Fidrmuc 2015), literacy rates and linguistic diversity (Melitz 2008), population and population dispersion (Chung and Wall 2005), and a common currency (Head et al. 2010), and policies that might affect demand or production costs, such as labor compliance.

We first follow the commonly used log-linearized form (LLF) to estimate equation (1). However, we acknowledge the potential biases of zero trade flows occurrences and the heteroskedasticity often found in trade data raised by Head and Meyer (2014) and Yotov et al. (2016). Zero trade observations do not occur randomly and when used in a log-linear model they are automatically dropped from the estimation since the logarithm of zero is undefined. The latter not only renders parts of the data unusable for estimation but bias the estimates of the effects of trade costs and trade policy and is also inconsistent with the LLF of the OLS estimator or other approaches that require a non-linear transformation.<sup>6</sup> Dropping these observations or adding one with the goal to have defined logarithms are not potential solutions as they generate information losses and do not solve the problem of a biased estimation.

To address concerns related to the log linearized estimation, we follow the literature (Santos Silva and Tenreyro, 2006; Yotov et al., 2016) and proceed with a second estimation of (1) using a Pseudo Poisson Maximum Likelihood (PPML) estimator. This approach allows to directly use the levels of the dependent variable (instead of the logarithm), avoiding dropping zero trade flow observations. Further, the PPML is a robust approach to the presence of heteroskedasticity (Burger, Van Oort, and Linders 2009, Silva and Tenreyro 2011, Martínez-Zarzoso 2013). Thus, we us it as our main empirical strategy for the gravity analysis. We also include country-specific fixed effects to address potential trade multilateral resistance –the barriers to trade each country faces with all its trading partners– and country-pair fixed effects to control for the endogeneity of trade agreements (Bair and Bergstrand 2007).

# • **Construction of the Data Set**

We employ the Centre d'Études Prospectives et d’Informations Internationales (CEPII) gravity data set to estimate the change in Egypt's imports following Egypt's trade agreements. The CEPII database contains total trade (summed across all HS codes) for each country pair over a long time span, but we limit the data to 1996-2019. The CEPII dataset includes an indicator for regional trade agreements (RTA) for each country pair-year covered by an RTA registered with the World Trade Organization. The CEPII database consists of 248 importers and 248 exporters, and we limit the sample to country pairs with positive trade flows. We draw upon the World Bank’s World Development Indicators data for the annual GDP.

As explained, the empirical strategy compares the change in trade between country pairs that have a regional trade agreement. We generate a dummy variable equal to 1 indicating when Egypt is an importer and another equal to one when Egypt is an exporter and interact each of

> 6 Chung and Wall (2005) find that unless heteroskedasticity is accounted for correctly, gravity models will typically overestimate the effects of integration with regards to trade.

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