trade and global value chains in the eu: a dynamic ......following koopman et al. (2011), it...

Aims GVCs & Trade in value added GVCs & NA measures Factory Europe Empirical application Conclusions & Further Steps

Trade and Global Value Chains in the EU:A Dynamic Augmented Gravity Model

Pierluigi Montalbano1 Silvia Nenci2 Lavinia Rotili3

1Sapienza University of Rome (Italy) & University of Sussex (UK)[email protected]

2University of Roma Tre (Italy)[email protected]

3Sapienza University of Rome (Italy)[email protected]

ITSG 2015University of Roma Tre10-11 December 2015

P. Montalbano, S. Nenci, L. Rotili ITSG Rome 2015 1 / 22

Outline

1 Aims

2 GVCs & Trade in value added

3 GVCs & NA measures

4 Factory Europe

5 Empirical application

6 Conclusions & Further Steps

Aims

Objective

Assessing the empirical importance of product fragmentation/GVCs andcountries’ interdependence on bilateral trade flows

Focus

The so-called “Factory Europe” - an “hub and spoke” system centeredon Germany and characterized by strong supply chain connectionsbetween countries (Baldwin & Lopez-Gonzales 2014)

Added value

We present augmented specifications of (static and dynamic) gravityeq. w/ fragmentation/GVC and NA measures

Main Conclusion

We demonstrate empirically that product fragmentation/GVCs outweigh(in magnitude and statistical significance) the “euro effect”

Aims

Objective

Focus

Added value

Main Conclusion

Aims

Objective

Focus

Added value

Main Conclusion

Aims

Objective

Focus

Added value

Main Conclusion

Aims

Objective

Focus

Added value

Main Conclusion

Why focusing on value added trade statistics?

Nowadays countries have developed comparativeadvantages in specific parts of the value chains

Standard Gross trade statistics are not able to reveal the“foreign value added” of exports producing biasedassessments of RCA (relevant for policymaking)

Value added trade statistics provide information about theactual value added in the production of exported goods,challenging conventional wisdoms

As argued by Baldwin & Taglioni (2014) when trade inparts and components and triadic flows are important,bilateral gravity equations need improvements;

The Data set

World I/O Database (WIOD) - (Timmer et al., 2012)It contains I/O tables for the global economy withtransactions from origin to destination, by distinguishingfinal and intermediate useData are disaggregated into 41 ctrs (EU members incl.)and 35 sectors for the period 1995-2011All data collected from national sources are converted intoUS dollars

The Data set

Koopman et al. (2014)’sGross Export Decomposition

*Source: Koopman al. (2014)

GVCs indicators

The GVC Participation index

Following Koopman et al. (2011), it adds FVA and IVA

The GVC participation index for country i and industry k is:

GVCparik =FVAik

Eik+ IVAikEik

where E stands for gross exports, FVA is foreign VA embodied in countries’exports and IVA is the domestic VA embodied in third countries’ gross exports

The GVC Position index

Following Koopman et al. (2011), it measures the level of involvement of acountry in vertically fragmented production (i.e., the extent to which the country’sspecialization is upstream or downstream in the GVCs)

The GVC position index for country i and industry k is:

GVCposik =IVAikEik

/FVAik

Eik

Why NA?

Background

NA enables to represent the network of trade flows by giving emphasisto the structure of the network itself

NA takes into account the “effect of the others” in the bilateral relations(i.e., the set of all possible trade relations that affect a dyadic flow)

Identification Strategy

to include centrality measures in the empirical analysis to take accountof the "third country effect" (De Benedictis & Tajoli, 2011; De Bruyne etal., 2013)

We can identify factors that vary only in the exporter and importerdimensions (i.e., mass variables, gvc indices, etc.)

We use alternative NA meaures (i.e., the out-strength/in-strengthdegree centrality and the weighted out-eigenvector/in-eigenvectorcentrality) provided by De Benedictis et al., 2014

Why NA?

Background

Why NA?

Background

Why NA?

Background

Why NA?

Background

Why NA?

Background

Centrality measures

Centrality measures can be classified into four main groups(Jackson, 2010):

1 Degree centrality, that measures how a node is connectedto others (with strength centrality as a weighted version);

2 Closeness centrality, that shows how easily a node can bereached by other nodes;

3 Betweenness centrality, that describs how important anode is in terms of connecting other nodes;

4 Eigenvector centrality or Bonacich centrality, that associatenode’s centrality to the node neighbors’ characteristics,directly referring to how important, central, influential ortightly clustered a node’s neighbors are.

Centrality measures applied in the analysis -1

Local centrality measure

As an example of local centrality measure, we apply the strengthcentrality that is computed by aggregating the weights of the arcsconnected to the node and normalizing by the total number of countries.

The out-strength and in-strength centrality measures are computed asfollows:

CSout =

∑Nj 6=i Tij

(N − 1) CSin =∑N

j 6=i Tji(N − 1) . (1)

where N is the total number of countries in the network, i is the exportingcountries, j is the importing countries, T is the trade flow (export or import flows),and N − 1 is a normalized factor.The strength centralities measure the ’average’ flow of imports and the ’average’flow of exports.

Centrality measures applied in the analysis -2

Global centrality measure

As an example of global centrality measure we apply the eigenvectorcentrality. A node’s eigenvector centrality is determined by theeigenvector centrality of its neighbors. In the weighted version, thesystem of equations can be written in matrix form as:

(I −W)−→CE = 0, (2)

where I is a n × n identity matrix,W is the trade weighted adjacency matrix and−→CE is the n × 1 vector of countries’ eigenvector centralities.Using the Perron-Frobenius theorem, we can consider the entries of the relevant(principal) eigenvector as a measure of country centralities.

In general, countries with a high value of eigenvector centrality are the oneswhich are connected to many other countries which are, in turn, connected tomany others.

Also in this case, we apply both the out and in-eigenvector centrality measures.

The EU FVA Network (main buyers)

*Authors’ elaboration from WIODOnly flows higher than 10th percentile are represented

Foreign value added (by country)

*Authors’ elaboration from WIOD

Participation and Position (2010), EU countries

Authors’ elaboration

Relative positions of EU trade partners forselected EZ members (2009)

Germany Austria Belgium Finland France Italy Greece Portugal Spain IrelandGRC 1.80 GBR 2.31 GBR 2.76 MLT 3.46 ITA 1.42 NLD 1.02 ESP 1.56 ESP 2.94 LVA 2.28 GBR 4.31GBR 1.74 GRC 1.91 BGR 2.65 GBR 1.98 ESP 1.20 GBR 0.95 NLD 1.38 ITA 2.76 LTU 1.56 NLD 4.05ESP 1.58 ESP 1.73 LVA 2.42 PRT 1.84 GBR 1.15 ITA 1.28 ROM 2.02 ROM 1.48 GRC 3.43ITA 1.41 ITA 1.58 IRL 2.23 EST 1.69 DEU 1.01 FIN 0.84 FRA 1.18 GBR 1.89 EST 1.45 DNK 2.50IRL 1.35 FRA 1.29 ESP 1.99 ESP 1.68 ROM 0.98 GRC 0.78 FIN 1.09 NLD 1.73 ITA 1.30 LVA 2.23ROM 1.23 PRT 1.27 ROM 1.78 DEU 1.51 ESP 0.77 GBR 1.05 LTU 1.69 BGR 1.03 ESP 1.94POL 1.11 CZE 1.18 NLD 1.58 LVA 1.28 FIN 0.91 BGR 0.72 PRT 0.97 DEU 1.36 MLT 0.98 AUT 1.56FRA 0.99 DEU 1.14 GRC 1.50 ITA 1.19 GRC 0.85 ROM 0.71 FRA 1.27 SWE 1.51

NLD 1.14 FRA 1.46 ROM 1.18 NLD 0.81 DEU 0.71 LVA 0.83 IRL 1.12 GBR 0.88 FRA 1.48NLD 0.88 BEL 1.14 ITA 1.46 IRL 1.18 PRT 0.79 FRA 0.71 CZE 0.71 LVA 1.09 FRA 0.83 LTU 1.47SVN 0.88 SVK 1.14 LTU 1.21 FRA 1.09 AUT 0.78 IRL 0.69 ROM 0.68 EST 1.08 HUN 0.72 ITA 1.45AUT 0.88 SWE 1.09 PRT 1.17 BGR 1.06 POL 0.77 BEL 0.69 POL 0.68 GRC 1.03 POL 0.69 POL 1.44BEL 0.86 POL 1.04 DEU 1.17 AUT 1.02 SWE 0.76 SWE 0.66 BEL 0.67 HUN 1.02 NLD 0.65 EST 1.37CZE 0.85 FIN 0.98 EST 1.13 SWE 0.96 DNK 0.71 AUT 0.63 EST 0.64 POL 1.00 GRC 0.64LVA 0.80 FIN 1.04 BEL 0.96 LTU 0.70 LVA 0.63 DEU 0.55 SVN 0.96 DEU 0.63 HUN 0.90BGR 0.75 SVN 0.80 MLT 1.03 LTU 0.96 LVA 0.69 POL 0.50 HUN 0.53 CZE 0.95 FIN 0.59 PRT 0.89SWE 0.75 LTU 0.78 CZE 1.00 CZE 0.95 BEL 0.69 DNK 0.47 AUT 0.52 AUT 0.58 FIN 0.85PRT 0.73 ROM 0.77 SWE 0.98 IRL 0.68 LTU 0.46 LUX 0.50 MLT 0.86 CZE 0.55 DEU 0.74FIN 0.66 LVA 0.75 GRC 0.91 CZE 0.67 CZE 0.46 SWE 0.48 BEL 0.85 IRL 0.51 MLT 0.74DNK 0.62 EST 0.74 POL 0.91 DNK 0.84 SVN 0.65 HUN 0.45 BGR 0.43 AUT 0.79 BEL 0.50 ROM 0.71EST 0.61 DNK 0.73 AUT 0.88 SVN 0.80 BGR 0.52 EST 0.43 IRL 0.29 BGR 0.75 SWE 0.50 BGR 0.65SVK 0.59 IRL 0.64 DNK 0.82 POL 0.75 EST 0.47 SVN 0.39 DNK 0.29 SWE 0.65 DNK 0.44 LUX 0.52HUN 0.49 HUN 0.59 SVK 0.82 SVK 0.62 MLT 0.38 PRT 0.36 LTU 0.28 FIN 0.54 SVN 0.39 CZE 0.50MLT 0.38 MLT 0.53 SVN 0.80 NLD 0.60 HUN 0.35 SVK 0.34 MLT 0.19 SVK 0.53 PRT 0.34 SVN 0.48LTU 0.32 BGR 0.51 HUN 0.74 HUN 0.45 LUX 0.28 LUX 0.26 SVK 0.18 LUX 0.52 SVK 0.32 BEL 0.45LUX 0.26 LUX 0.17 LUX 0.28 LUX 0.43 SVK 0.28 MLT 0.09 SVN 0.15 DNK 0.48 LUX 0.12 SVK 0.43

Authors’ calculations

The model specification: standard gravity

xijt = β0 + β1gdpit + β2gdpjt + β3ETAijt − p1−σit − p1−σjt + �ijt (3)

where small letters denote variables in natural logarithms

i is the exporting country; j the importing country, and t the year;

x are bilateral trade flows;

gdp is the nominal GDP;

ETA is a set of dummies for controlling for the presence of Europeantrade agreements (i.e., EMU, EU and others regional memberships);

p1−σi and p1−σj are time varying multilateral (price) resistance terms

(Anderson & Van Wincoop, 2003);

� is the error term.

The model specification:our augmented dynamic gravity

Following Olivero & Yotov (2012):

xijt = α0+α1xijt−1+α2gdpit +α3gdpit−1+α4gdpjt +α5gdpjt−1+α6ETAijt +α7ETAijt−1++ α8gvcit + α9gvcit−1 + α10gvcjt + α11gvcjt−1 + α12ηit + α13ηit−1++ α14ηjt + α15ηjt−1 + γt + µijt

where small letters denote variables in natural logarithms

i is the exporting country; j the importing country, and t the year;

gvc are indices of international fragmentation of production and globalvalue chains;

ηi and ηj are network centrality measures (proxy for multilateral (price)resistance term;

γt is a set of time fixed effects;

the rest of the variables are the same as in the standard equation.

SGMM Gravity estimates (out/in-strength)

1 2 3 4 5 1 2 3 4 5L. Log exports 0.533*** 0.549*** 0.597*** 0.570*** 0.602***

(0.000) (0.000) (0.000) (0.000) (0.000)Log GDP exporter 0.113 0.375** 0.145 0.324** L. Log GDP exporter 0.0216 -0.108 -0.0798 -0.0843

(0.499) (0.017) (0.365) (0.031) (0.868) (0.459) (0.573) (0.561)Log GDP importer -0.219 0.127 -0.161 -0.00509 L. Log GDP importer -0.413*** -0.304** -0.506*** -0.350***

(0.224) (0.485) (0.381) (0.977) (0.002) (0.025) (0.000) (0.005)EMU (yes=1) 0.0725+ -0.0422 -0.0448+ -0.0463+ -0.0288 Lagged EMU (yes=1) 0.0681 0.0700** 0.0317 0.0833** 0.0479+

(0.112) (0.176) (0.124) (0.133) (0.324) (0.167) (0.038) (0.297) (0.016) (0.137)EU (yes=1) -0.00164 0.254*** 0.256*** 0.251*** 0.199*** Lagged EU (yes=1) 0.0605 0.0500 0.0559+ 0.0719** 0.0610*

(0.976) (0.000) (0.000) (0.000) (0.000) (0.322) (0.166) (0.111) (0.045) (0.082)Log FVA exporter 0.814*** 1.014*** L. Log FVA exporter -0.331** -0.596**

(0.000) (0.000) (0.031) (0.011)Log FVA importer 0.175 0.197 L. Log FVA importer 0.0410 0.856***

(0.170) (0.422) (0.797) (0.001)Log Pos exporter -0.401*** 0.163 L. Log Pos exporter 0.146* -0.180

(0.000) (0.274) (0.081) (0.161)Log Pos importer 0.109 0.418** L. Log Pos importer 0.179** 0.524***

(0.262) (0.015) (0.028) (0.000)Log outdregree strengthi 0.513*** 0.346*** 0.444*** 0.351*** L. Log outdregree strengthi -0.0673 -0.125 -0.200* -0.149+

(0.000) (0.002) (0.000) (0.001) (0.610) (0.261) (0.096) (0.148)Log indregree strengthi -0.271** -0.296*** -0.288*** -0.488*** L. Log indregree strengthi 0.138 0.0350 0.162+ 0.186**

(0.015) (0.005) (0.005) (0.000) (0.203) (0.726) (0.128) (0.049)Log outdregree strengthj 0.0892 0.0623 0.216 0.0881 L. Log outdregree strengthj -0.119 -0.0255 0.0786 -0.00270

(0.588) (0.704) (0.207) (0.553) (0.451) (0.872) (0.613) (0.987)Log indregree strengthj 1.271*** 1.011*** 1.193*** 1.129*** L. Log indregree strengthj 0.114 -0.0985 0.145 0.0141

(0.000) (0.000) (0.000) (0.000) (0.247) (0.406) (0.195) (0.893)cons. -0.403+ -12.71*** -12.85*** -13.79*** -15.68***

(0.146) (0.000) (0.000) (0.000) (0.000)Country pair dummy no no no no no No Obs 14318 12559 12559 12559 12559Time dummy no yes yes yes yes AB test (AR2) 0.810 0.948 0.840 0.928 0.860Exporter*time dummy yes no no no no No Instr. 928 100 132 132 164Importer*time dummy yes no no no no R-sq 0.954 0.930 0.944 0.938 0.945

Robust s.e. clustered by pair in parentheses; *** p

SGMM Gravity estimates (out/in-eigenvector)

1 2 3 4 5 1 2 3 4 5L. Log exports 0.533*** 0.647*** 0.647*** 0.617*** 0.678***

(0.000) (0.000) (0.000) (0.000) (0.000)Log GDP exporter 0.280* 0.609*** 0.421*** 0.586*** L. Log GDP exporter -0.144 -0.286** -0.225+ -0.293**

(0.091) (0.000) (0.009) (0.000) (0.274) (0.037) (0.105) (0.029)Log GDP importer 0.518*** 0.383** 0.600*** 0.306* L. Log GDP importer 0.0965 0.180 0.207 0.0436

(0.001) (0.016) (0.000) (0.056) (0.559) (0.288) (0.201) (0.784)EMU (yes=1) 0.0725+ -0.0357 -0.0457+ -0.0529* -0.0549* Lagged EMU (yes=1) 0.0681 0.0407 -0.0136 0.00699 -0.00682

(0.112) (0.270) (0.130) (0.099) (0.065) (0.167) (0.163) (0.625) (0.818) (0.812)EU (yes=1) -0.00164 0.336*** 0.248*** 0.331*** 0.171*** Lagged EU (yes=1) 0.0605 0.0640* 0.0306 0.0575* 0.00850

(0.976) (0.000) (0.000) (0.000) (0.000) (0.322) (0.070) (0.378) (0.094) (0.804)Log FVA exporter 0.768*** 0.954*** L. Log FVA exporter -0.596** -0.454*** -0.518**

(0.000) (0.000) (0.011) (0.003) (0.029)Log FVA importer 0.0776 0.278 L. Log FVA importer 0.856*** 0.543*** 1.025***

(0.586) (0.275) (0.001) (0.000) (0.000)Log Pos exporter -0.307*** 0.103 L. Log Pos exporter -0.180 0.213** -0.0518

(0.001) (0.425) (0.161) (0.014) (0.688)Log Pos importer -0.232*** 0.216+ L. Log Pos importer 0.524*** -0.192** 0.379**

(0.008) (0.150) (0.000) (0.023) (0.011)Log outdegree eigenvectori 0.356*** 0.313*** 0.376*** 0.254*** L. Log outdegree eigenvectori -0.0885 -0.0928 -0.126 -0.140*

(0.000) (0.000) (0.000) (0.003) (0.367) (0.299) (0.157) (0.073)Log indegree eigenvectori -0.154** -0.280*** -0.129* -0.441*** L. Log indegree eigenvectori -0.112 -0.0420 -0.131* 0.0417

(0.037) (0.000) (0.081) (0.000) (0.150) (0.562) (0.100) (0.570)Log outdegree eigenvectorj 0.101 -0.165 0.00926 -0.0959 L. Log outdegree eigenvectorj -0.109 0.0341 -0.0110 0.0854

(0.491) (0.213) (0.945) (0.424) (0.320) (0.750) (0.922) (0.402)Log indegree eigenvectorj 0.384*** 0.651*** 0.315*** 0.861*** L. Log indegree eigenvectorj -0.574*** -0.599*** -0.629*** -0.489***

(0.001) (0.000) (0.003) (0.000) (0.000) (0.000) (0.000) (0.000)cons. -0.403+ -8.043*** -12.58*** -12.08*** -10.70***

(0.146) (0.000) (0.000) (0.000) (0.000)Country pair dummy no no no no no No Obs 14318 12559 12559 12559 12559Time dummy no yes yes yes yes AB test (AR2) 0.810 0.747 0.794 0.795 0.792Exporter*time dummy yes no no no no No Instr. 928 100 132 132 164Importer*time dummy yes no no no no R-sq 0.954 0.942 0.952 0.942 0.955

Robust s.e. clustered by pair in parentheses; *** p

SGMM Gravity estimates(EU bilateral trade in intermediates)

LSDV SGMML. Log exports in intermediates 0.502*** 0.531***

(0.000) (0.000)EMU (yes=1) 0.0519 0.0945*

(0.250) (0.072)Lagged EMU (yes=1) -0.0311 0.0310

(0.524) (0.593)EU (yes=1) -0.0157 0.0148

(0.766) (0.791)Lagged EU (yes=1) -0.0174 0.0340

(0.729) (0.550)Cons 3.549*** -0.280

(0.000) (0.353)N 14355 14355R-sq 0.976 0.900Country pair dummy yes yesExporter*time dummy yes yesImporter*time dummy yes yes

Conclusions

Augmenting gravity equation w/indices of production fragmentation/GVC andtrade interdependence, we assess the empirical relevance of supply chains forbilateral trade across EU member countries

Specifically, estimates show, on average, a strong positive relation between theFVA content of the exporting countries and the magnitude of their exports of finalgoods

These suggest the relevance of the “Factory countries” on bilateral flows of finalgoods

Conversely, the relationship between the magnitude of imports of final goods andthe upstream position of the importing countries remains ambiguous

As expected, a strong positive association of out-strength/eigenvector centralityof exporters & in-strength/eigenvector centrality of importers with bilateral flowsis actually in place

Preliminary results show that the impact of the EU trade integration process isbetter specified when regressions properly account for vertical specialization andthe effects of the others on each bilateral trade

Conclusions

Further Steps

We would like to address explicitly the following critical issues:

countries and industries’ heterogeneity;

trade off between endogeneity and instruments’proliferation;

better MRT identification by using alternative NAmeasures.

Further Steps

AimsGVCs & Trade in value addedGVCs & NA measuresFactory EuropeEmpirical applicationConclusions & Further Steps

trade and global value chains in the eu: a dynamic ......following koopman et al. (2011), it...

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