Empirical strategy

I¯_ijt^g + ¯I_ijt^b

in expression (5.5), and assuming a constant elas-ticity of the c.d.f. G(c), we can write the volume of exports from i toj in the following way

lnXijt=α₀lnβ+α₁lnV +φ(1−E[qijt]) + lnYjt−lnX

k6=i

GDP_k+τlnDij+uijt, (5.13) where u_ijt is an error term whose properties have to be discussed below. Our simple theoretical model would suggest that the termα₀lnβ+α1lnV is just a constantα.While we do not have any reason to suspect that the exporter’s ‘fair’ bargaining powerβ should vary across importers or time, the same is unlikely to hold forV,the total joint available from the transaction. The surplus is likely to depend also on the exporter’s market size, Yit,as well as on its per capita income of both countries. Moreover, the price competitiveness should

also matter. Turning to trade costs, the theoretical model only talks about geographical distance, which is of course time-invariant. In reality, the evolution of technology and the stance of trade policy should be taken into account as well.

B&B specification

These considerations motivate us to include a non-parametric time trend and a host of controls that are meant to capture the stance of commercial policy and the quality of in-stitutions. We include measures of the population size for both countries, which accounts jointly with the GDP terms for per capita income. Finally, to account for price com-petitiveness, we follow Baier and Bergstrand (2001) and include price level data for both countries.⁹ This leaves us with a first specification, that–for mnemonic reasons is called B&B specification:

lnX_ijt=α+φRBB_ijt+τlnD_ij +γ₁lnY_jt+γ₂lnY_it+γ₃P OP_it+γ₄P OP_jt

+π₁Pit+π₂Pjt+ξ₁POL⁰_it+ξ₂POL⁰_jt+ξ₃POL⁰_ijt+θt+υijt, (5.14) where we use the variable RBB_ijt to proxy for 1−E[qijt] ; see below for details. P OP_it refers to the population of countryi, Pit is its price level and POLis a collection of policy or institutional variables, which may be dyadic in nature, e.g., the incidence of regional trade areas, or apply to the exporter or importer separately, e.g., the quality of institutions.

The coefficient of interest will be φ.

FE specification

We estimate a fixed effects model that we term FE specification. Here, we replace all exporter- or importer-specific price and policy variables by a comprehensive set of fixed effects. This regression is somewhat closer to the standard specification used in gravity equations of bilateral trade; see Feenstra (2004) for a survey or Combes et. al. (2005) for

9However, in contrast to our work, Baier and Bergstrand work with first differences, since they are ultimately interested in a decomposition of world tradegrowth.

a recent application:

lnXijt=α+φRBBijt+τlnDij+γ₁lnYjt+γ₂lnYit+γ₃P OPit+γ₄P OPjt+νi+νj+θt+uijt, (5.15) where νi and νj are comprehensive sets of exporter and importer dummies, respectively.

This specification has the advantage of lower data requirements¹⁰ so that the sample of countries and years covered is larger. However, the disadvantage is that the time dimension of the price- and policy variables is usually relegated into time fixed effects, which are identical for the same sample. Note that the dependent variable in both models is the natural logarithm of bilateral exports. This implies that country pairs with zero trade drop from the sample. One could argue that either a semi-log specification, or a Tobit-type regression methodology should therefore be preferred to our model, see Felbermayr and Kohler (2004) for a discussion. However, we follow the established literature and drop zero-trade observations.

Purging and IV Strategy

We have argued that the RBB data contains useful information that can be brought in as a proxy for µ_ijt. However, a number of problems arise. First, and most importantly, there is a strong endogeneity problem. Successful transactions will produce good news, and this will in turn drive up the RBB index. In order to sort out this simultaneity problem, exploiting data with a highly disaggregated time dimension would help. While the RBB data is available on a daily basis, trade data is not.¹¹

A second problem arises due to the fact that single transactions (of whatever type they may be) often are not independent from each other: one event triggers the next so that it is difficult to exactly identify what an event is. Most event studies share this feature. We are not particularly worried by this fact, since the reappearance of the same event may indicate that this event is more important relative to the others and should therefore obtain a higher weight. However, if a non-cooperative event triggers a cooperative event later in the same year, we may have a problem, since the measureµ_ijt is reduced by these events, while it is

10In particular, price data is difficult to obtain for a large sample of countries from 1990-2004.

11In recent years, monthly and quarterly data are available also for bilateral trade flows.

more natural to argue that the net effect of these effects should cancel out.

For these reasons, we purge the cooperation and conflict data before using it in the regression models (5.14) and (5.15). More precisely, we adjust the cooperative (non-cooperative) RBB measure so that it is orthogonal to the most important drivers of bi-lateral trade, namely GDP levels and geographical distance, and an aggregate measure of non-cooperative (cooperative) behavior. We include time dummies and run the following regressions using OLS (correcting the variance-covariance matrix for within-group correla-tion) and a zero-inflated Poisson regression when applicable:

I_ijt^g = α^g_ij +β^g₁lnYjt+β^g₂lnYit+δ^glnDij +ξI_ijt^b +υ^g_t +u^g_ijt, (5.16) I_ijt^b = α^b_ij +β^b₁lnY_jt+β^b₂lnY_it+δ^blnD_ij +ξI_ijt^g +υ^b_t+u^b_ijt. (5.17) Table 5.9 in the Appendix provides the results of some of these regressions, with either the aggregate cooperation measure or the underlying event forms as dependent variables.

The Appendix also gives summary statistics for the dependent and independent variables used in our regressions. Note that whenever we purge single event forms, we use a zero-inflated Poisson model, since we deal with count data that features a large amount of zero entries. However, once the different events are aggregated, the data becomes continuous.

Hence, we use OLS. The key insight from this table is that larger countries (as measured by GDP) have a larger amount of information, both cooperative and non-cooperative, and that geographical remoteness reduces our RBB measure. In the following, we use the residuals from the above equations as ‘purged’ measures of cooperative and non-cooperative behavior.

Finally, we recognize that even the purged measures do not appropriately account for simultaneity bias. For this reason, when we run our regressions (5.14) and (5.15), we instrument the purged cooperation indices by their lags. We find that two lags are enough to consistently achieve first-stage F-statistics that satisfy Stock and Watson’s rule of thumb (F-statistic above 10). We have experimented with other lag structures and have also tried differences; these variations do not seem to make much of a difference. Since we use constructed variables from our ‘purging’ equations (5.16) and (5.17) in our final regressions,

we have to account for sampling error stemming from these auxiliary regressions. Hence, we bootstrap the standard errors in our IV regressions. In order to be on the safe side, we choose 200 replications. Note that we have chosen δ= 1 in (5.8).