Demography: Key Terms for Population Dynamics (Lecture 3)

Notes on Lecture 3: Demography, Types, and Public Engagement

Recap

• Built on the previous lecture: core questions about demography and its significance.
• Central ideas re-emphasized: definitions, significance of two phrases:
  • "Demography is destiny" \text{(demographic forces shape outcomes over time)}
  • "We are all population actors" \text{(everyone participates in demographic processes in daily life and policy context)}
• Key demography concerns to track:
  • Population size and population growth or decline
  • Population processes (births, deaths, net migration)
  • Population distribution and population structure
  • Population characteristics (e.g., age structure, race/ethnicity, education levels)

Demography Matters Today

• Case study discussed: Katrina and persistent inequality in New Orleans.
• Article: "Persistent Inequality: The Remnants of Hurricane Katrina 20 Years Later" by Rogelio Sáenz (Aug 29, 2025).
• Katrina highlights (summary):
  • Katrina (Aug 29, 2005) caused 1,400–1,800+ deaths; one of the costliest and deadliest U.S. hurricanes.
  • Disparities widened: poor Black residents faced higher mortality rates (roughly \text{1.4} \times\text{ to }\text{4.0} times higher than whites).
  • 2023 New Orleans: population declined by 120{,}000 residents vs 2000, a -25\% drop; Black population fell 39\%, 2.5× faster than the white population.
  • Mobility and origin:
  • Despite resettlement, 84\% of Blacks in New Orleans in 2023 were born in Louisiana (down slightly from 89\% in 2000).
  • Whites born in Louisiana in 2023 fell from 59\% to 48\%.
  • Education: Black adults (25+) with a bachelor’s or higher rose from 13\% (2000) to 28\% (2023); Whites ~75\% hold a college diploma; ratio of White to Black college graduates ≈ 2.6:1.
  • Income and wealth gaps: Black households earned about 0.41 for every 1.00 earned by White households in 2023, down from 0.56 in 2000.
  • Poverty: Black family poverty rate ~32\% in 2000 and ~30\% in 2023; White family poverty ~12\% (2000) and 9\% (2023).
  • Homeownership and wealth: Black homeownership rose from 47\% (2000) to 56\% (2023), but the Black–White gap in home values remained (Black values ≈ 0.55\$ per\$1.00 White\_home_value in 2000, ≈ 0.53\$ per\$1.00 White in 2023).
  • Segregation: Index of dissimilarity ( Blacks vs Whites in the area around Orleans County) was 66 in 2000 and 65.3 in 2023, indicating persistent high segregation (range: 0 = no segregation, 100 = complete segregation).
  • Bottom line: Katrina exposed systemic racism and inadequate protective institutions; future cataclysms could reproduce these patterns unless structural changes occur (note on FEMA’s uncertain status during the Trump administration).
• About the author: Rogelio Sáenz, professor, UT–San Antonio; opinion piece reflects his views, not necessarily the university.

An Intervention re: a Variable

• What is a variable?
  • A characteristic that is not uniform but can vary across cases (e.g., \text{Sex} = {\text{Male, Female}}, \text{Age} = 0,1,2,3,\dots, \text{State of residence}, \text{Education}, \text{Income}).
• Types of variables:
  • Categorical: e.g., Sex, State, Political affiliation, Religion
  • Ordinal: e.g., Education levels (0–8 years, 9–11 years, high school, some college, bachelor’s, etc.)
  • Continuous: e.g., Age, Income, IQ, GNP, Number of births

Unit of Analysis

• Levels of analysis:
  • Individual
  • Small group, family, or team
  • Community, organization, or institution
  • Globe, society, or crowd
• Question to ask: What is your unit of analysis for a given study?

Unit of Analysis (Examples)

• Individual-level example: Relationship between educational level and income for persons 25+ years old
  • Variables: Years of education; annual income/salary
• Aggregate-level example: Relationship between educational level and income for persons 25+ years old in Texas counties
  • Units: Percent with a bachelor’s degree or higher; median annual income by county

Population Concept

• Population Concept components:
  • Population size
  • Population growth or decline
  • Population processes (births, deaths, net migration)
  • Population distribution
  • Population structure
• Population Concept Indicator components:
  • Number of people
  • Absolute or percentage change
  • Births, deaths, net migration
  • Persons per square mile
  • Median age; Sex ratio

Non-Demographic Variables

• Non-Demographic Discipline examples:
  • Economic, Geographic, Psychological, Health, Political
• Non-Demographic Discipline Indicator examples:
  • Unemployment rate; median wages
  • Average temperature
  • Mental health well-being
  • Overall health assessments
  • Political orientation

Direction of Relationship Between Two Variables

• Positive relationship illustration:
  • Example: Salary vs Years of Education (as education increases, salary tends to increase)
  • Visual cue: upward sloping relation (assumed in slide)
• Negative relationship illustration:
  • Example: Female secondary education vs Total Fertility Rate (as female education enrollment increases, fertility tends to decline)
• Quantitative cue shown: R^2 = 0.7058$$ for the education–fertility relationship (indicating a strong fit in the depicted data)

Cause-Effect and Directional Reasoning of Variables

• Core idea: Independent Variable (IV) and Dependent Variable (DV)
• Relationship type: often correlational (not strictly causal in all observational contexts) but discussed in a cause-and-effect framing
• Directionality questions:
  • If IV increases, what happens to DV?
  • Positive relationship: IV↑ ⇒ DV↑
  • Negative relationship: IV↑ ⇒ DV↓
• Examples:
  • IV: Education → DV: Annual Salary (higher education linked to higher salary) → positive relationship
  • IV: Level of exercise → DV: Probability of Death (more exercise → lower probability of death) → negative relationship

Types of Demography

• Formal Demography
• Social Demography

Formal Demography

• Conceptual model: Demographic variables (IV) → Demographic variables (DV)
• Core components: Population Concept and Population Concept Indicator as the drivers of the analysis
• Typical relationships:
  • NEGATIVE RELATIONSHIP: Higher IV leads to lower DV (example: Median Age → Fertility Rate)
  • POSITIVE RELATIONSHIPS: Higher IV leads to higher DV (example: Fertility Rate, Mortality Rate, Migration Rate → Population Projection)
• Example notes:
  • More births and higher net migration tend to increase the population projection; higher mortality or aging can reduce growth unless offset by births or migration

Social Demography

• Definition: The relationship between demographic and non-demographic variables
• Frameworks:
  • Non-Demographic variable (IV) → Demographic variable (DV)
  • Demographic variable (IV) → Non-Demographic variable (DV)

Social Demography: Demographic Variable as DV

• NEGATIVE RELATIONSHIP example: Unemployment rate → Net migration rate
  • Higher unemployment → fewer people move into the area (lower net migration)
• POSITIVE RELATIONSHIP example: Percentage of people without healthcare insurance → Death rate
  • More uninsured people → higher death rate (worse health outcomes)

Social Demography: Demographic Variable as IV

• NEGATIVE RELATIONSHIPS: Higher IV leads to lower DV
  • Example: Percent population growth → Poverty rate (higher growth, lower poverty? as shown, negative relation in this example)
• POSITIVE RELATIONSHIPS: Higher IV leads to higher DV
  • Example: Median age → Conservative political ideology (older populations tend to skew more conservative)

Applied Demography

• Definition: Using demography to address real-world problems and policy questions
• Core activities include: identification of market segments, identifying growing consumer markets, forecasting voter locations, projecting future workforce, projecting dementia prevalence, etc.

Public Demography

• What is Public Demography? (Donaldson 2011 definition and context)
• Public demography aims to bring population information and analysis to nonspecialists.
• Activities include:
  • Popular newspaper/magazine articles
  • Website/blog contributions
  • Talk radio appearances
  • Speeches before community groups
• Purpose: Inform public opinion on population-related issues and guide policy discussions and design.
• Mediums and trends: Web, blogs, podcasts provide an exciting era for public demography
• Key quote (paraphrased from the source): Public demography is presenting population information to the public to shape dialogue and policy
• Institutions often support this work via government and NGOs that value public communication

Writing and Communicating Demographic Research to the General Public… Doing Public Demography

• Focus on translating demographic findings into accessible, non-technical language for broad audiences
• Emphasis on clarity, relevance, and policy impact

Personal Reflections on Doing Public Demography

• Personal background highlights:
  • Pan American University: discovery of demography
  • Iowa State University and Iowa Census Services experience
  • Interest in public policy and Population Reference Bureau / Russell Sage Foundation
  • Policy Fellow at Carsey School of Public Policy (UNH)
• Activities in public demography:
  • Policy briefs, op-eds, general essays
  • Demographic analysis and expert testimony in lawsuits
  • Public demography activities during the pandemic

Quiz 1

• Availability: September 8 (Monday) 12:00 am – 11:59 pm
• Time limit: 35 minutes maximum
• Covered material: Lectures 1–4 (Aug. 25 to Sept. 3), videos, and Demography Matters Today articles; Poston & Bouvier, Chs. 1–3 (What is Demography)
• Question types: Approximately 15–20 questions including multiple choice, fill-in-the-blanks, matching, true-false, listing, calculations, interpretation of demographic measures, and short to moderate essays
• Open-book-like: Students may use class materials

Recap (Final synthesis)

• Core theme: DEMOGRAPHY MATTERS TODAY – persistent inequality illustrated via Katrina case study
• Core concepts covered:
  • Variables and their types (IV, DV; categorical, ordinal, continuous)
  • Unit of analysis (individual vs aggregate)
  • Population concept vs population indicators
  • Directionality of relationships (positive vs negative)
  • Cause-and-effect reasoning (IV → DV) with correlational caveats
  • Formal versus Social Demography (IVs/DVs flow in either direction)
  • Applied demography (real-world applications) and Public demography (informing the public)
  • Writing for the public and ethical/policy implications
• Next steps: Prepare for Quiz 1 on Sept. 8; anticipate coverage from Lecture 1 through Lecture 4 and related readings; note that the upcoming lecture will focus on
  • "Demographic Data" topics