# V.2 Estimation

Gravity equations are widely used to analyze the determinants of bilateral trade flows. Chen and Novy (2012) and Arvis et al. (2013) employ a gravity specification to analyze the determinants of bilateral trade costs in a cross-sectional dataset. The determinants of trade costs, as defined above, are estimated in a panel specification with time fixed effects, in line with the established literature (Moïsé, Orliac, and Minor 2011). The regression equation takes the following form: _TCijt_ = β1 _RTAijt_ + β2 _tariffijt_ + β3 _LSCIijt_ + β4 _LPIijt_ + β5 _Trade Policy Uncertaintyijt_ + β6 _Gravityij_ + η t + ε _ijt_ , (1) where, for any given country pair ij, bilateral trade costs _TC_ observed at time _t_ are regressed on a wide range of candidate drivers. These include membership in a regional trade agreement (RTA); sector-specific bilateral tariffs; shipping connectivity (UNCTAD’s Liner Shipping Connectivity Index, or LSCI) and logistics (the World Bank’s Logistics Performance Index, or LPI); a proxy for trade policy uncertainty; and standard gravity indicators (distance, a common language, and a common border). In line with Osnago, Piermartini, and Rocha (2018), trade policy uncertainty is defined as the gap between binding tariff commitments and applied tariffs. To ascertain the role of policies aimed at facilitating trade, indexes of logistic performance and maritime connectivity are included.

The model is estimated for the economy as a whole and for manufacturing separately. The regression uses bilateral trade data for 2007-18 for up to 23 advanced economies and 72 EMDEs for which data on trade costs and its determinants are available (table 1). The choice of variables in the panel is informed by Arvis et al. (2013), but also by findings from the discussion of the drivers of trade costs presented in the previous sections. Full details on data and sources are presented in annex A (table 2).

In the estimation results, all coefficients have signs and magnitudes consistent with expectations from the literature (annex A, table 3). Geographic distance and bilateral tariff rates are positively associated with trade costs, while proximity, common language, and membership in a common RTA tend to reduce trade costs. Specifically, membership in a common RTA lowers bilateral trade costs statistically significantly, by just under one-fifth.<sup>6</sup> Greater trade policy uncertainty is also associated with higher trade costs, including in the manufacturing sector.

The regression results help shed light on the sources of the higher trade costs in EMDEs than in advanced economies and of the decline in trade costs over time. In 2018, trade costs for the average EMDE in the regression sample were almost one-quarter higher

> 6 This is somewhat smaller than the effect found by Bergstrand, Larch, and Yotov (2015), who estimate that an economic integration agreement lowered trade costs by 30 percent in a smaller and earlier sample (41 mostly advanced economies during 1996-2000). Qualitatively, the results are consistent with Brenton, Portugal-Perez, and Regolo (2014) who found that trade agreements helped to reduce the price differential between domestic and traded foods.