Back Matter

Raju Huidrom; Nemanja Jovanovic; Carlos Mulas-Granados; Laura Papi; Faezeh Raei; Emil Stavrev; Philippe Wingender

doi:10.5089/9781498315135.087.A999

Back Matter

Author:

Raju Huidrom

Raju Huidrom https://isni.org/isni/0000000404811396, International Monetary Fund

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Nemanja Jovanovic

Nemanja Jovanovic
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Mr. Carlos Mulas-Granados

Mr. Carlos Mulas-Granados https://isni.org/isni/0000000404811396, International Monetary Fund

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Ms. Laura Papi

Ms. Laura Papi https://isni.org/isni/0000000404811396, International Monetary Fund

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Ms. Faezeh Raei

Ms. Faezeh Raei
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Mr. Emil Stavrev

Mr. Emil Stavrev
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, and

Mr. Philippe Wingender

Mr. Philippe Wingender
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Publication Date:: 12 Jun 2019

Language:: English

Keywords:: DPPP; DP; value-added export; trade flow; value chain participation; trade shock

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Annex 1. Network Analysis

This annex describes the steps used in Chapter 3 to illustrate the short-term impact of a hypothetical tariff shock through global value chain linkages. The calculation is based on the network of linkages between country-sector pairs within and across countries. Such linkages describe how the value added generated by each country sector embeds value added from and contributes value added to output of other country sectors, including to the country itself. These linkages are calculated using global input-output tables provided by EORA and the methodology described in Aslam, Novta, and Rodrigues-Bastos (2017). The data derived comprise several levels of aggregation, including country level, bilateral country level, and bilateral country-sector level. The main variables of interest are domestic value added and foreign value added in exports of each country sector, either in the aggerate (using country-level data) or by specific origin (using bilateral data).

The steps to calculate the impact of a tariff shock τ on country c, sector s, are as follows: a tariff of τ percent on exports of country c, sector s ( exp_cs) is assumed to imply a loss of α × τ × exp_cs in exports, in which α is based on import elasticities as described in Chapter 3. The elasticity applies to the total monetary value of exports and for simplification abstracts from delving into how much volume and prices react to such tariffs. Since exp_cs embeds value added by other countries, the loss incurred by country c would amount to $\frac{D V A}{\exp_{c s}} α \times τ \times \exp_{c s}$ ,in which $\frac{D V A}{\exp_{c s}}$ is the share of domestic value added in country c, sector s. The remaining loss $(1 - \frac{D V A}{\exp_{c s}}) α \times τ \times \exp_{c s}$ is accrued to the set of foreign country-sectors that comprise the foreign value-added component of exp_cs . This loss is then traced back to the countries of origin that comprise the foreign value added in country c, sector s, using bilateral country-sector linkage data. In the above scenario, a country sector could be affected directly, incurring a loss, or indirectly, through participation in the supply chain of another country. This exercise once repeated over all exporting countries allows us to capture all the losses incurred directly and indirectly by various countries and sectors. It is worth noting that the losses incurred by country c itself can be broken down in turn into losses directly incurred by sector s and losses by other sectors within country c that feed into sector s. The latter part, plus the sums of losses incurred through participation in foreign supply chains are summed up as supply-chain-related losses.

Annex 2. Global Value Chains and Real Effective Exchange Rates

This annex and the analysis in Chapter 4 draw on Bems and Johnson (2017), which contains more detailed derivations. This annex provides a very brief description of the main features of the model and the approach used to measure the effects of a tariff on gross output and value-added prices and demand for gross and value-added exports.

Modeling Demand for Trade

Assume that countries produce and consume both intermediate and final goods. Each country indexed by i, j ∈ {1, . . . , N} produces an aggregate good Q_i

\begin{matrix} Q_{i} = {({(ω_{i}^{v})}^{\frac{1}{γ}} {V_{i}}^{\frac{(γ - 1)}{γ}} + {(ω_{i}^{x})}^{\frac{1}{γ}} {X_{i}}^{\frac{(γ - 1)}{γ}})}^{\frac{γ}{(γ - 1)}}, & (1) \end{matrix}

with V_i denoting domestic value added. X_i is a constant elasticity of substitution (CES) composite intermediate good with elasticity of substitution (EoS) ρ that aggregates varieties X_ji imported from countries j . Demand for final goods is modeled the same way as in the original Armington (1969) framework as a CES aggregation of final goods varieties produced by all countries j.

\begin{matrix} F_{i} = {(Σ_{j} {(ω_{j i}^{f})}^{\frac{1}{σ}} F_{j i}^{\frac{(σ - 1)}{σ}})}^{\frac{σ}{(σ - 1)}} . & (2) \end{matrix}

The market clearing condition requires $Q_{i} = Σ_{i = 1}^{N} [F_{i j} + X_{i j}]$ . This contrasts with the original Armington specification with only demand for final goods fully specified by equation (2).

The above equations also illustrate the role played by the three underlying elasticity parameters: γ, ρ, and σ. The conventional framework models only one source of demand, with an elasticity parameter σ that governs substitutability across final goods varieties. Instead, Bems and Johnson’s value-added framework includes two types of goods and three elasticities that can take different values. The effective EoS for value added can be calculated as the weighted average of the three parameters. There is only sparse evidence on the relative size of these parameters, but there is some empirical support for the assumption that elasticity in final demand σ is higher than for production inputs γ and ρ. It is important to note that, qualitatively, the results presented in this paper rely on the assumption that final demand is the more elastic. Bems and Johnson show that the results are sensitive to this assumption and can even reverse if the relative size of elasticities is revised.

Impact of Tariffs

The tariffs are assumed to apply to countries’ gross exports. For simplicity, we assume full pass-through of the tariff to the price paid by buyers.¹ Using international relative price indices, this assumption implies for instance that a 5 percent tariff on the gross exports of country i to country j leads to a relative price change between 100 and 105. Equation (13) from Bems and Johnson characterizes how such changes in the price of gross output translate into changes in value-added prices.

{\hat{P}}^{V A} = [1 - Ω "] {[d i a g (s_{i}^{V A})]}^{- 1} {\hat{P}}^{G O} .

This equation means that value-added price changes are a weighted average of gross output price changes in all countries where the weights are the inverse of total cost shares.

The effect of a relative value-added (gross output) price change on a country’s demand for value added (gross output) is modeled sequentially since the paper’s framework has only a single price per country. The single price assumption also precludes the analysis of trade diversion.² First, the effect of a US tariff increases the relative price of gross and value-added exports of partner countries and decreases relative US prices. Equation (15) in Bems and Johnson shows how value-added demand for the United States can be calculated from changes in value-added prices using the following expression (and holding overall demand ${\hat{F}}_{i}^{w}$ constant):

\begin{matrix} {\hat{V}}_{U S} = - Σ_{j} T^{U S . j} {\hat{p}}_{j}^{V A} + {\hat{F}}_{U S'}^{w} & (3) \end{matrix}

in which $T^{U S, j} \equiv σ T_{σ}^{U S, j} + ρ T_{ρ}^{U S, j} + γ T_{γ}^{U S, j}$ are the value-added weights given by a linear combination of the underlying structural elasticities σ, ρ, and γ.³

Given the demand for gross and value-added exports of the United States, this decreases the exports of partner countries and increases domestic output for the United States. Retaliation is then modeled separately for partner countries in a parallel manner. A 5 percent retaliatory tariff decreases each country’s demand for US exports and increases demand for their own and others’ products in a way that mirrors equation (3). Given the size of the US economy and the fact that it is much more closed to international trade in relative terms, the impact of retaliation is on average about 10 times smaller than the impact of the US tarifF.

Annex 3. A Two-Step Method for Estimating Growth Spillovers

Principal components analysis: The first step purges the effects of common shocks for cross-country growth dynamics, which then yields country-specific growth shocks. We estimate three principal components, which account for much of the cross-country commonalities in growth. Country-specific shocks are then calculated as the residual growth after purging the effects of these estimated common components. The model is estimated using GDP (quarter-over-quarter) growth for a balanced panel of 68 economies during 1995:Q1 and 2018:Q3. It covers advanced Europe, emerging Europe, and other major advanced as well as emerging market and developing economies, thus providing global representation.

Local Projections Model: The second step is to trace the effects of the country-specific shocks on growth in other economies. The model is described as follows:

y_{i, t + h} = u_{i, h} + β_{h} U_{t} + θ_{h} (U_{t} \times {G V C}_{i, t - 1}) Ψ_{h} (L) y_{i, t - 1} + X_{i, t}^{"} Γ_{h} + ϵ_{i, t + h} f o r h = 0, 1, ... H,

in which y_i,t+h denotes (cumulative) GDP growth during time t + h and t for country i. In the model that is used to estimate spillovers originating in the United States, the shock U_t refers to the US-specific growth shock, and GVC_i denotes value-added exports as percent of GDP—as a measure of trade exposure of country i to the United States. The model includes its own growth and other variables, such as oil prices, global uncertainty,¹ and controls for country fixed effects. The model is then estimated for different horizons h, which is then used to project the impact of the US shock on growth in other countries h periods ahead. We use the same quarterly database as in the first step, but the spillover destinations in the local projections model are confined to the European countries. In the model for the United States, we include an additional dummy for the global financial crisis.

The local projections model yields a flexible framework to explore nonlinearities by including value-added exports as interaction terms: U_t × GVC_i,t-1. Because of this interaction term, the marginal impact of the shock on growth in other economies depends on the level of value-added exports. In our implementation, following Iacoviello and Navarro (2018), the measure of exposure is normalized as ${G V C}_{i, t} = \frac{{g v c}_{i, t} - {g v c}_{50}}{{g v c}_{90} - {g v c}_{50}},$ in which gvc_i,t (lowercase) denotes value-added exports for country i at time t; gvc₅₀ and gvc₉₀ respectively denote the median and 90th percentile of value-added exports in our sample.² With this normalization, the marginal impact of a US-specific growth shock on a “median” economy—defined as an economy whose value-added exports are at the median in the sample—for a given horizon h is given by β_h . And the corresponding marginal impact for a “high-exposure” economy—defined as an economy whose value-added exports are at the 90th percentile—is given by β_h+θ_h.³ The confidence bands are calculated using the Driscoll and Kraay (1998) standard errors, which allow arbitrary correlation of the error term across countries and time. We report two standard deviation confidence bands to assess the significance of the results. To estimate spillovers from China and Germany, we estimate a similar model by appropriately changing the shocks and the value-added exports. The controls remain the same as before.

Annex Table 3.1.

Value-Added Exports

(Percent of GDP)

Sources: EORA database; and IMF staff calculations. Note: Green denotes the median European economy with median level of value-added exports. Red refers to a “high-exposure” European economy with value-added exports at the 90th percentile of the distribution.

Annex Table 3.1.

Value-Added Exports

(Percent of GDP)

	United States	China	Germany
Albania	0.4	0.1	0.9
Austria	3.3	1.4	5.4
Belgium	4.4	1.6	5.0
Bulgaria	2.5	0.6	3.4
Bosnia and Herzegovina	0.9	0.2	1.6
Belarus	0.1	0.1	0.1
Switzerland	5.7	2.3	5.3
Cyprus	0.3	0.1	0.5
Czech Republic	4.0	1.3	11.7
Denmark	3.2	1.0	1.7
Spain	1.9	0.6	2.3
Estonia	2.6	0.6	2.5
Finland	3.6	2.1	2.1
France	2.6	1.0	1.3
United Kingdom	2.8	0.8	0.9
Greece	0.7	0.2	2.6
Croatia	1.7	0.2	2.0
Hungary	3.6	0.8	6.5
Ireland	5.2	0.7	2.3
Iceland	2.8	1.2	1.2
Israel	6.4	0.7	2.4
Italy	3.3	1.1	2.4
Lithuania	1.6	0.3	2.7
Luxembourg	1.6	0.7	1.9
Latvia	1.8	0.4	2.4
Moldova	0.0	0.0	0.0
North Macedonia	3.3	0.5	4.2
Malta	3.5	0.9	4.9
Montenegro, Rep. of	0.2	0.1	0.1
Netherlands	3.7	1.2	2.6
Norway	3.2	1.0	1.9
Poland	1.5	0.4	4.4
Portugal	1.9	0.5	2.3
Romania	1.8	0.4	3.1
Russia	1.7	1.3	1.1
Serbia	1.1	0.4	0.4
Slovak Republic	1.8	0.5	6.2
Slovenia	4.1	0.9	6.9
Sweden	3.6	1.5	1.5
Turkey	1.2	0.2	1.5
Ukraine	2.0	0.9	1.6
Median	2.5	0.7	2.3
High-exposure	4.1	1.4	5.4

Annex Table 3.1.

Value-Added Exports

(Percent of GDP)

	United States	China	Germany
Albania	0.4	0.1	0.9
Austria	3.3	1.4	5.4
Belgium	4.4	1.6	5.0
Bulgaria	2.5	0.6	3.4
Bosnia and Herzegovina	0.9	0.2	1.6
Belarus	0.1	0.1	0.1
Switzerland	5.7	2.3	5.3
Cyprus	0.3	0.1	0.5
Czech Republic	4.0	1.3	11.7
Denmark	3.2	1.0	1.7
Spain	1.9	0.6	2.3
Estonia	2.6	0.6	2.5
Finland	3.6	2.1	2.1
France	2.6	1.0	1.3
United Kingdom	2.8	0.8	0.9
Greece	0.7	0.2	2.6
Croatia	1.7	0.2	2.0
Hungary	3.6	0.8	6.5
Ireland	5.2	0.7	2.3
Iceland	2.8	1.2	1.2
Israel	6.4	0.7	2.4
Italy	3.3	1.1	2.4
Lithuania	1.6	0.3	2.7
Luxembourg	1.6	0.7	1.9
Latvia	1.8	0.4	2.4
Moldova	0.0	0.0	0.0
North Macedonia	3.3	0.5	4.2
Malta	3.5	0.9	4.9
Montenegro, Rep. of	0.2	0.1	0.1
Netherlands	3.7	1.2	2.6
Norway	3.2	1.0	1.9
Poland	1.5	0.4	4.4
Portugal	1.9	0.5	2.3
Romania	1.8	0.4	3.1
Russia	1.7	1.3	1.1
Serbia	1.1	0.4	0.4
Slovak Republic	1.8	0.5	6.2
Slovenia	4.1	0.9	6.9
Sweden	3.6	1.5	1.5
Turkey	1.2	0.2	1.5
Ukraine	2.0	0.9	1.6
Median	2.5	0.7	2.3
High-exposure	4.1	1.4	5.4

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Van der Marel, Erik. 2015. “Positioning on the Global Value Chain Map: Where Do You Want to Be?” Journal of World Trade, Issue 49(6): 915–949.
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World Bank. 2016. Global Economic Prospects: Spillovers amid Weak Growth. Washington, DC.
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World Bank. 2017. Measuring and Analyzing the Impact of GVCs on Economic Development. Washington, DC.
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World Trade Organization (WTO). 2019. Global Value Chain Development Report. Geneva.
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This paper uses the terms “global value chain data,” “value-added data,” and “value-added exports” interchangeably.

To study a hypothetical US-China trade dispute, the WEO chapter uses global general equilibrium models with three approaches that emphasize different transmission channels.

This chapter also abstracts from the trade diversion effect that takes place over the medium to long term when exporting countries face a rise in tariffs in their preferred destinations. IMF (2019) also uses network analysis, and instead of focusing on the impact of a specific tariff on the automobile sector, considers a hypothetical tariff common to all sectors of the economy and finds strong trade diversion effects.

See https://globalvaluechains.org/concept-tools.

http://www.oecd.org/sti/ind/measuring-trade-in-value-added.htm.

http://www.wiod.org/home.

http://worldmrio.com.

Although automation has so far been associated with greater value-added exports in Europe, it is unclear whether this trend will continue: advances in robotics, artificial intelligence, and automation could shorten global value chains (Box 3).

This potential measure was initially threatened by the US administration in the first half of 2018. The US president and the European Commission president agreed in July 2018 to start negotiations; in the meantime the United States would refrain from raising tariffs. A US report regarding the national security implications of imports of cars and car parts was completed in February 2019, but a decision is still pending as of May 21, 2019.

This figure is based on the EORA MRIO database, and although it is broadly in line with other databases such as the WIOD in aggregate, individual country values could be different. For this exercise EORA is chosen due to the broader country coverage. Please see Antràs and De Gortari (2017) and Aslam, Novta, and Rodrigues-Bastos (2017) for comparisons of the EORA and WIOD databases, which generally find comparable aggregate outcomes based on either database.

Johnson (1958); Corden (1960); Armington (1969); McGuirk (1987); Marsh and Tokarick (1994); Backus, Kehoe, and Kydland (1993); Obstfeld (2001).

The recent literature has underscored the importance of distinguishing between these types of traded goods. See Bayoumi, Saito, and Turunen (2013); Cheng and others (2016); Bems (2014); Bems and Johnson (2017); and Bayoumi and others (2018).

Gross turnover is available from national input-output tables and is equal to the sum of intermediate consumption, taxes minus subsidies, transportation margins, and value added. GDP itself can be calculated as the sum of value added across sectors.

For this chapter, we follow Bems and Johnson and use the WIOD database (http://www.wiod.org/release16).

An additional difference between VA-REER and GO-REER is the price measure used in REER indices. Many conventional indices rely on the consumer price index to capture changes in relative prices, consistent with the emphasis on trade in final goods and the expenditure-switching channel. The VA-REER, however, relies on changes in the GDP deflator, a more accurate measure of the price for value added. See Bems and Johnson (2017) for further discussion.

In the conventional framework, these weights are determined by gross trade flows. In the VA-REER framework, however, the weights reflect trade in value added—namely, the amount of value added originating in one country that is ultimately used in another (Koopman, Want, and Wei 2014; Aslam, Novta, and Rodrigues-Bastos 2017).

The calculations assume nested constant elasticity of substitution (CES) demand with a final demand EoS of 3 and Leontief production elasticities—between differentiated inputs and between total inputs and domestic value added—of zero. Bems and Johnson (2017) and Bayoumi and others (2018) discuss calibration of these parameters.

See Annex 2 for a description of the demand equations relating relative prices and demand for goods.

We use the Matlab code provided in the online additional materials of Bems and Johnson (2017) to calculate gross and value-added trade flows, partner weights, effective elasticities of substitution, and demand spillovers https://www.aeaweb.org/articles?id=10.1257/mac.20150216). We rely on the 2016 vintage of the World Input-Output Database (http://www.wiod.org/database/wiots16) to conduct the analysis for 43 countries, from 2000 to 2014. Bilateral exchange rates, the consumer price index, and the GDP deflator are taken from the World Economic Outlook.

For average tariff rates across the world, see https://data.worldbank.org/indicator/TM.TAX.MRCH.WM.AR.ZS.

This means a 5 percent tariff increases the value of the relative price index for gross output of country i from 100 to 105. See Annex 2 for details.

IMF (2018) discusses several channels through which trade tensions can affect economic activity. Ebeke and Siminitz (2018) analyze the impact of trade policy uncertainty on investment in the euro area.

The trade exposure of an economy to the United States, for instance, is defined as value-added exports of that economy to the United States as percent of that economy’s GDP. See Table 2.

There is a rich set of literature that looks at the role of trade for business cycle synchronization and spillovers (for example, Frankel and Rose 1998; Baxter and Kouparitsas 2005; Imbs 2004; Inklaar and others 2008; Iacoviello and Navarro 2018). Most of these studies have, however, relied on gross trade data (conventional trade). The role of global value chains, despite its increasing relevance, has received scant attention in the literature. Recent work (for example, Duval and others 2016) suggests that supply chain linkages are important for business cycle synchronization. We extend this relatively new work stream by exploring the role of global value chains for growth spillovers.

The methodology is similar to the one used in IMF (2013b) which computes the exogenous growth shock for an economy as the residual growth after purging out the effects of average growth over time of that economy and growth in the rest of the economies at a given point in time.

The sample starts in the first quarter of 1995 since this is the earliest period for which a balanced panel can be compiled for the countries we cover. In addition to the European economies, the panel includes the major advanced as well as emerging market and developing economies, thus providing a global representation. The latter is crucial since we want to capture the common shocks in the first step.

The three principal components, on average, explain about 28 percent of the variance in GDP growth. The first principal component explains the bulk of the variance, about 21 percent. For robustness, we estimate a version of the PCA model with only the first principal component. It yields very similar residual growth shocks.

See World Bank (2016) for a survey.

The propagation of common shocks is a relevant question. It is, however, unclear what the interaction term should be if one were to estimate the effects of common shocks using the framework.

See, for instance, World Bank (2016), Rey (2015), Miranda-Agrippino and Rey (2017), Bruno and Shin (2016) for a discussion.

Mapping the size of the tariff shocks in the previous chapters to the size of the growth shocks is not straightforward. Hence, without loss of generality, we simply take a unit growth shock that allows an easy comparison across the hub countries.

Also, note that value-added exports for a spillover destination economy (as a measure of trade exposure) are calculated relative to its GDP. So this already takes into consideration the fact that economies in emerging Europe are smaller than those in advanced Europe.

Assuming a different pass-through would rescale the effects of tariffs on trade but would not lead to qualitatively different results since demand is linear in prices.

A derivation of the value-added demand equations that relaxes the assumption of unique prices per country is beyond the scope of this paper.

Equation (6) in Bems and Johnson shows that demand for gross output in the conventional framework without intermediate trade is a function of σ and export market shares.

Measured by the stock market volatility index (http://www.cboe.com/vix).

See Annex Table 3.1 for value-added exports of various countries.

Our results are generally robust to the inclusion of value-added exports as a separate regressor.

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Trade Tensions, Global Value Chains, and Spillovers: Insights for Europe

Author:

Raju Huidrom

Nemanja Jovanovic

Mr. Carlos Mulas-Granados

Ms. Laura Papi

Ms. Faezeh Raei

Mr. Emil Stavrev

, and

Mr. Philippe Wingender

Volume/Issue:: Volume 2019: Issue 009

Publisher:: International Monetary Fund

ISBN:: 9781498315135

ISSN:: 2616-5333

Pages:: 77

DOI:: https://doi.org/10.5089/9781498315135.087

Keywords
Annex 1. Network Analysis
Annex 2. Global Value Chains and Real Effective Exchange Rates
- Modeling Demand for Trade
- Impact of Tariffs
Annex 3. A Two-Step Method for Estimating Growth Spillovers
References

Annex Table 3.1.

Value-Added Exports

(Percent of GDP)

	United States	China	Germany
Albania	0.4	0.1	0.9
Austria	3.3	1.4	5.4
Belgium	4.4	1.6	5.0
Bulgaria	2.5	0.6	3.4
Bosnia and Herzegovina	0.9	0.2	1.6
Belarus	0.1	0.1	0.1
Switzerland	5.7	2.3	5.3
Cyprus	0.3	0.1	0.5
Czech Republic	4.0	1.3	11.7
Denmark	3.2	1.0	1.7
Spain	1.9	0.6	2.3
Estonia	2.6	0.6	2.5
Finland	3.6	2.1	2.1
France	2.6	1.0	1.3
United Kingdom	2.8	0.8	0.9
Greece	0.7	0.2	2.6
Croatia	1.7	0.2	2.0
Hungary	3.6	0.8	6.5
Ireland	5.2	0.7	2.3
Iceland	2.8	1.2	1.2
Israel	6.4	0.7	2.4
Italy	3.3	1.1	2.4
Lithuania	1.6	0.3	2.7
Luxembourg	1.6	0.7	1.9
Latvia	1.8	0.4	2.4
Moldova	0.0	0.0	0.0
North Macedonia	3.3	0.5	4.2
Malta	3.5	0.9	4.9
Montenegro, Rep. of	0.2	0.1	0.1
Netherlands	3.7	1.2	2.6
Norway	3.2	1.0	1.9
Poland	1.5	0.4	4.4
Portugal	1.9	0.5	2.3
Romania	1.8	0.4	3.1
Russia	1.7	1.3	1.1
Serbia	1.1	0.4	0.4
Slovak Republic	1.8	0.5	6.2
Slovenia	4.1	0.9	6.9
Sweden	3.6	1.5	1.5
Turkey	1.2	0.2	1.5
Ukraine	2.0	0.9	1.6
Median	2.5	0.7	2.3
High-exposure	4.1	1.4	5.4

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Annex 1. Network Analysis

Annex 2. Global Value Chains and Real Effective Exchange Rates

Modeling Demand for Trade

Impact of Tariffs

Annex 3. A Two-Step Method for Estimating Growth Spillovers

References

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