# **1.2 Trade, Production and PTAs**

A fully-fledged general equilibrium structural gravity approach relies on a trade matrix that includes both intranational and inter-national manufacturing and agricultural trade flows. The inclusion of intra-national sales is critical, as the correct benchmark for trade integration is the domestic economy (Head & Mayer 2014, Yotov 2021). The trade database used for the empirical analysis includes both domestic and international sales of agricultural and manufacturing products. Domestic sales are calculated as the difference between the value of production and total exports (both measured in gross terms).<sup>18</sup> Manufacturing output and trade come from UNIDO INDSTAT2 (2022) and UN-ComTrade, respectively.<sup>19</sup> We follow standard practice for filling gaps in intra-national flows if output is missing or implies negative domestic sales. Specifically, we linearly interpolate production values between non-missing data points and extrapolate the remaining missing values using the ratio of gross output to value added as in Head & Mayer (2021).

Agricultural product flows come from the Food and Agriculture Organization of the United Nations Statistics Division (FAOSTAT).<sup>20</sup> The FAOSTAT reports a Detailed Trade Matrix in both quantities (in tons) and values (in thousands of US dollars) from 1986 to 2018 and a disaggregated agricultural production data. As done for the manufacturing sector, after filling in some missing values following the same procedure mentioned earlier, we combined production and trade data into a dataset containing both domestic and international sales.

Finally, we combine manufacturing and agricultural data and aggregate, for each year, the underlying flows by country of origin and destination. The final dataset includes, for each year, only countries with non-missing (or nonnegative) domestic sales. Overall, the estimation dataset covers 137 countries (34 of which in the African continent) on 1-year intervals from 1986 to 2018. The counterfactual exercises are computed for year 2018 and 2014.<sup>21</sup> Trade and production data are then combined with the list of in force Preferential Trade Agreement (PTAs) as provided in Fontagn ́e et al. (2023). Using various clustering techniques, Fontagn ́e et al. 2023 show that existing PTAs can be classified into three optimal groups based on the distribution of provisions included in each of the 18 policy areas covered by the World Bank Deep Agreement database. In what follows, we use this classification and estimate the elasticity of bilateral trade flows to different types of PTAs.

> 18If the resulting value of domestic sales is negative, it is considered missing. Head & Mayer (2014) discuss an alternative options based on ratios and proposed by Romalis (2007), and Caliendo & Parro (2015) .

> 19Manufacturing data are available at `https://stat.unido.org/` and `https://comtrade.un.org/`

> 20Agricultural data are available at `http://faostat3.fao.org/home/E` .

> 21In year 2018, the dataset covers 112 and (20 of which in the African continent); while in 2014 the total number of countries is 132 (30 of which in the African continent). Importantly, these data do not include informal trade, which can be a significant part of total agricultural trade (Bouet, Pace & Glauber 2018). The likely underestimation of trade flows between African countries may also have an impact on the estimated gains from the reduction of trade barriers between them.

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