Experts caution: a potential risk if political actors undermine credibility over time; nonetheless, most analysts maintain data reliability in the short run
  • Overall stance:
    • The raw numbers are considered reliable for now, but it remains prudent to interpret with an understanding of revisions, seasonal patterns, and data collection realities

SPECIFIC DATA SOURCES

  • The session introduces major data sources used in population and demographic analysis:
    • American Community Survey (ACS)
    • IPUMS (Integrated Public Use Microdata Series)
    • PRB (Population Reference Bureau) World Population Data Sheet
    • Hometowns dataset (demographic/socioeconomic profile for SOC 3223 students)
    • Nigeria/Nigeria-analog demographic data discussion (context for data gaps)

American Community Survey (ACS)

  • ACS background and evolution:
    • The U.S. Census Bureau redesigned the long-form questionnaire (transition from decennial long form to ACS)
    • Historically, decennial census used a short form plus a long form; long form was replaced by ACS
  • 1% Long-Form-equivalent sample (pre-ACS long form)
  • Moving picture rather than a single decennial snapshot:
    • Since 2005, ACS provides a moving picture of the U.S. population every year
  • Geography and units of analysis:
    • Geography blocks include: blocks, block groups, census tracts, places (cities), counties, states
  • Period estimates:
    • 1-year estimates for nation, states, and large geographies (populations of 65,000+)
    • 5-year estimates for smaller geographies (< 65,000)
  • Access point and tools:
    • Data source: data.census.gov
    • The system displays official U.S. Census Bureau data with map/table interfaces
  • Example: Mercedes city, Texas data profile (illustrative of a city-level ACS profile)
    • Shows how to read ACS tables (e.g., P1, DP03, S1501, B01001, etc.) and geographic profiles
  • On-screen example content (from slides):
    • Includes narrative summaries and table/table-entry examples for a specific city (Mercedes, TX) including population, employment, income, education, health, etc.
  • Key ACS table ideas mentioned:
    • B01001: Sex by age
    • DP02: Demographic characteristics (housing, families, etc.)
    • DP03: Selected economic characteristics
    • S1501: Educational attainment
    • Income/poverty and housing tables (B25002, etc.)
  • Data navigation notes:
    • The ACS 5-year estimates are frequently used to analyze smaller geographies; 1-year estimates are used for larger geographies
    • The data density and accuracy depend on geographic level and sample size

ACS Data Tables and Hometowns Data (Mercury city example and beyond)

  • Mercedes city, Texas (illustrative ACS 5-year estimates):
    • Population: e.g., total around 16,449 (ACS detail), with breakdowns across sex, age, race/ethnicity, education, income, and housing
    • Examples of variables (from slide excerpts):
    • B01001: Sex by Age (breakdowns by race categories such as White, Black, Asian, etc.)
    • B01002: Median Age by Sex
    • B19113: Median Family Income in the past 12 months (in 2023 inflation-adjusted dollars)
    • S1501: Educational attainment (bachelors or higher)
    • DP03: Economic characteristics (employment, industry, etc.)
    • Geography snapshot components: Nation, State, County, Place, ZIP Code Tabulation Areas (ZCTAs), etc.
  • Hometowns dataset (SOC 3223 student hometowns, 2019–2023 response set):
    • Hometowns listed include: Alice, TX; Alief, TX; Austin, TX; Baytown, TX; Boerne, TX; Bulverde, TX; Canyon Lake, TX; Chicago, IL; Corpus Christi, TX; Daphne, AL; Donna, TX; Eagle Pass, TX; El Paso, TX; Harlingen, TX; Houston, TX; Katy, TX; Kerrville, TX; La Palma, CA; Lake Jackson, TX; Laredo, TX; McAllen, TX; Piedras Negras, Coahuila, Mexico; San Antonio, TX; Sugar Land, TX; Yanbu, Saudi Arabia
    • Each row contains multiple columns of demographic and socioeconomic indicators, including:
    • Population and population shares by race/ethnicity (e.g., pctafram, pctaian, pctasn, pctlat, pctmltrace, pctsor, etc.)
    • Nativity and place-of-birth indicators (pborninst)
    • Education attainment (pctbach+, f p Bach+, m p bach+)
    • Median age (mdnage, fmdnage, mmdnage)
    • Sex ratio (sexratio)
    • Ownership and housing characteristics (pownhome, mdvalhome)
    • Health insurance coverage (p<19noins, p1964noins, p65+noins)
    • Family/poverty measures (mdfaminc, pfampov, fp18+pov, mp18+pov, fpampov)
    • Some caveats in the dataset:
    • Data for Alief, Texas unavailable (don’t know the reason)
    • Some data available for 2023 and drawn from multiple sources (e.g., Point2 Homes, Statistical Atlas)
    • Piedras Negras (Mexico) and Yanbu (Saudi Arabia) datasets are limited and approximate (2020/2022 data, with external sources cited)
  • Definitions (examples from the slides):
    • pop = Population
    • pctafram = Pct. of population African American
    • pborninst = Pct. of population born in state of residence
    • pimmnat = Pct. of immigrants who have naturalized citizenship
    • mdnage = Median age; fmdnage = Female median age; mmdnage = Male median age
    • B01001 = Sex by Age; B01002 = Median Age by Sex; B19113 = Median Family Income
    • p<19noins = Pct. of persons 0-18 years without insurance; p1964noins = 19-64 years without insurance; p65+noins = 65+ without insurance
  • Data interpretation cautions:
    • Some towns have missing data or limited data availability; not all variables exist for all geographies
    • The Hometowns table combines ACS data with alternate sources for non-U.S. locations (e.g., Piedras Negras, Yanbu)

Population Reference Bureau (PRB) — World Population Data Sheet (2024)

  • Contents overview:
    • Special Focus sections by region: Africa; Americas; Asia; Europe; Oceania
    • World Population per 10,000 population for Nursing and Midwifery Personnel (the metric shown as a regional distribution figure)
    • GLOBAL TOTAL FERTILITY RATE; Infant mortality; Life expectancy at birth; Urban population percentage; GNI per capita (ppp)
    • Notes & Sources; Definitions; Data tables across regions and countries
  • Key highlights from the 2024 sheet:
    • Global message: investments in Primary Health Care (PHC) can significantly improve health outcomes
    • Approximately 50% of the world’s population lacks access to good PHC
    • PHC as a platform for integrated health service delivery across the life cycle (pregnancy care, childhood immunizations, care for noncommunicable diseases, etc.)
    • Scaling up quality PHC with workforce resources could prevent up to 60 imes 10^6 deaths by 2030, potentially increasing global life expectancy by 3.7 years
    • Shortages of skilled health professionals contribute to overworked staff and reduced quality of care
  • Health workforce data (illustrative figure):
    • Number of Nursing and Midwifery Personnel per 10,000 Population, with categories:
    • <15
    • 15–35
    • 35.1–60
    • 60.1–120
    • 120.1–525
    • Data not available
    • Note: Data reflect most recent available years between 2018 and 2022
  • Regional and country data structure (examples):
    • World totals and regional groupings; country-level values include births, deaths, fertility, life expectancy, urbanization, GNI per capita, health care coverage, and PHC-related metrics
    • Regions include: NORTHERN AFRICA; AFRICA; NORTHERN AMERICA; AMERICAS; LATIN AMERICA AND THE CARIBBEAN; CENTRAL AMERICA; WESTERN ASIA; ASIA; OCEANIA; EUROPE; NORTHERN EUROPE; etc.
  • Notes on data presentation:
    • The sheet presents many variables with definitions and units (e.g., births per 1,000; deaths per 1,000; life expectancy in years; urban population percentage; GNI per capita, PPP)
    • Data sources and notes accompany each region and country; some values are labeled as estimates with confidence intervals (not shown in detail in slides)
  • United States and Canada (sample takeaway from the North American section):
    • United States population mid-2024 roughly 336.6 million
    • Births around 11 per 1,000; Deaths around 9 per 1,000
    • Life expectancy at birth in the upper 70s to around 80 years (approximate trend in the sheet)
    • GNI per capita (PPP) around 60{,}700 (US figure in the sheet)
  • Oceania snapshot (Australia, New Zealand, etc.):
    • Australia population around 27.3 million; life expectancy in the low 80s
    • Health workforce and PHC indicators vary by country
  • General interpretive notes:
    • The PRB data sheet is a global reference tool for comparing population health, demographic indicators, and PHC strength across regions and over time
    • It emphasizes policy relevance: investments in primary health care and health workforce expansion can yield large longevity and poverty-reduction benefits

Primary Health Care and Health Workforce (PRB, World Population Data Sheet 2024)

  • Central claim: Access to and quality of Primary Health Care (PHC) are foundational to improved population health outcomes
  • Core statements:
    • About 50 ext{ extbackslash%} of the world’s population lacks access to good PHC
    • PHC is a platform for comprehensive, continuous care across the life cycle (pregnancy care, immunizations, noncommunicable diseases, etc.)
    • Scaling up PHC in low- and middle-income countries could prevent as many as 60 imes 10^6 deaths by 2030, increasing average life expectancy by 3.7 years
    • Resource constraints include shortages of trained health professionals, leading to overworked staff and service quality concerns
  • Health workforce data (per 10,000 population):
    • Trends show varying levels of nursing/midwifery personnel across regions; data are the most recent available (2018–2022)
  • Regional highlights (examples):
    • Africa; Northern Africa; Americas; Europe; Asia; Oceania – each with country-level and regional aggregates for PHC-related indicators
  • Implication for students and researchers:
    • Use PRB data to assess health-system strength, PHC capacity, and potential policy impacts on population health and equity

Integrated Public Use Microdata Series (IPUMS)

  • What IPUMS is:
    • Integrated Public Use Microdata Series (IPUMS) provides harmonized census and survey microdata from many countries to enable cross-time and cross-country analysis
  • Major IPUMS data families:
    • IPUMS USA: U.S. Census and American Community Survey microdata from 1850 to present
    • IPUMS CPS: Current Population Survey microdata including basic monthly surveys and supplements from 1962 to present
    • IPUMS INTERNATIONAL: World’s largest collection of census microdata covering 100+ countries (contemporary and historical)
    • IPUMS GLOBAL HEALTH: Health survey data from DHS, MICS, and PMA surveys (harmonized)
    • IPUMS NHGIS: U.S. Census summary tables and GIS data from 1790 to present
    • IPUMS IHGIS: Summary tables and GIS data from population, housing, and agricultural censuses around the world
    • IPUMS TIME USE: Time-use data from 1930 to present
    • IPUMS HEALTH SURVEYS: U.S. health survey data (NHIS since 1963; MEPS since 1996)
  • Access and support:
    • IPUMS site emphasizes free access to population data and tools to analyze change over time and across geographies
    • Includes data are updated regularly and documented (with user guides, sample descriptions, question inventories, etc.)
  • Practical uses:
    • Merge census microdata across decades, study demographic change, compare regions, conduct multivariate analyses, and build custom extracts
  • Motto and reminder:
    • USE IT FOR GOOD — NEVER FOR EVIL

IPUMS: How to Access and Use (SDA and Data Tools)

  • IPUMS USA/SDA interface basics:
    • The SDA (Statistical Data Analysis) environment allows row, column, filter, and control specifications to build tables and analyses
    • Weights: perwt (person weight) is used to ensure representativeness in analyses
  • Steps to build a table in SDA:
    • Select a dataset (e.g., ACS 2001–2023 or 1850–2023 for US data)
    • Choose a row variable (e.g., year, race, education, etc.)
    • Choose a column variable (e.g., sex, age group, etc.)
    • Add controls/filters (e.g., geography, year, region)
    • Choose output mode (append vs. replace) and data display options
  • Output options:
    • Frequencies, cross-tabulations, means, correlations, regressions, etc.
    • Ability to download CSV, or view results interactively
  • Important guidance:
    • For multi-year ACS data, include a year variable in analyses to avoid mixing years
    • The 2020 1-year ACS PUMS file has special considerations; avoid direct comparisons with multi-year samples without guidance
    • COVID-19 and related data collection changes affect interpretability of some ACS periods; consult guidance when necessary
  • Resources:
    • IPUMS online data analysis system (SDA) tutorials and help sections
    • Video tutorials and user forums available on the IPUMS site

Practical notes on IPUMS data and examples

  • Examples of data types accessible via IPUMS:
    • Decennial census microdata (1790–2010 annually; cross-year harmonization)
    • ACS microdata (2000–present; single-year and multi-year samples)
    • CPS microdata for monthly employment and related indicators
    • NHGIS and IHGIS provide aggregated tables and GIS data for geographies and time periods
  • Data integration and harmonization:
    • IPUMS harmonizes variable names and codes across decades/countries to enable cross-time comparisons
    • Documentation and user guides explain variable codes and harmonization schemes
  • Access considerations:
    • Some features require a free IPUMS account (register to download data or use Abacus for online analysis)
    • Abacus (IPUMS Abacus) allows building custom datasets for download
  • Ethical guidance:
    • IPUMS emphasizes responsible use of microdata and privacy-respecting practices

Recap and Next Steps

  • Recap of required readings and data sources:
    • PRB article on census accuracy and measurement; Abbott-related Texas seat piece
    • NYT business/economics piece on job data reliability and BLS governance context
    • ACS as a primary data source for U.S. demographic characteristics (with detailed city profiles and hometown-level data in the course materials)
    • IPUMS as a key suite of harmonized microdata resources for cross-time analysis
    • PRB World Population Data Sheet (2024) as a global health/population reference, including PHC focus and regional country data
  • In the next lecture, we will move to the topic of “Understanding Population Growth” and continue to build a framework for measuring and interpreting demographic change, data quality, and measurement challenges

Key Concepts and Takeaways (synthesis)

  • Demographic data come from multiple sources: censuses, registration systems, vital statistics, surveys, and population registers
  • ACS provides a moving, yearly picture of the U.S. population, replacing the one-shot long-form census in 2005 and beyond
  • The 1-year vs. 5-year ACS estimates balance timeliness and precision by geography
  • IPUMS harmonizes and provides easy access to vast microdata across countries and time for comparative demographic analysis
  • PRB’s World Population Data Sheet highlights global health context, PHC investment benefits, and regional/country-level indicators
  • The reliability of official data (like the U.S. jobs data) can be high, but political context, revisions, and data-collection dynamics require cautious interpretation
  • Hometowns data in this course illustrate how ACS data can be used to profile local demography and socioeconomic status, while also noting data gaps and reliability issues for certain locations
  • Ethical and practical considerations matter when using demographic data for policy, forecasting, or public communication

Next Lecture Preview

  • Focus: Understanding Population Growth (measurement, drivers, and interpretation of growth rates), including methodological notes on data quality, measurement error, and how growth relates to policy and social outcomes

Quiz Reminder

  • Quiz 1 is today. Be prepared to apply concepts from ACS, data reliability, and interpretation of demographic indicators to short-answer and data-interpretation questions

References and URLs (for quick access)

  • PRB: How will we measure the accuracy of the 2020 census?
    • URL: https://www.prb.org/resources/how-will-wemeasure-the- accuracy-of-the-2020-census/
  • Express News: How Abbott cost Texas a House seat
    • URL: https://www.expressnews.com/opinion/commentary/article/Commentary-How-Abbott-cost-Texas-a-Houseseat-16159960.php
  • NYT article: Is the Jobs Data Still Reliable? Yes, for Now
    • URL: https://www.nytimes.com/2025/09/05/business/jobs-data-reliability.html?smid=url-share
  • ACS data access and Mercedes, TX example
    • Data access: https://data.census.gov
  • IPUMS data portal
    • https://www.ipums.org/