# **4. ECONOMETRIC APPROACH: TOBIT MODEL**

We turn now to the bulk of our econometric analysis using the dataset described in Section 2. As we have seen earlier, the fact that some bilateral trade relations switch from zero to positive values (and the factors behind the switch) is what diversification is about. The existence of zeros in the trade matrix is an important piece of information as it accounts for the non-successful experiences of diversification. From an econometric point of view, the way we deal with censored data in our analysis is using the Tobit model.<sup>19</sup> We start the section by presenting our estimating equation, which rests on the theoretical framework in Section 3. Then, we discuss the econometric technique used to estimate the equation (the Tobit model) and we conclude with a discussion of the estimated results.

## 4.1. **_The estimation equation_**

Following the general facts previously found and the theoretical framework presented in Section 3, we estimate the following gravity equation: log( _Vodit_ ) = α + β 1 log( _GDP_ dt_ ) + β 2 log( _Distod_ ) + β 3 _Trade_ North_ + β 4 _FTA_ + _SD_ + _TD_ + ε _t_ (3) where; Vodit is the dollar value of exports from country-o (origin) to country-d (destination), of product i -for each 6-digit category- in year t; GDP_d is the GDP of t the destination country in year t;<sup>20</sup> Distod is the distance between country-o and country-d. In principle, we expect the GDP and the distance to show positive and negative sign respectively.<sup>21</sup> As for the additional specific factors in the bilateral relationship, we include the Trade_North dummy that takes the value of 1 when the trade relationship is with a developed country (North).

Also, following the theoretical approach in the previous section and in order to take into account the reduction of trade costs (e.g. the reduction in the market entry costs), we also created the FTA dummy<sup>22</sup> , which takes the value of 1 for trade relations between countries parties to the same agreement.<sup>23</sup>

> 19 The approach that we use in this paper differs from that of Helpman, Melitz, Rubinstein (2007). 20 We do not include the origin’s GDP in the equation because as we will explain later with more detail, due to computational limitations, we have to estimate the equation for each exporter (origin) separately. However, in order to evaluate whether the time-variant GDP of the exporter mattered, we also included the GDP of the exporting country (origin) in alternative estimations, but it did not change the results. 21 The GDP is expressed in current US dollars and it is extracted from the World Development Indicators. (http://www.worldbank.org). Distance data were obtained from CEPII website (- http://www.cepii.fr/francgraph/bdd/bdd.htm) and it is defined as the distance between the economic centres of one country to another. We have selected Brussels as the economic centre of the EU.

> 22 The trade agreements covered with this dummy are those included in the study for the Global Economic Prospects, World Bank (2005), which in turn includes all those agreements notified to the WTO and that can be found at: <u>http://www.wto.org/english/tratop_e/region_e/region_e.htm; and also</u> other agreements not notified.

23 For robustness purposes we also estimated the model using tariff data -as a proxy of trade costs- for the available 6-digit level categories instead of and in addition to the FTA dummy. Our conclusion is that using tariff data does not undermine the findings. The results do not change drastically.

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