Tari s versus Non-Tari Barriers Hiau Looi Kee and Cristina Neagu Development Research Group Trade, The World Bank Oct 2011 Work in Progress and Preliminary Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 1 / 22
Motivations With the dramatic decline in tari s through the successive rounds of multilateral negotiation and unilateral liberalizations, the main trade policy tools could be in the form of non-tari barriers (NTBs) Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 2 / 22
Examples on commonly used NTBs Origin of materials and parts Import licence fees Customs inspection fees Testing requirement Inspection requirement Direct consignment requirement Requirement to pass through speci ed port Service charges Labelling requirements Certi cation requirement Processing history Systems Approach Temporary geographic prohibition for SP Conformity assessments related to SPS Traceability requirement Certi cation required by the exporting Storage and transports conditions Microbiological criteria on the nal p Quarantine requirement Traceability information requirements Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 3 / 22
Million Dollar Question Are tari s and NTBs substitutes or complements? Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 4 / 22
Million Dollar Question Are tari s and NTBs substitutes or complements? Scant and mixed empirical evidence: Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 4 / 22
Million Dollar Question Are tari s and NTBs substitutes or complements? Scant and mixed empirical evidence: Complements Dean, Ludema, Signoret, Feinberg and Ferrantino (2009) based on cross country evidence of NTBs Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 4 / 22
Million Dollar Question Are tari s and NTBs substitutes or complements? Scant and mixed empirical evidence: Complements Dean, Ludema, Signoret, Feinberg and Ferrantino (2009) based on cross country evidence of NTBs Substitutes Kee, Nicita and Olarreaga (2009); Bown and Crowley (2009) based on time series evidence on the use of antidumping duties in India Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 4 / 22
What we plan to do Exploit a newly collected dataset of NTBs by the WB on a group of developing countries Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 5 / 22
What we plan to do Exploit a newly collected dataset of NTBs by the WB on a group of developing countries Estimate bilateral AVEs at detailed product level as rigorously as possible Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 5 / 22
What we plan to do Exploit a newly collected dataset of NTBs by the WB on a group of developing countries Estimate bilateral AVEs at detailed product level as rigorously as possible Study the factors that in uence the level of AVEs such as country sizes, stage of development, and bilateral distance Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 5 / 22
What we plan to do Exploit a newly collected dataset of NTBs by the WB on a group of developing countries Estimate bilateral AVEs at detailed product level as rigorously as possible Study the factors that in uence the level of AVEs such as country sizes, stage of development, and bilateral distance Relate the estimated AVEs to bilateral tari s Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 5 / 22
What we plan to do Exploit a newly collected dataset of NTBs by the WB on a group of developing countries Estimate bilateral AVEs at detailed product level as rigorously as possible Study the factors that in uence the level of AVEs such as country sizes, stage of development, and bilateral distance Relate the estimated AVEs to bilateral tari s Allow the degree of substitutability or complementarity between NTBs and tari s to be in uenced by the characteristics of the bilateral country pair Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 5 / 22
Theories Optimal level of protection Countries may use trade policies to in uence their terms of trade and transfer rents from their trading partners Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 6 / 22
Theories Optimal level of protection Countries may use trade policies to in uence their terms of trade and transfer rents from their trading partners Level of protection depends on tari s and the ad valorem equivalent (AVE) of NTBs t nij + AVE nij = T nij Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 6 / 22
Theories Optimal level of protection Countries may use trade policies to in uence their terms of trade and transfer rents from their trading partners Level of protection depends on tari s and the ad valorem equivalent (AVE) of NTBs t nij + AVE nij = T nij Lowering tari s due to multilateral negotiation or unilateral liberalizations implies that NTBs will be increased to keep protection at its optimal level ==> tari s and NTBs could be substitutes Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 6 / 22
Theories Protection for sale Trade policies can be in uenced by interests parties through lobbies and governments care about social welfare as well as campaign contributions Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 7 / 22
Theories Protection for sale Trade policies can be in uenced by interests parties through lobbies and governments care about social welfare as well as campaign contributions Level of protection depends on tari s and NTBs t nij + AVE nij = T nij which increases with lobby contributions ==> tari s and NTBs could be complements Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 7 / 22
Other factors that may in uence government s policy choices Tari s are more transparent while NTBs are harder to implements, subject to corruptions Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 8 / 22
Other factors that may in uence government s policy choices Tari s are more transparent while NTBs are harder to implements, subject to corruptions Some NTBs such as rules of origin requirement may a ect multiple industries while tari s are more targeted Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 8 / 22
Other factors that may in uence government s policy choices Tari s are more transparent while NTBs are harder to implements, subject to corruptions Some NTBs such as rules of origin requirement may a ect multiple industries while tari s are more targeted Tari s are under more scrutinized while NTBs are harder to monitored Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 8 / 22
Other factors that may in uence government s policy choices Tari s are more transparent while NTBs are harder to implements, subject to corruptions Some NTBs such as rules of origin requirement may a ect multiple industries while tari s are more targeted Tari s are under more scrutinized while NTBs are harder to monitored Unlike NTBs, tari s generate revenues which could be important for small developing countries Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 8 / 22
Data on NTBs Up to now the main data source is from TRAIN, which has not been updated since 2006 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 9 / 22
Data on NTBs Up to now the main data source is from TRAIN, which has not been updated since 2006 In 2010, World Bank collected detailed NTB data from about 20 developing countries Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 9 / 22
Data on NTBs Up to now the main data source is from TRAIN, which has not been updated since 2006 In 2010, World Bank collected detailed NTB data from about 20 developing countries Table 1 presents the share of products of each country that are subjected NTBs and positive tari s Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 9 / 22
Data on NTBs Up to now the main data source is from TRAIN, which has not been updated since 2006 In 2010, World Bank collected detailed NTB data from about 20 developing countries Table 1 presents the share of products of each country that are subjected NTBs and positive tari s At sample mean, 41% of products face NTBs while 68% of products face positive tari s Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 9 / 22
Data on NTBs Up to now the main data source is from TRAIN, which has not been updated since 2006 In 2010, World Bank collected detailed NTB data from about 20 developing countries Table 1 presents the share of products of each country that are subjected NTBs and positive tari s At sample mean, 41% of products face NTBs while 68% of products face positive tari s No clear correlation between the shares of products a ected by NTBs vs the shares of products a ected by positive tari s Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 9 / 22
Table: Percentage of Products A ected by NTBs and Positive Tari s Code NTB Tari s Code NTB Tari s ARG 87.2 80.8 NAM 49.8 40.7 BOL 6.1 85.5 PER 6.4 51.8 COL 7.1 88.6 PHL 20.3 91.4 ECU 43.3 73.8 PRY 25.1 88.1 EGY 91.7 77.2 SYR 86.9 86.4 IDN 42.0 73.9 TUN 22.2 68.5 JPN 37.5 28.2 TZA 5.5 59.1 KEN 83.1 56.0 UGA 94.0 59.1 LBN 12.4 55.5 URY 6.8 80.0 MEX 55.4 69.4 VEN 43.4 89.4 MUS 41.6 15.8 MEAN 41.3 67.6 Notes: Products are de ned by HS6-exporter pair; There are all together 1,234,555 products. Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 10 / 22
Figure: Share of products a ected by NTBs vs Tari s e( NTMshare X ).4.2 0.2.4.6 MUS JPN NAM UGA KEN LBN PER TZA MEX TUN ECU IDN EGY ARG SYR VEN PRY PHL URY BOLCOL.6.4.2 0.2 e( postariffshare X ) coef =.04772611, se =.34338015, t =.14 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 11 / 22
Estimating AVE of NTBs Modify the framework of Kee, Nicita and Olarreaga (2009) to estimate bilateral AVEs Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 12 / 22
Estimating AVE of NTBs Modify the framework of Kee, Nicita and Olarreaga (2009) to estimate bilateral AVEs Gravity model at product level: Country i 0 s import of product n from country j (M nij ), depends on GDP of the two countries (Y i, Y j ), distance in kilometers (D ij ), border (B ij ), tari s (t nij ) and the presence of NTB (NTB ni ) ln M nij = β n + β 1 ln Y i + β 2 ln Y j + β 3 ln D ij + β 4 B ij + β 5 ln (1 + t nij ) + β nij NTB ni Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 12 / 22
Estimating AVE of NTBs Modify the framework of Kee, Nicita and Olarreaga (2009) to estimate bilateral AVEs Gravity model at product level: Country i 0 s import of product n from country j (M nij ), depends on GDP of the two countries (Y i, Y j ), distance in kilometers (D ij ), border (B ij ), tari s (t nij ) and the presence of NTB (NTB ni ) ln M nij = β n + β 1 ln Y i + β 2 ln Y j + β 3 ln D ij + β 4 B ij + β 5 ln (1 + t nij ) + β nij NTB ni While NTBs are importer-product speci c, the e ect of NTBs on bilateral trade may depend on the characteristics of the importer and the exporter, proxied by the total imports of product n in country i from the rest of the world (M nirow ) and the total exports of product n from country j to the rest of the world (E nrowj ) β nij = exp (γ n + γ 1 ln M nirow + γ 2 ln E nrowj ) Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 12 / 22
By construction β nij varies by product, importer and exporter Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 13 / 22
By construction β nij varies by product, importer and exporter Negativity constraint on β nij so that NTB always decrease trade Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 13 / 22
By construction β nij varies by product, importer and exporter Negativity constraint on β nij so that NTB always decrease trade Negativity constraint on β 5 when necessary Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 13 / 22
By construction β nij varies by product, importer and exporter Negativity constraint on β nij so that NTB always decrease trade Negativity constraint on β 5 when necessary Estimated with non-linear LS Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 13 / 22
By construction β nij varies by product, importer and exporter Negativity constraint on β nij so that NTB always decrease trade Negativity constraint on β 5 when necessary Estimated with non-linear LS The resulting AVE estimate is AVE nij = ( exp(βnij ) 1 ε nij, if NTB = 1 0, if NTB = 0, where ε nij is the country i 0 s import demand elasticity of product n from country j (from Kee, Neagu and Nicita, ReStat, forthcoming) Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 13 / 22
By construction β nij varies by product, importer and exporter Negativity constraint on β nij so that NTB always decrease trade Negativity constraint on β 5 when necessary Estimated with non-linear LS The resulting AVE estimate is AVE nij = ( exp(βnij ) 1 ε nij, if NTB = 1 0, if NTB = 0, where ε nij is the country i 0 s import demand elasticity of product n from country j (from Kee, Neagu and Nicita, ReStat, forthcoming) If M nij = 0, then use ε ni from Kee, Nicita and Olarreaga (ReStat, 2008) Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 13 / 22
Caveats Zero s in trade: Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
Caveats Zero s in trade: we add 1 to all the trade variables before taking logs ==> not ideal Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
Caveats Zero s in trade: we add 1 to all the trade variables before taking logs ==> not ideal tried Poisson estimations but cannot introduce negativity constraints (nearly half of the estimated β nij s are positive) Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
Caveats Zero s in trade: we add 1 to all the trade variables before taking logs ==> not ideal tried Poisson estimations but cannot introduce negativity constraints (nearly half of the estimated β nij s are positive) tried non-linear LS in level but very di cult to reach convergence M nij = Y β 1 i Y β 2 j D β 3 β5 ij exp β 4 B ij 1 + tnij exp β nij NTB ni Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
Caveats Zero s in trade: we add 1 to all the trade variables before taking logs ==> not ideal tried Poisson estimations but cannot introduce negativity constraints (nearly half of the estimated β nij s are positive) tried non-linear LS in level but very di cult to reach convergence M nij = Y β 1 i Y β 2 j D β 3 β5 ij exp β 4 B ij 1 + tnij exp β nij NTB ni Endogeneity of NTB: Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
Caveats Zero s in trade: we add 1 to all the trade variables before taking logs ==> not ideal tried Poisson estimations but cannot introduce negativity constraints (nearly half of the estimated β nij s are positive) tried non-linear LS in level but very di cult to reach convergence M nij = Y β 1 i Y β 2 j D β 3 β5 ij exp β 4 B ij 1 + tnij exp β nij NTB ni Endogeneity of NTB: not as bad as it seems since the dependent variables and regression errors vary by product-importer-exporter, while NTBs only vary by product-importer Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
Caveats Zero s in trade: we add 1 to all the trade variables before taking logs ==> not ideal tried Poisson estimations but cannot introduce negativity constraints (nearly half of the estimated β nij s are positive) tried non-linear LS in level but very di cult to reach convergence M nij = Y β 1 i Y β 2 j D β 3 β5 ij exp β 4 B ij 1 + tnij exp β nij NTB ni Endogeneity of NTB: not as bad as it seems since the dependent variables and regression errors vary by product-importer-exporter, while NTBs only vary by product-importer could instrument for NTBs using a Heckman s selection equation Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 14 / 22
AVE Estimates Nearly 26 millions AVEs of NTBs are estimated (21 importers 245 exporters 5039 HS 6) Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 15 / 22
AVE Estimates Nearly 26 millions AVEs of NTBs are estimated (21 importers 245 exporters 5039 HS 6) The average AVE and tari is 11% and 10% Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 15 / 22
AVE Estimates Nearly 26 millions AVEs of NTBs are estimated (21 importers 245 exporters 5039 HS 6) The average AVE and tari is 11% and 10% The average AVE when AVE is positive is 28%; it is 15% for tari Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 15 / 22
AVE Estimates Nearly 26 millions AVEs of NTBs are estimated (21 importers 245 exporters 5039 HS 6) The average AVE and tari is 11% and 10% The average AVE when AVE is positive is 28%; it is 15% for tari Only about 870 thousands observations have positive trade Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 15 / 22
AVE Estimates Nearly 26 millions AVEs of NTBs are estimated (21 importers 245 exporters 5039 HS 6) The average AVE and tari is 11% and 10% The average AVE when AVE is positive is 28%; it is 15% for tari Only about 870 thousands observations have positive trade Among the observations with positive trade, the average AVE and tari is 6.5% and 9.4% Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 15 / 22
AVE Estimates Nearly 26 millions AVEs of NTBs are estimated (21 importers 245 exporters 5039 HS 6) The average AVE and tari is 11% and 10% The average AVE when AVE is positive is 28%; it is 15% for tari Only about 870 thousands observations have positive trade Among the observations with positive trade, the average AVE and tari is 6.5% and 9.4% If only focus on observations with NTBs, the average AVE is 15%; the average tari is 14% among observation with positive tari s Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 15 / 22
AVE Estimates Assessment Despite the large number of AVE being estimated, the results look reasonable Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 16 / 22
AVE Estimates Assessment Despite the large number of AVE being estimated, the results look reasonable AVEs of NTBs look comparable with tari s in terms of size on average Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 16 / 22
AVE Estimates Assessment Despite the large number of AVE being estimated, the results look reasonable AVEs of NTBs look comparable with tari s in terms of size on average When NTBs are binding, AVE is almost twice as large as tari Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 16 / 22
AVE Estimates Assessment Despite the large number of AVE being estimated, the results look reasonable AVEs of NTBs look comparable with tari s in terms of size on average When NTBs are binding, AVE is almost twice as large as tari Both AVE and tari are lower among trading partners Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 16 / 22
AVE Estimates Assessment Despite the large number of AVE being estimated, the results look reasonable AVEs of NTBs look comparable with tari s in terms of size on average When NTBs are binding, AVE is almost twice as large as tari Both AVE and tari are lower among trading partners AVE is lower than tari among trading partners Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 16 / 22
Figure: Distribtion of AVEs for Traded Products with NTBs Kernel density estimate Density 0 5 10 15 0.2.4.6.8 1 ave_core_nl kernel = epanechnikov, bandwidth = 0.0072 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 17 / 22
Table: Average Estimated AVEs of NTBs and Tari s Code AVEs of NTBs Tari s All > 0 Traded All > 0 Traded ARG 24.9 30.3 12 12.0 13.8 11.1 BOL 1.6 25.6 0.5 8.0 8.7 6.2 COL 2.0 28.2 0.6 11.9 12.5 10.7 ECU 5.9 14.1 3.1 11.0 14.0 9.7 EGY 23.0 26.8 12.8 14.5 17.5 12.7 IDN 11.2 27.4 3.3 6.7 8.5 5.6 JPN 18.7 49.4 6.9 4.0 13.4 4 KEN 34.4 44.3 13 11.8 19.6 12.1 LBN 2.0 17.0 1.6 6.4 10.7 8.3 MEX 16.5 31.1 5.6 10.5 14.1 6.1 MUS 5.3 13.1 3.5 3.4 20.0 5.7 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 18 / 22
Table: Average Estimated AVEs of NTBs and Tari s Code AVEs of NTBs Tari s All > 0 Traded All > 0 Traded NAM 8.1 17.8 7.6 7.4 17.0 6.3 PER 2.3 34.3 1.1 6.0 10.7 6.7 PHL 4.4 25.5 2 6.2 6.4 6 PRY 14.9 61.6 2.1 11.4 12.0 9.4 SYR 19.8 22.7 17.8 13.2 14.3 6.4 TUN 6.2 28.3 2.6 21.5 27.0 21.9 TZA 1.2 21.9 0.4 12.5 19.8 13.1 UGA 16.1 18.2 34.6 12.0 18.9 12.2 URY 2.6 36.7 0.6 10.3 12.0 9.6 VEN 9.2 22.6 4.6 12.8 13.3 12.3 MEAN 11.0 28.4 6.5 10.2 14.5 9.4 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 19 / 22
Figure: Partial Correlations by Importers at Sample Mean e( ave_core_nl X ).1 0.1.2 UGA MUS JPN PER SYR KEN EGY NAM MEX URY VEN LBN ECU PRY TUN IDN COL PHL BOL TZA ARG.05 0.05.1 e( tariff X ) coef =.07952134, se =.54985548, t =.14 e( ave_core_nl X ).1 0.1.2 MUS JPN KEN PER SYR EGY MEX VEN URY NAM ECU PRY LBN TUN IDN COL PHL BOL TZA UGA ARG 3 2 1 0 1 e( lgdpi X ) coef =.01474484, se =.01579062, t =.93 e( ave_core_nl X ).1 0.1.2.3 UGA ARG KEN EGY SYR PER MEX JPN IDN PHL PRY ECU NAM MUS VENURY BOL COL TZA TUN LBN 2 1 0 1 2 e( lgdppci X ) coef =.03308281, se =.02140866, t = 1.55 e( ave_core_nl X ).1 0.1.2 MUS KEN JPN EGY SYR PER MEX URY NAM VEN LBN ECU TUN PRY IDN COL PHL BOL TZA UGA ARG.4.2 0.2.4 e( lgdpj X ) coef =.00840381, se =.10611599, t =.08 e( ave_core_nl X ).1 0.1.2 SYR UGA ARG JPN VEN NAM EGYPER KEN URY MEX MUS PRY ECU COL BOL TUNLBN PHL IDN TZA.2.1 0.1.2 e( lgdppcj X ) coef =.19126077, se =.22355714, t =.86 e( ave_core_nl X ).1 0.1.2 SYR UGA ARG EGY PER JPN KEN NAM MUS LBN VEN MEX TUN URY ECU PRY PHL IDN BOL COL TZA.6.4.2 0.2.4 e( lkm1 X ) coef =.0553735, se =.06877168, t =.81 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 20 / 22
Figure: Dependent variable: ln 1 + AVE nij (1) (2) (3) (4) (5) (6) (7) (8) Tariffs 0.006*** 0.020*** 0.024*** 0.181*** 0.050 0.039*** 0.101 0.060 (0.002) (0.002) (0.002) (0.030) (0.042) (0.004) (0.074) (0.085) Tariff*GDPi 0.004*** 0.016*** 0.003* 0.006*** (0.001) (0.002) (0.002) (0.002) Tariff*GDPj 0.014*** 0.003*** 0.007*** 0.006** (0.001) (0.001) (0.002) (0.003) Tariff*GDPPCi 0.014*** 0.060*** 0.009*** 0.004 (0.003) (0.003) (0.003) (0.004) Tariff*GDPPCj 0.000 0.002* 0.002 0.005* (0.000) (0.001) (0.003) (0.003) Tariff*Distanceij 0.005*** 0.001 0.001 0.000 (0.001) (0.001) (0.005) (0.004) GDPi 0.008*** (0.000) GDPj 0.002*** (0.000) GDPPCi 0.012*** (0.000) GDPPCj 0.000*** (0.000) Distanceij 0.003*** (0.000) Elasticity 0.000*** 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) Product FE Yes Yes No Yes No No Importer FE Yes No No No No No Exporter FE Yes No No No No No Importer Product FE No No Yes Yes Yes No No Importer ExporterFE No No No No No Yes Yes Tariffs at sample mean 0.025*** 0.039*** (0.002) (0.003) Observations 850640 826563 850640 826563 826563 850640 826563 826563 Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 21 / 22
Preliminary Conclusions Tari s and NTBs could be substitutes within importer-hs6 products, comparing across exporters Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 22 / 22
Preliminary Conclusions Tari s and NTBs could be substitutes within importer-hs6 products, comparing across exporters Preferential tari s often come with rules of origin requirements Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 22 / 22
Preliminary Conclusions Tari s and NTBs could be substitutes within importer-hs6 products, comparing across exporters Preferential tari s often come with rules of origin requirements Tari s and NTBs could be complements within importer-exporter pair, comparing across products Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 22 / 22
Preliminary Conclusions Tari s and NTBs could be substitutes within importer-hs6 products, comparing across exporters Preferential tari s often come with rules of origin requirements Tari s and NTBs could be complements within importer-exporter pair, comparing across products Products that have low tari barriers often have lower NTBs Kee and Neagu (World Bank) Tari s versus Non-Tari Barriers 10/11 22 / 22