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About the article
Published Online: 2013-07-06
In general, there is heterogeneity about any type of agreements regarding the extent of market access and the so-called preference margins granted across different activities (goods with goods trade, services with services trade, and investment-related activities with foreign direct investment). We account for such heterogeneity to the extent that we distinguish between agreements that contain goods trade provisions only, ones that capture goods and services trade provisions, ones that capture goods and investment provisions, and ones that capture goods, services, and investment provisions. However, even GTAs with goods provisions only differ with regard to the tariff lines (products) covered and the preference margins granted (i.e. the difference between the applied tariff outside the agreement and the one charged within the agreement). Accounting for the latter type of heterogeneity lies beyond the scope of this article (see Baier and Bergstrand, 2004, 2007, and 2009, for a similar approach in that regard).
Many GTAs include services trade and investment provisions. For instance, this is obvious from a new data-set compiled by the World Trade Organization. In accordance with that data-set, we classify country-pairs to have services trade provisions whenever they appear in a GTA or a separate STA. Similarly, we classify country-pairs to have investment provisions no matter whether they appear in a trade agreement or a bilateral investment agreement.
We follow the Mundlak-Chamberlain-Wooldridge device to parameterize fixed pair effects as an additive function of averaged time-variant covariates in (see Mundlak 1978; Chamberlain 1982; Wooldridge 2002).
In the following, we provide a highly selective list of examples of work in economics which focused on the determinants or consequences of just one dimension of preferential market access. Work on the causes of agreements. GTAs-theory: Baldwin (1995, 1997); Bond and Syropoulos (1996); Limao and Tovar Rodriguez (2011); Arcand, Olarreaga, and Zoratto (2010); GTAs-empirics: Magee (2003); Baier and Bergstrand (2004); Bergstrand, Egger, and Larch (2010); STAs-theory: Huang, Whalley, and Zhang (2009); STAs-empirics: Egger and Lanz (2008); Francois and Hoekman (2010); Egger and Wamser (2013); BITs-theory: Egger, Larch, and Pfaffermayr (2007a, b); BITs-empirics: Bergstrand and Egger (2011); DTTs-theory: Davies (2003, 2004); DTTs-empirics: Egger, Larch, Pfaffermayr, and Winner (2006). Work on the consequences of agreements. GTAs-theory: Frankel, Stein, and Wei (1995); Freund (2000); Ornelas (2005a–c); Limao (2007); Karacaovali and Limao (2008); GTAs-empirics: Baier and Bergstrand (2007, 2009); Egger et al. (2011); STAs-theory: Huang, Whalley, and Zhang (2009); STAs-empirics: Egger, Larch, and Staub (2012); BITs-theory: Egger, Larch, and Pfaffermayr (2004, 2007a, b); BITs-empirics: Egger and Merlo (2012); Sauvant and Sachs (2009); DTTs-theory: Davies, Egger, and Egger (2010); DTTs-empirics: Blonigen and Davies (2004); Egger et al. (2006); Davies, Norbäck, and Tekin-Koru (2009).
In principal, all time-invariant variables and their parameters in Table 4 could be thought of as being part of the vector of country-pair fixed effects. However, we report the corresponding parameters for convenience. Moreover, one could allow for continent-specific or even country-specific year effects rather than pooled year effects. With the data at hand, this leads to an extremely flat likelihood function which is difficult to optimize for parameter values. Therefore, we resort to the more parsimonious specification.
The results in Egger, Larch, Staub, and Winkelmann (2011) suggest that these two margins can be analyzed as two separate parts of an integrated model.
In general, endogenous treatment effects’ problems are ones of missing data. In our context, say, a country-pair ij with a scope of preferentialism of unity in period t is only observed with that treatment level. Ideally, we would like to compare this country-pair to itself with another treatment level (say, one with a scope of preferentialism of four) in the same period. However, such a data point does not exist. We can only impute (or estimate) it by finding other country-pairs in the same year which are very similar (ideally, they are identical) to country-pair ij.
Similar to linear regression models, matching based on the propensity score requires that all relevant variables are included in the model (so that the estimates – with matching, including the propensity score – are consistent). Notice that Assumption Al is not testable.
For observations to be comparable, country-pairs with a specific treatment and control level of the scope of preferentialism in a given year t should have overlapping probabilities of having the same scope as the treated. Otherwise, the units would be too dissimilar to infer causal treatment effects from.
Suppose units of observation with specific treatment and control levels of the scope of preferentialism had similar, overlapping probabilities of exhibiting the level of treatment of the treated but the respective determinants of those treatment probabilities would be very dissimilar for units with treatment s versus . Then, similarity in propensity scores would be an artifact and not a compact measure of similarity in the underlying , which they are supposed to be.
In principal, one could allow even for country-specific rather than continent-specific year effects. However, with the data at hand, the model’s objective function is difficult to optimize for parameter values in that case. Therefore, we resort to the more parsimonious specification.
Suppose that the propensity of having any scope other than 0 is zero in some initial period. Then, the level of propensity of having a given scope greater than 0 in a later period does represent not only the propensity of having that state but also the change in propensity relative to the initial period.
Notice that, following Lechner (2001), one may compute average treatment effects — say, and – based on the results reported in Tables 7 and 8, which can be compared to the scope-specific average treatment effects in Tables 5 and 6. In Table 9, we do so for the intensive margin of trade . Besides these scope-dyad-specific ATEs, we report scope-specific average treatment effects in the same table. The latter can be compared with the results on the scope-specific average treatment effects on the intensive margin of exports in Tables 5 and 6. The findings suggest that the average treatment effect of broadening the scope depends on the initial state of liberalization, which is not accounted for in the estimates in Tables 5 and 6.