estimator (Silva & Tenreyro 2006) demonstrated by Fally (2015): the solution of the GE system of equations derived from a gravity model can indifferently be estimated (Anderson, Larch & Yotov 2018, Fontagn ́e & Santoni 2021) or computed with a solver Head & Mayer (2014). And when the error term is in a multiplicative form (Anderson et al. 2018) this is equivalent to the so-called “hat algebra” resolution in line for instance with the approach coined as “trade theory with numbers” (Arkolakis, Costinot & Rodr ́ıguez-Clare 2012) – and thus not fundamentally different from what a resolution of a CGE implies.

A natural extension of such modeling approach is to assess the uneven impacts of PTAs (Baier, Yotov & Zylkin 2019), provided that the “ambition” of signed agreements differs strongly. In this paper, we explore the uneven economic impacts deriving from the diverse provisions contained in PTAs. Indeed, competition policy, by allowing foreign competitors to benefit from a level playing field should have more trade creation and less trade diversion effects compared to a simple phasing out of tariffs among members of the PTA. Alternatively, a provision on government procurement might facilitate trade between members of the agreement but increases trade costs vis-a-vis third countries that do not receive the same access to members’ public procurement markets. In other words, the varying content of PTAs may be an important determinant of their uneven trade effects. Providing a new method to assess these effects and offering a first application using new data on the content of PTAs is the purpose of this paper.

Our starting point is to rely on an exhaustive description of the provisions included in PTAs based on a new database on the content of PTAs that has been collected by the World Bank (Mattoo, Rocha & Ruta 2020). We use information on all policy areas (except tariffs) encompassing objectives, substantive commitments and enforcement procedures present in legal texts and available annexes of the 278 PTAs in force and notified to the WTO up to 2018. Examples of policy areas covered in the database include competition policy, Stateowned Enterprises (SOEs), subsidies, public procurement, Technical Barriers to Trade (TBTs) and Sanitary and Phytosanitary Standards (SPS), labor rights, or environment.

Such very rich set of information has to be collapsed in broad categories of PTAs in order to be tractable in a GE framework. The first step of the analysis is therefore to define statistically significant groupings. We rely on a clustering approach to identify groups of trade agreements based on the provisions’ content. In doing so, we opt for the iterative “kmean++” algorithm developed by Arthur & Vassilvitskii (2007) which ensures greater accuracy by randomizing starting points at each replication. Given the underlying distribution of provisions in each of the 18 policy areas in the data, the silhouette width criterium (which evaluates cluster fit on withingroup cohesion and between-group separation) recommends 3 clusters. In words, with 3 groups the distance between observations within each group of PTAs is minimized and the distance between groups is maximized. The underlying assumption is that each group of PTAs has a similar content of provisions by policy subject, and thus should impact differently the trade costs between members of the agreement and between the latter and 3