heterogeneity including indirect transport costs, wholesale and retail distribution costs, belonging to the WTO, the quality of institutions, domestic regulations, for example a nation‟s customs procedures or the ease of obtaining the required documentation to trade, infrastructure and geography, levels of corruption and domestic security protocol. Additionally, other channels through which migrants could potentially influence trade will also be controlled for, for example through accounting for any rise in the stock of human capital.

Equation 8 is estimated using the least squares dummy estimator (LSDV henceforth). Although algebraically analogous to the standard panel „within‟ estimator, the LSDV estimator yields an R<sup>2</sup> which may serve as the basis for comparison, for the cross-section and pooled results in this paper and the rest of the estimates in trade gravity literature more broadly. In the presence of many zero trade flows however; Santos Silva and Tenreryo (2006) demonstrate how heteroskedastic residuals may lead to inconsistent results, in which case they argue the Pseudo-Poisson Maximum Likelihood estimator is appropriate. The LSDV estimator is nevertheless chosen for analysis since there are no zero observations whatsoever in the underlying trade data. There exist missing values however. The key question therefore is whether these represent true zero values, in which case they need to be handled with care; or whether they are actual missing values. Since aggregate trade data are used, there is every reason to believe that the majority of these „missing values‟ are indeed missing. This is especially so in the later period to which the trade data refer, since small values are not reported due to the financial constraints faced by the authors (see below). Indeed, the conclusion of private correspondence with the authors of the dataset was that these values should be treated as missing, since it was argued, it was a far bigger assumption to assume these missing values are all zero. For the sake of robustness, several samples, which have varying degrees of missing values, are estimated to ensure that their presence do not lead to spurious results.

# _5. Data_

The paper draws upon data from three main sources. Migration data are obtained from Özden et al (2011), which details five origin-destination matrices that comprise every nation state, major territory and dependency from across the globe (226*226). The dataset is based upon the foreign-born concept of migration and each matrix corresponds to one of the last five completed census rounds, 1960-2000.<sup>15</sup> The data correspond to economic migrants and every effort has been made to remove refugees. Although the proportion of illegal migration captured in the dataset is unknown, it is still likely that a fairly large proportion of illegal migrants are captured in national censuses. This dataset is superior to those previously estimated in terms of its broad global coverage and the number of decades to which it refers. It

> 15 In this paper, the version of the data chosen from Ozden et al (2011) is that which equates the migration data to a specific year, for example, 1970 or 1980 as opposed to the version which pertains to census rounds or decades, for example 1994-2005.

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