MACRO-COMPARATIVE MACRO-COMPARATIVE QUANTITATIVE QUANTITATIVE

RESEARCHRESEARCHDemography Colloquium

Shawn F. DoriusNovember 12, 2009

“The fundamental rationale of comparative research: to learn about one setting, we

must necessarily examine others” (Western 2001).

1.Units of Analysis2.Types of Analysis3.Pitfalls4.Data Sources

Unit of AnalysisUnit of Analysis• Individuals

– Micro-level data and context effects (ISSP; Eurobarometer)

• Societies– Sovereign & non-sovereign nation-states (US and Puerto

Rico)

• Economies– Independent economic units (China, Hong Kong, and Macao)

• Special Administrative Regions

• Polities– Independent sovereign governments (US, but not Guam)

• Populations• Nation-States (Indian population)• Cultures (Muslim, Hindu, Catholic)• Within-nation groupings (Native vs Foreign-Born)

Types of AnalysisTypes of Analysis

• Comparative– Typically compare two units (China and Taiwan)– Must have clear justification for units of comparison– Think experiments (T & C)

• Regional– Middle East, Latin America– Typically requires specialized knowledge of culture,

language, history, etc.

• International/Global– Country and population coverage is critical– Population weights

Population Weights: Between-Population Weights: Between-Nation Income InequalityNation Income Inequality

Unweighted

Weighted

SOURCE: Milanovic (2005, Table 11.1)

Pitfalls: FallaciesPitfalls: Fallacies

• Ecological Fallacy– Aggregate Individual– Robinson (ASR 1950)

• 1930 State-level correlation: Prop foreign born and literacy rate (.53)

• 1930 Individual correlation: Immigrant status and literacy (-.11)

• Atomistic/Individualistic Fallacy– Individual Aggregate– Stereotypes: Muslims/Terrorists

Pitfalls: Units Change Pitfalls: Units Change • Names change

– Ceylon Sri Lanka– Zaire Democratic Republic of the Congo– Rhodesia Zimbabwe– Kampuchea Cambodia

• Decolonization– The number of countries changes over time,

particularly after 1940.

• Countries merge and divide over time– Germany, Yugoslavia, Czechoslovakia

Pitfalls: AnalysisPitfalls: Analysis1. Small sample size (country-level units)

1. Pool data2. Parsimonious, theory driven models

2. Interdependence among units1. Fixed effects2. Clustering (robust standard errors)

3. Low comparability of data between units1. Intersect theory and methods (middle class)

4. Under representation of developing countries1. Blood, sweat, tears, and creativity

Pitfalls: CategoriesPitfalls: Categories1. Apply country-level grouping schemas

with caution.2. Goal should be to explain variance

1. Think X Y when assigning countries to groups2. Grouping schema should reflect dominant X

3. Common grouping schema1. Europe & Offshoots (neo-Europe’s)2. Eastern Europe & Russia3. Latin America & Caribbean 4. Asia & Pacific5. North Africa & West Asia (MENA)6. Sub-Saharan Africa

Data SourcesData Sources• Data Clearinghouses:

• ICPSR• United Nations

– Cite source and not data clearing house• World Bank (World Development Indicators)

– Economies• DHS, WFS

– Library Holdings• CDs

– WISTAT, World Christian Database

• Books– Brian Mitchell: International Historical Statistics – Jean Claude Chesnais: The Demographic Transition…

Data SourcesData Sources

• Institutional Websites:• PRI• COW• Emory• London School of Economics• American Religion Data Archive

– Articles• Tables and Appendices• Datasets

– Fellow Researchers

Data Sources: ArticlesData Sources: Articles