cross-section regression analysis points to the potentially ambiguous causal relationship between trade facilitation and trade. We cannot exclude the possibility that greater bilateral trade will lead to higher values of trade facilitation measures rather than the postulated reverse relationship as estimated. Port efficiency, customs environment and service sector infrastructure may induce reforms that improve with a country’s import and export flows and the estimated coefficients for these variables would be biased upwards if this endogeneity is present.

A logical approach to the endogeneity problem is (1) to employ instrumental variables for the trade facilitation variables so the error term does not correlate with trade facilitation measures, and/or (2) to extend the trade facilitation data to a multiple year series and to use time-lagged measures of trade facilitation as explanatory variables. Good instruments should be sufficiently exogenous to decision makers or pre-determined, and should uniquely capture the characteristics of each trade facilitation indicator. Given the very large number of countries, finding good instruments is difficult, and data are lacking. We have already used such data exhaustively as inputs to form our trade facilitation indicators. The implication of the use of time-lagged measures was investigated in more depth in WMO using the smaller and more data rich APEC sample, yielding weak evidence that endogeneity was not too large an issue.

Further methodological issue arise from not having time-varying trade facilitation indicators. We cannot use a fixed effects model to isolate country specific effects that are correlated but not specific to trade facilitation. Whereas WMO used a fixed-effects model to account for the variation across exporting countries, here the use of country’s trade facilitation measures as an exporter will make it impossible to use fixed-effects for exporting countries. The time-varying gravity variables and the dummy variables will absorb variation other than that caused by differences in trade facilitation such that the trade facilitation variables appropriately capture the country specific effect associated only with trade facilitation. Although, we re-open the endogeneity box with this rationale.

In the end, our estimation can only be improved when panel data with a sufficiently long time series in trade facilitation variables become available, which would allow direct attention to endogeneity and application of fixed-effect modeling.

# **4.3 Regression Results**

The approach used here, which constructs a set of distinct trade facilitation indicators and deploys them in a gravity model of trade, is generally successful. Table 3 displays regression results. The first column includes the estimated coefficients and standard errors for the model under the specification in Equation (1). The second column includes those for the specification with aggregate FTA and language dummies (i.e. membership of any FTA, or any common language). The model was run using an ordinary least squares (OLS). The coefficients for the four trade facilitation measures are statistically significant and the estimated coefficients differ for the different trade facilitation indicators. From a policy perspective, these differences in estimated elasticities of trade flows with respect to trade facilitation indicator implies that different 11