Mapping regionalism and IGOs: a new dataset

Jordi Mas Elias | UOC

Contents

  • Why do we need a dataset?
  • Selecting the cases
  • The variables
  • Conclusion

Why do we need a dataset?

  • Claims to foster comparative forms of studying regionalism (Börzel and Risse 2016; Söderbaum 2016: 30–31)
  • Most inferences drawn from case studies
  • Lack of comparative measures in the field … few large N studies
  • Growing empirical substance: IGO dataset (Pevehouse et al. 2019) and MIA dataset (Hooghe et al. 2017)

The dataset: Three main requirements

  • it only contains a single unit of analysis
  • the observations are represented in rows
  • the variables are represented in columns (Wickham 2014)

Region as observation

Socially constructed space formed by more than two members of the COW-defined state system, located between the global and the national level, that has shared institutions and that makes references to territorial location and to geographical or normative contiguity. The timeframe encompasses the period 1950-2010, since it is the period where more data is available.

Region as observation

  • More than two members: Shared agreement.
  • COW-defined state system: Data availability and reliability.
  • Shared institutions: Delimit the groups of states.
  • Global and national level: Not world, UN.
  • Geographical or normative contiguity: Regional scope.
  • 1950-2010: Data availability.

Variables

  • Drivers of regionalism: regional institutions, economic homogeneity, security homogeneity, political homogeneity, power, and culture and identity (Aggarwal and Fogarty 2004; Börzel and Risse 2016; da Conceição-Heldt and Meunier 2014; Hettne and Söderbaum 2000; Hurrell 1995; 2005).

Regional institutions

  • Delegation and pooling
  • MIA dataset

Economic homogeneity

  • Regionalization: Intraregional trade, trade intensity, and trade introversion.
  • Development (mean): GDP
  • Development (sd): GDP

Security homogeneity

  • Security community
  • COW War, MID, Formal Alliances

Political homogeneity

  • Number of veto players
  • Regime type (mean): V-dem / DCD
  • Regime type (sd): V-dem / DCD
  • Political preferences: UN Votes

Power

  • Hegemon: Largest GDP
  • Concentration: Fractionalization index
  • Polarization: Polarization index
  • U-Shaped: Hegemon + small economies

Culture and identity

  • Ethnicity: Ethnic distance
  • Language: Linguistic distance
  • Religion: Religious distance
  • Legal systems: Fractionalization index

Conclusion

  • Stage of refining variables and dataset construction
  • Contribute to large N studies on regionalism and other fields

Questions?

jordimas.cat/

UOC