The Impact of Trade Agreements: New Approach, new insights Swarnali Ahmed Hannan Strategy, Policy and Review Department International Monetary Fund Email: sahmed@imf.org The views expressed are those of the author and should not be attributed to the IMF, its Executive Board, or its management.

Motivation The Endogeneity Issue “…by and large [the studies] fail to address the endogeneity related to many of the policy variables…There are many examples where the countries that sign a trade enhancing agreement already trade a great deal together (NAFTA, EU).” Head and Mayer (2014, pp. 162) The Widely Different Impact of Trade Agreements—Baier, Yotov, and Zylkin (2016) Synthetic Control Method First study to employ SCM across a large number of trade agreements. Current trade slowdown witnessed in data What drives trade? What can policy do?

Synthetic Control Method

SCM at a Glance SCM is an econometric tool for comparative studies where the control unit is determined by a systematic data driven procedure. SCM creates a synthetic (artificial) control unit that is a weighted average or linear combination of the untreated units. The weights are chosen such that both the outcome variable and its observable covariates/determinants are matched with the treated unit before treatment. The evolution of the actual outcome of the treated unit post- treatment is then compared against the outcome of the synthetic unit, and the difference is interpreted as the treatment effect. Intuitively, the SCM basically uses a weighted average of the outcome of the control units to estimate the counterfactual outcome of the treated unit.

WHY SCM? By constructing a counterfactual, SCM can address the core endogeneity issue related to “countries that have trade agreements are natural trading partners and would have traded anyway”. Currently a very popular approach of comparative case studies in both micro and macro studies (e.g. impact of cigarette sales tax, economic impact of German reunification). Econometric benefits compared to traditional approaches: A number of methods have been used to deal with the problem of selection bias in observational data, including matching estimators, difference-in-differences regressions, etc. These techniques are useful but do not deal with unobservable country heterogeneity. At best, control for time-invariant country characteristics (Hosny, 2012). SCM can allow the effects of unobserved confounders to vary with time (Abadie et al., 2010). As reviewed, ex-post studies of impact of trade agreements suffer from an endogeneity problem that does not allow proper causal inference.

Data coverage Coverage: Balanced Sample 1983-1995  104 pairs Export – Import For some exercises also considered 1973- 2001216 pairs (All) 38124 observations

Trade Creation

Trade creation 80 countries had higher than synthetic, 24 countries lower than synthetic. EM-EM pair had higher synthetic than treated. The y-axis refers to ten years before and after trade agreement.

NAFTA The y-axis refers to ten years before and after trade agreement.

Each country in NAFTA benefitted from the agreement The y-axis refers to ten years before and after trade agreement.

Trade Agreements boost Trade! Export Growth of Average Treated Over Ten Years, Relative to Average Synthetic (cumulative, percentage points)

Export gains by size of exporting country

Export Gains by Depth of Trade Agreements Source of trade agreements’ depth: Left hand chart: Economic Integration Agreement Database (1950-2011), Bergstrand and Baier. Right hand chart: Dür, Andreas, Leonardo Baccini, and Manfred Elsig. 2014. “The Design of International Trade Agreements: Introducing a New Database.” Review of International Organizations 9(3), 353-375.

Placebo tests

Placebo (or Falsification) tests Concept: Assess whether the effect estimated by the synthetic control for a country pair affected by the trade agreement is large relative to the effect estimated for a country pair chosen at random. Process: Randomly select 10 treated units. Let A = exporter in the treated unit. Randomly select 5 country pairs showing the exports of A to a country not in the trade agreement (placebo). Run SCM on these selected country pairs. Compare treated relative to synthetic for treated unit versus the placebo unit. Example: Treated unit is CAD  USA (one of the 10 randomly chosen treated unit). Here, CAD is the exporter in the treated unit. Take CAD, and randomly choose 5 country pairs showing CAD exports to other partners not in trade agreement (placebos). Run SCM on each randomly chosen country pair. Compare treated relative synthetic of CAD USA with that of the 5 placebo units.

Results from placebo tests

trade diversion

Slight Import Diversion -What happens to the top importer outside trade agreement? -Apply SCM to the top importer that is outside the trade agreement. Import Growth of Average Treated Over Ten Years, Relative to Average Synthetic (cumulative, percentage points)

No export Diversion -What happens to the top export destination outside trade agreement? -Apply SCM to the top export destination that is outside the trade agreement. Export Growth of Average Treated Over Ten Years, Relative to Average Synthetic (cumulative, percentage points)

Concluding thoughts Trade agreements can generate substantial gains, particularly for emerging markets. The study falls under a small group of literature that shows trade agreements matter! Relevant for policy making in the current context of trade slowdown. The limitations of SCM approach should also be borne in mind while interpreting these results.

Background Slides Technical details

Assumptions (I) There are J+1 units (regions) in periods t=1,….,T. Region “one” is exposed to the intervention during periods T0+1 to T. is the outcome that would be observed for region i at time t in the absence of intervention. is the outcome that would be observed for region i at time t if region i is exposed to the intervention in periods T0+1 to T. is the effect of the intervention for unit i at time t for t>T0. AIM: estimate the effect of the intervention on the treated unit

Assumptions (II) Suppose is given by a factor model: is an unobserved (common) time-dependent factor, is a vector of observed covariates is a vector of unknown parameters is a vector of unknown common factors is a vector of unknown factor loadings are unobserved transitory shocks : heterogeneous responses to multiple unobserved factors. Basic idea: reweight the control group such that the synthetic control unit matched and (some) pre- treatment of the treated unit, . As a result, is automatically matched.

theory Let Each value of W represents a particular weighted average of control units. The value of the outcome variable for each synthetic control indexed by W is: Suppose that we can choose W* such that: Then an unbiased estimator of is

Implementation In practice, the vector is optimally chosen to minimize the following pseudo- distance: where represents a vector of pre- intervention characteristics of the treated region, while is a matrix containing the same pre-intervention variables of the control regions.

Covariates/Data Start off with the typical gravity equation used to model bilateral trade. xijt = GtMexit Mimjt φijt The dependent variables can be regarded as covariates of SCM approach. Distance between the bilateral pairs GDP of each country in the bilateral pair GDP per capita of each country in the bilateral pair Population of each country in the bilateral pair Bilateral Real Exchange Rate Remoteness of each country in the bilateral pair, proxy for multilateral trade resistance (MTR) term (remoteness due to physical distance and/or policy). Colonial history = 1 if pair ever in colonial relationship Col to = 1 if export from hegemon to colony Col from = 1 if export from colony to hegemon Contig = 1 for contiguity Comleg = 1 for common legal origins Comcur = 1 for common currency Common language = 1 for common official language Flow, lagged by 3years Source: Head, Mayer and Ries (2010), WDI, National Sources

Background Slides List of Country Pairs