Discussion of The Gravity of Experience by Dutt, Santacreu and Traca Andrew K. Rose ABFER, CEPR, NBER and Berkeley-Haas

Ludicrously Provocative Essential Result Empirical highlighted in abstract: “… an additional year of experience at the country-pair level reduces trade costs by 2.0% and increases bilateral exports by 8%.” First part a model-driven interpretation, so relevant part is: “… an additional year of experience … increases bilateral exports by 8%.” Who can believe that each additional year of interaction raises international commerce by such an enormous percent? Accordingly, this discussion is a “search and destroy” mission 2

First Line of Attack: Empirical Setup Result depend on some implausible empirics? Problematic because plain-vanilla setup: 1.Dyadic (country-pair) exports modelled in standard gravity model 2.Examine exports (also extensive and intensive margins for trade) 3.Conventional regressors 4.Country x year fixed effects for both exports and importers included 5.Dyadic fixed effects added for sensitivity analysis (!) 6.Large number of observations implies many countries, years 7.Two different data sets All apparent from (self-contained) Table 1 3

Second Wave: Functional Form Experience in paper measured as “number of years for which the country-pair had strictly positive trade” Seems crude! Aggregate: do Paramount’s exports benefit from Universal’s foreign sales? More importantly: hard to believe functional form is linear in years Surely years diminish in importance Does Canada’s 150 th year of exporting to US matter as much as South Somalia’s first? Easy to test in principle, given the data Thanks to Pushan for quickly providing data and sample program (Wouldn’t be necessary to request if adhere to “replication standard”) 4

What does the Data Set Really Reveal? Stick to Dutt et al setup, using their data set Standard export gravity model, country x year dummies Just mess with measure of experience 5

Sensitivity Analysis (Almost) replicate their results (admittedly their data, program) 6 Coefficient (robust std error) Default (log exports).060 (.001) Regressand: Extensive Margin.030 (.001) Regressand: Intensive Margin.030 (.001) Experience: Log time.869 (.018) Experience decays (slowly).070 (.001) Log experience decays (slowly).887 (.018)

Splines for Functional Form Robustness Coefficient (robust std error) Experience: 1-2 years.056 (.043) Experience: 3-5 years.168 (.048) Experience: 6-10 years.592 (.053) Experience: years1.12 (.057) Experience: years1.66 (.062) Experience: >30 years2.46 (.067) 7

More Checks Coefficient (robust std error) Add Lag Dependent Variable.0143 (.0004) → lr effect of.051 Data before (.001) Data after (.001) Data after (.001) 8

Sending in the Reserves: The Footnote Footnote 14 (p9), highlights added: “We restrict our sample to since we decompose total trade into an extensive and intensive margin based on COMTRADE HS-6 data.” Aha! Can’t test importance of sample restriction with Dutt et al data set But possible with an independently-constructed but similar data set (ongoing work with Reuven Glick, FRBSF) Slightly different gravity model Much larger span of time (back to 1948, forward to 2013) 9

How Sensitive are Results to Sample? Coefficient (robust std error) Dutt et al default.060 (.001) My replication, Dutt et al sample.071 (.001) My replication, larger sample.078 (.001) Before (.007) Before (.002) Before (.001) After (.001) After (.001) After (.001) 10

A Flanking Maneuver Can test impact of experience without any assumptions about functional form Just add experience dummies year by year (possible with many df) Can do this with my data set Then check for disintegration with larger sample 11

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Surrender! This is a very powerful and strong result! Stunning impact and importance. The way forward: more verification 1.Others (make data accessible) 2.Micro data A tip of the hat! 14