The Political Economy of Discrimination: Modelling the Spread of Preferential Trade

Agreements

Mark S. MangerAssistant Professor

McGill Universitymark.manger@mcgill.ca

What explains the geographic variation in the spread of PTAs?

Research Question

•Milner (1997), Chase (2003, 2005): Multinational firms seek greater economies of scale through PTAs. Problem: Suggest no PTAs in Asia Pacific

•Chase (2005): PTAs to facilitate regional production sharing. Problem: Predicts PTAs with China, not ASEAN

•Mansfield, Milner, Rosendorff (2002): Democratic Countries are more likely to sign PTAs. Problem: Everybody’s doing it now.

Current Knowledge:Bottom-up Explanations

• Grieco (1997): Capabilities shift hypothesis; disadvantaged countries will shun PTAs with rising powers. Problem: Opposite of what we observe.

• Mansfield and Reinhardt (2003): Growing WTO membership creates friction; more PTAs induce states to likewise sign agreements. Problem: Leaves geographic pattern of PTAs unexplained.

Current Knowledge:Top-down Explanations

• Baldwin’s (1996) predicts regional spread of PTAs. Countries join agreements because they fear trade diversion.

• Problems:

trade diversion is not evident

complex network of PTAs does not conform to expectations. Countries rarely join existing PTAs.

Current Knowledge: Domino theory of regionalism

• Countries will sign PTAs when their neighbours are doing so.

• Baldwin hypothesis: trade diversion leads countries to join existing PTAs with neighbours

• Existing PTA not open to expansion or unattractive: Excluded countries form alternative PTAs with other proximate countries

• Competition for export markets or FDI: Developing countries sign PTAs when their neighbours are gaining preferential access to major markets

Spatially Dependent PTAs

First-cut at only modelling geographic proximity

Undirected dyad-year framework with binary dependent variable

DV: All reciprocal PTAs 1960-2004, not counting partial scope agreements

Estimators: Familiar logit with cubic splines; Bayesian MCMC

W= row-standardized spatial weight matrix of the dependent variable at t-1 divided by distance

Model

Economic model:GDP per capita for i and jGDP per capita j Difference in GDPDistance between i and jBilateral tradePolitical economy variables:

Spatial weights in Wy

Trade dependence i on jTrade dependence j on i

Controls:

AllianceDemocracy i and j

Multilateral trade round underway

New dispute with 3rd partyDispute loss with 3rd party

Hegemony

PTA density and PTA density squaredNumber of WTO members

Trade partner PTA coverage iTrade partner PTA coverage j

Variables

•Space is not just geography: explore similarity measures like GDP/cap, export profiles, trade links.

•Develop appropriate spatial lag estimator for binary DV panel data

•Computational challenges: R is sloooow

Future research avenues