section. Our preferred results are in column 7, where the STRI, DTRI, and WGI variables all have statistically significant coefficients. While their signs indicate that they all have the expected impact on bilateral trade, it is important to be able to compare them in terms of quantitative importance. To do that, we convert them to AVEs for each country pair (since the STRI varies bilaterally due to EU preferential data), then take the simple average at the country level.

Results are in Table 5. The first three columns report AVEs. The fourth shows the total effect of measures related to TFiS (the sum of columns two and three) as a percentage of the AVE related to policy restrictiveness (column 1). Focusing on the last column, we can see that for all countries in the sample, the trade cost impact of measures related to TFiS is much higher than that due to straightforward policy restrictiveness. The difference is at least 1.5 times and is frequently much higher. The evidence therefore strongly suggests that policies that do not affect traditional market access, but which nonetheless increase transparency and improve regulatory governance in a non-discriminatory fashion, would have great potential to reduce trade costs in services and thereby boost bilateral trade. Figure 3 shows that in particular many middle-income countries would benefit from improving institutional governance as proxied by the WGI given that their AVE in this area is comparatively big.

The final part of the empirical exercise consists of re-estimating the model from Table 4 column 7 using sectoral data. This exercise is necessarily approximate, as the sectoral definitions used to construct the STRI are different from the definitions used to record and harmonize the trade and production data. We can therefore only identify four sectors where there is a close enough correspondence to make the analysis meaningful: finance (using the commercial banking STRI), computer services (using the computer services STRI), transport (using the maritime transport STRI), and telecom (using the telecom STRI).

Results are in Table 6. There is substantial variation across sectors, including some counterintuitive coefficients that are statistically significant. The STRI, as an indicator of market access restrictions, has a particularly large effect in financial services and telecom, but no significant effect in the other two sectors. In terms of the TFiS indicators, regulatory heterogeneity has a counterintuitive coefficient in all regressions, but the DTRI, regulatory governance, and the WGI indicator all have statistically significant coefficients with the expected signs in three of the four regressions, although the particular sectors vary. Doing Business has counterintuitive coefficients in three of the four cases, although it gives the expected results in transport.

The net result of the regressions in Table 6 is that there is substantial evidence of cross-sectoral heterogeneity in the impact of TFiS measures relative to market access measures captured by the STRI. In general, zooming down to the sectoral level seems to be associated with stronger impacts across the board, but there is substantial variation in signs, significance, and magnitudes across sectors. As always with sectoral services trade data, which are often poorly measured, it is difficult to draw strong conclusions. We therefore summarize the results by emphasizing that the impact of TFiS measures could differ substantially across sectors, so from a policy perspective it will be important to identify interventions that have the largest effects not only at the aggregate level, but on particular sectors.

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