lnXijt = β0 + β1EIAijt + δit + φjt + γij + εijt (6) where Xijt is a vector containing trade flows from country _i_ to _j_ in time _t_ for total aggregate (X) bilateral trade flows as well as intermediates (Int), and other (Oth) goods categories, per the BEC classification breakdown. The category for other goods includes consumption goods, capital goods, and goods not specified elsewhere (Appendix 2). EIAijt is a time-variant binary variable (described in section 2.2) equal to 1 if exporting country _i_ and importing country _j_ have an EIA in force in year _t_ . The EIA variable used in the specifications presented in this section was constructed from the DESTA dataset. The terms δit and φjt are exporter-time and importer-time fixed effects, respectively, that control for time-varying country-specific variables that include both bilateral trade costs and GDPs. Importantly, these controls include both “multilateral resistance” terms Ωit and Pjt described in equation (4). Lastly, γij is a country-pair fixed effect used to capture timeinvariant factors influencing trade flows (and commonly used in gravity equations, such as common language among trading partners, contiguity, common colonizer, etc.).<sup>13</sup> In addition to estimating this equation for disaggregated goods, our baseline specification differs from that in BBF in three key ways. First, whereas BBF estimate their version of equation (3) using five-year differenced data, we choose to estimate our gravity specifications in log-level form as in Baier and Bergstrand (2007). This implies that we are going to be looking at the effect of EIAs on trade volumes, instead of the growth rate of trade flows. Second, our country sample includes 89 additional countries beyond both Baier and Bergstrand (2007) and BBF. Third, whereas BBF examine the impact of agreement depth on trade flows, we use a binary EIA variable in order to keep the discussion focused around the effects of geographic proximity. We do follow the Baier and Bergstrand (2007) and BBF convention of using panel data over five-year intervals (1965, 1970, 1975, ..., 2010) to isolate our results from year to year variability.

In our first pass at better understanding the interplay between closeness and EIAs we augment the baseline gravity specification with an interaction term between the CEPII capital city to capital city distance measure<sup>14</sup> and our EIA dummy from the DESTA dataset, as shown in equation (7): For robustness, we also add a vector of controls to account for trade agreement depth. This is pertinent, as distance and depth of agreement are correlated (see section 3.2). We thus estimate: where Dijt is a vector comprising the seven dummy variables for each provision category in the DESTA dataset, as described in section 2.2.

> 13 This specification is equivalent to that used in BBF for aggregate trade flows only.

> 14 CEPII provides various measures of bilateral distance. The most frequently used are simple distance, which measures distance from capital city to capital city, and population-weighted distance (both detailed in Mayer and Zignago (2011)). Regression results when using the two different measures were very similar (as was also found by Johnson and Noguera (2012)). Therefore, we report results using the simple distance measure since it is more commonly used in the gravity literature.

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