Gravity-Based Tools to Assess the Impact of Tariff Changes: An Application to Armenia

Abstract

This paper uses two empirical tools to quantify the impact of tariff changes on bilateral trade and welfare. Both tools are rooted in structural gravity literature. The first tool estimates the impact of tariff changes on bilateral trade for 5,020 products in a partial equilibrium framework. The second tool quantifies the impact on bilateral aggregate trade in a general equilibrium setup, allowing estimates of trade diversion and welfare changes. These tools are used to estimate the impact of tariff changes on Armenia with regard to (i) its alignment with the external tariff of the Eurasian Economic Union; (ii) free trade agreements between the Eurasian Economic Union and other economies, including Iran and the People's Republic of China; and (iii) Armenia's loss of beneficiary status under the Generalised Scheme of Preferences of the European Union.

Headnote

This paper uses two empirical tools to quantify the impact of tariff changes on bilateral trade and welfare. Both tools are rooted in structural gravity literature. The first tool estimates the impact of tariff changes on bilateral trade for 5,020 products in a partial equilibrium framework. The second tool quantifies the impact on bilateral aggregate trade in a general equilibrium setup, allowing estimates of trade diversion and welfare changes. These tools are used to estimate the impact of tariff changes on Armenia with regard to (i) its alignment with the external tariff of the Eurasian Economic Union; (ii) free trade agreements between the Eurasian Economic Union and other economies, including Iran and the People's Republic of China; and (iii) Armenia's loss of beneficiary status under the Generalised Scheme of Preferences of the European Union.

Keywords: Armenia, Eurasian Economic Union, free trade agreements, generalised scheme of preferences, gravity

ML codes: F13, F14, F15, F17

I. Introduction

This paper uses two empirical tools to quantify the impact of tariff changes on trade and welfare. The tools can assess the impact of any trade policy change that can be mapped to tariff changes. The approach combines the literature on the structural gravity model (Arkolakis, Costinot, and Rodriguez-Clare 2012; Head and Mayer 2014; Yotov et al. 2016) with product-level bilateral trade data.1 These tools complement each other. They can inform policymakers, business communities, and the public on the impact of policies such as bilateral trade agreements and preferential tariff schemes.

Assessments of the impact of tariff changes aimed at policymakers often rely on Computable General Equilibrium (CGE) models, notably the Global Trade Analysis Project (GTAP) CGE model. The structural gravity framework, on the other hand, has mostly remained within academia. This paper aims to bridge this gap by using tools rooted in the gravity literature to quantify the impact of tariff changes at the product level.

CGE models are generally complex as they explicitly model, for example, labor and capital markets, natural resources, and investment and savings patterns. As such, they may uncover unexpected consequences of trade policy changes. CGE models also rely on numerous elasticities to capture responses by certain variables to changes in others. These elasticities are taken from studies that often estimate them using data for advanced economies, which makes conclusions questionable in different settings. Their complexify limits the ability of CGE models to attribute effects to specific mechanisms. They also have high data requirements, which constrain the questions they can address in terms of time and geographical coverage, and product detail.2 For smaller economies, the data underlying GTAP are also often outdated. For example, input-output data for Armenia in the latest version of GTAP are from 2002.

The structural gravity model is also a general equilibrium model-it accounts for indirect linkages between economies while imposing market clearing in all markets- but it is much simpler. The elasticities required to quantify the impact of tariff changes can be estimated using data relevant to the focus of the analysis. The gravity model allows for the elicitation of the mechanisms that lead from tariff changes to their effects. It is also less demanding than CGE models in terms of data. It can thus answer

As of February 2021, bilateral trade data in the BACI Database from the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) only covered up to 2018 (http://www.cepii.fr/ CEPII/en/bdd modele/bdd modele item.asp?id=37, accessed 1 February 2022).

The 2019 iteration of GTAP covers 121 economies, plus 20 regions, for 4 years (2004, 2007, 2011, and 2014) and distinguishes 65 sectors (including services).

3Trade data before 1995 for most former Soviet Union countries are missing. The last year with available tariff and trade data is 2018. Tariff data are from the Trade Analysis Information System of the United Nations (UN) Conference on Trade and Development, https://databank.worldbank.org/source/ unctad-%5E-trade-analysis-information-system-(trains) (accessed 1 February 2021). Fontagne, Guimbard, and Orefice (2019) use data from the International Trade Center. Market Access Map Database, https:// www.macmap.org/ (accessed 1 February 2022).

questions on distant periods and smaller economies, and at high levels of product detail.

The first tool we rely on reflects a partial equilibrium reading of the gravity model. This partial equilibrium tool generates trade impacts across 5,020 products facing tariff changes. This tool requires product-level elasticities of trade-to-trade costs. These elasticities are estimated using two specifications anchored in the structural gravity literature. The first one includes the usual gravity controls and is similar to Fontagne, Guimbard, and Orefice (2019). The second specification follows the best-practice estimation of the structural gravity model even more closely by including pair fixed effects. The elasticities estimated with pair fixed effects are on average closer to zero (less negative) than those from the specification with gravity controls. This suggests the latter are biased downward as unobserved trade costs- those not controlled for in the specification with gravity controls rather than pair fixed effects-are positively correlated with tariffs. The estimations are performed on data for 1995-2018, while Fontagne, Guimbard, and Orefice (2019) use data for every third year during 2001-2016.3

The elasticities are then combined with scenarios of tariff changes to predict the impact on trade at the product level. The product-level detail makes this tool relevant for stakeholders, such as trade negotiators (to assess alternative deals), local governments, and business communities (to assess impacts on specific industries). This distinction provides an important advantage over models relying on sector data as trade agreements are negotiated on highly disaggregated products, fn addition, economic outcomes may depend on concessions or exceptions for very specific products-particularly for small economies as they tend to be more specialized.

The second tool reflects the full general equilibrium linkages in the gravity model. Contrary to the first tool, it captures the indirect impact of tariff changes on trade, also known as trade diversion. An increase in tariffs between two economies reduces trade between them-this is the direct effect. But these tariff increases also make trade with other economies cheaper in relative terms. This pushes some trade to reallocate to partners not affected by tariff changes-this is the indirect effect. Other than bilateral trade, the general equilibrium approach also requires data on domestic consumption. However, these data are not available at the Harmonized System (HS)

classification 6-digit product level and is limited to manufacturing.4 Due to data limitations, the general equilibrium tool thus relies on aggregate manufacturing data.

In short, this second tool quantifies the indirect impact of tariff changes but at the cost of losing the product granularity of the first tool and reducing the coverage to manufacturing. As the general equilibrium tool excludes commodities, which are a key component of Armenia's exports, the resulting assessment of import and export changes might underestimate the impact of tariff changes on Armenia's overall trade.

The general equilibrium tool estimates the impact of tariff changes on welfare for all countries, including those not directly affected by the tariff changes. The welfare estimations also proceed from the structural gravity model, following Arkolakis, Costinot, and Rodriguez-Clare (2012). They are implemented using a routine proposed by Baier, Yotov, and Zylkin (2019) and inspired by Head and Mayer (2014).

Both tools are used in this paper to estimate the impacts of three recent or potential series of tariff policy changes affecting Armenia: (i) the convergence to the Common External Tariff (CET) of the Eurasian Economic Union (EEU); (ii) Free Trade Agreements (FTAs) between the EEU and Iran, and between the EEU and the People's Republic of China (PRC); and (iii) the end of eligibility for the Generalised Scheme of Preferences (GSP) of the European Union (EU).5

Armenia has been a member of the EEU since 2015, together with Belarus, Kazakhstan, the Kyrgyz Republic, and the Russian Federation. Armenia has pursued a liberal trade policy since 1991, with an average Most Favored Nation (MFN) tariff at 2.9% in 2014. Accession to the EEU required that its tariffs converge to the EEU CET, which was 6.0% in 2015. To ease the convergence to this new, higher EEU external tariff, Armenia received exemptions for 6.3% of the products listed in the CET, accounting for 38% of Armenia's imports from outside the EEU (World Trade Organization [WTO] 2018), with a transition period until 31 December 2021. In other words, joining the EEU made Armenia more protectionist vis-a-vis third economies without reducing intra-EEU tariffs as they had already been eliminated through FTAs signed following the dissolution of the Soviet Union. This specific case is in contrast with Article XXIVof the General Agreement on Tariffs and Trade, which requires FTA

A first version of the International Trade and Production Database for Estimation was released by the United States International Trade Commission in May 2020. The database reports product-level domestic production as well as bilateral trade, making it a natural fit for gravity estimations. Numerous data gaps, however, made it unsuitable to our purpose. A second version of the database was released in July 2022 with more extensive coverage.

5ADB (2021) estimates that the EEU-Serbia FTA (signed on 25 October 2019) will have a negligible impact, as would a potential EEU-India FTA.

6The scenario for Iran extends beyond the interim agreement in force since October 2019. The full-coverage assumption is critical as key export opportunities for Armenia concentrate on a few products such as liquor and tobacco.

and customs union members not to raise trade barriers with other countries. In such a case, reduction in trade costs can thus only arise from nontariff channels, and the welfare impact of joining the EEU would depend on the nontariff gains relative to the welfare-reducing tariff changes.

The nontariff implications of EEU membership include measures regarding product quality, certification, labeling, and packaging. Many of these requirements, however, reflect international standards recognized by the WTO, which Armenia has been a member of since 2003. As a single market, the EEU also implies freedom of movement for goods, services, capital, and people. This should boost Armenia's trade within the bloc beyond the effect of raising tariffs with other trade partners. Armenia, however, does not share a border with any other EEU members. Trade to the EEU thus needs to first cross through Georgia. And Armenia already enjoyed free movement of people with Russia prior to 2015. These factors limit the impact of the single EEU market for Armenia, leaving changes to the tariff schedule arguably as the main channel through which the EEU affects the country.

Membership in the EEU only affects the tariffs imposed on Armenia's imports, as its exports to all EEU members were already duty-free. The general equilibrium results suggest that alignment to the EEU CET reduces Armenia's imports of manufactured goods by 1.5% and its welfare by 1.6% (Table 1). Imports of manufactured goods from outside the EEU are estimated to be reduced by 4.1%, mostly from the EU and the PRC, while imports from the EEU and other duty-free partners are estimated to increase by 2.5%, mostly from Russia. The partial equilibrium tool suggests that import reductions from outside the EEU will be concentrated in vehicles, pharmaceuticals, and plastics. Convergence to the CET also implies tariff reductions for about 20% of tariff lines. These are estimated to increase imports of electronics, fruit, coffee, and tea.

EEU membership also improves market access for Armenia through the FTAs negotiated by the EEU. The paper assesses the impact of potential EEU-PRC and EEU-Iran FTAs.6 The general equilibrium analysis suggests that together the two FTAs would increase Armenia's welfare by 1.0%.

Results suggest that an EEU-Iran FTA would increase Armenia's welfare by 0.3%, manufacturing imports by 0.4%, and manufacturing exports by 0.7%-with total exports to Iran almost doubling. The partial equilibrium tool suggests that

Armenia would export more vehicles, chemicals, and confectionery to Iran, while importing more plastics, mineral fuels, and iron from Iran. An EEU-PRC FTA would boost Armenia's welfare by 0.7%, manufacturing imports by 0.3%, and manufacturing exports by 0.6%. Bilateral exports to the PRC would increase for vehicles and clothing; and imports would increase for electronics, vehicles, engines, and furniture. Both FTAs would also involve some trade diversion away from Russia.

Armenia was granted GSP+ benefits in 2009, obtaining duty-free access to the EU for 66% of tariff lines. However, EU legislation provides that a country classified by the World Bank as having upper-middle-income status for 3 consecutive years should stop benefitting from GSP arrangements (EU 2012). As Armenia has been classified as an upper-middle-income country since fiscal year 2019, which ended on 30 June 2019, its GSP eligibility ceased on 1 January 2022 (EU 2021). The general equilibrium results suggest that the loss of GSP eligibility will reduce Armenia's welfare by 0.1% and its manufacturing exports by 0.9%, with manufacturing exports to the EU falling by about 12% and those to Russia increasing by about 2%. The partial equilibrium analysis suggests that the impact will concentrate on exports of iron and steel products (largely ferromolybdenum), aluminum foil, and textiles.

Section II discusses the theoretical and empirical frameworks. Section III presents the product-level estimations of trade elasticities. Section IV discusses recent changes in Armenia's tariffs. Section V presents the estimated impacts of these changes. Section VI concludes.

II. Theoretical and Empirical Frameworks

A. Structural Gravity

The original gravity model is an empirical relationship describing bilateral trade proportional to the gross domestic product (GDP) of the partners and inversely proportional to the distance between them. Proposed by Tinbergen (1962), this relationship is known as the gravity equation due to its similarity to Isaac Newton's equation.

Although empirically powerful, the gravity equation lacked theoretical foundations until the early 2000s. Eaton and Kortum (2002) derived gravity equations from a Ricardian model, and Anderson and van Wincoop (2003) derived gravity equations from a national product differentiation model. Chaney (2008) derived a similar gravity equation from a heterogeneous trade model, expanding on Melitz (2003). Yotov et al. (2016) extend the baseline structural gravity model with iceberg trade costs to include tariffs. Tariff revenues enter the budget constraint of the consumers, and the revenues are assumed to be rebated to consumers and added to their nominal income. The resulting structural gravity equation is as follows:

where Xy denotes exports from location / to locationy. Yt is i's production. Ej isj's total expenditure, ty represents bilateral trade costs between / andy.7 And Ty = 1 + tariff^ (ad-valorem tariff on y's imports from /).

The key feature in structural gravity is the "multilateral resistance terms." II, is the outward resistance term measuring exporter z's market access:

These two terms (II, and Pj) capture the overall trade restrictiveness of partners. They highlight that bilateral trade is also linked to the alternative options available to

7These are the so-called iceberg transport costs as a fraction of the shipment value is spent on export-related costs.

8In 2018, total trade between Australia and New Zealand was $12.3 billion (compared with $3.8 billion between Greece and Spain). Australia's GDP was $1,433 billion (compared with $1,420 billion for Spain), and both Greece's and New Zealand's GDP was $212 billion. The distance between Sydney and Wellington is 2,232 kilometers (compared with 2,383 kilometers between Athens and Madrid).

Armenia's weighted average tariff increased from 2.4% in 2014 to 3.1% in 2020. Applied to imports as of 2021, this translates into a $37 million increase in tariff revenue, equivalent to 0.27% of GDP. In contrast-and although GDP and welfare are distinct concepts-this paper estimates the welfare-reducing impact of the EEU at 1.57%.

each partner. For example, Australia and New Zealand trade four times more than Greece and Spain, despite Australia and Spain having similar GDPs, and Greece's and New Zealand's GDPs being similar as well. The geographic distance between the two pairs is also similar.8 The multilateral resistance terms imply that the structural gravity model cannot be solved analytically. The general equilibrium tool thus relies on a numerical resolution of the model.

In equation (1), 1 - a is the elasticity of bilateral trade to trade costs, referred to in the literature as the "trade elasticity." The interpretation of this elasticity varies across the micro-foundations of the structural gravity equation. In the national product differentiation model (Anderson and van Wincoop 2003), a > 1 is the elasticity of substitution across varieties. Intuitively, when a product can easily be replaced by another (large a), a slight increase in trade frictions will cause a large decline in trade. In the Ricardian model (Eaton and Kortum 2002) and the heterogeneous firms model (Chaney 2008), the trade elasticity is -9, where 9 reflects heterogeneity in the productivity distribution. In these models, trade gains arise because sector- and firm-level productivities vary across countries. The larger 9 is (i.e., more homogeneous productivities), the smaller the trade gains and the larger the sensitivity of trade-to-trade costs.

The traditional structural gravity model assumes iceberg trade costs. But from the perspective of the domestic economy-and contrary to iceberg costs-tariffs do not disappear; they contribute to welfare through the government spending they enable. Tariff revenue thus partially mitigates the welfare loss from tariff protection. The welfare estimations in this paper do not factor this in. However, simple computation shows that this effect is much smaller than the estimated welfare impacts.9

B. Partial Equilibrium Tool

1. Step 1: Estimation of Product-Level Trade Elasticities

Trade elasticities are not only necessary as an input to compute direct trade impacts in the partial equilibrium tool, they are also a crucial input to compute the welfare impact of tariff changes. Arkolakis, Costinot, and Rodriguez-Clare (2012) show that in a wide range of models leading to structural gravity equations, the welfare

This magnification is larger for countries where products with low trade elasticities-such as food and raw materials-are either a large share of consumption or are largely imported.

impact of changes in trade is a function of (i) changes in the ratio of domestic-to-total expenditure, and (ii) the trade elasticity.

Feenstra (1994), Broda and Weinstein (2006), and Ossa (2015) estimate product-level elasticities of substitutions across varieties (a) using an import demand function. These elasticities can then be mapped to trade elasticities (1 - a). They estimate these elasticities (i) as the response of imports to changes in the relative prices of varieties (product-country combinations), and (ii) to assess welfare gains from increased diversity in imported varieties, as per Krugman's (1979) monopolistic competition trade model. Feenstra (1994) estimates elasticities for six products using United States (US) import data. Broda and Weinstein (2006) estimate elasticities for US imports, with a breakdown across 11,040 products during 1972-1988 and 13,972 products during 1990-2001. Ossa (2015) extends the Arkolakis, Costinot, and Rodriguez-Clare (2012) welfare analysis to allow for sector-level heterogeneity. He estimates trade elasticities using data from 49 countries and 251 sectors in 2007. He finds that the welfare impacts of trade changes increase by a factor of three when computed using the Arkolakis, Costinot, and Rodriguez-Clare (2012) formula at the sector level instead of using aggregate data and a single trade elasticity.

Estimations in this paper rely on the structural gravity framework and use tariffs to identify trade elasticities. In that, they are closer to Caliendo and Parro (2015) and similar to Fontagne, Guimbard, and Orefice (2019). Caliendo and Parro (2015) use the multiplicative structure of the gravity equation to cancel out unobserved symmetric trade costs (i.e., those that pertain to a country pair regardless of their direction). Their identification relies on asymmetries in tariffs between partners. They estimate elasticities for 20 sectors in 1993. Fontagne, Guimbard, and Orefice (2019) estimate product-level trade elasticities through a fixed-effects specification. They use bilateral tariffs to identify trade elasticities, controlling for other observable bilateral trade costs. Like Ossa (2015), they find that product-level elasticities magnify the welfare impact of trade, particularly for lower-income economies.10 This paper expands on their work by estimating elasticities using country-pair fixed effects. These fixed effects control for observable bilateral features-such as distance and common language-but also for other bilateral time-invariant features. However, tariffs vary across country pairs more than they do across time, leading to a smaller number of significant elasticities.

Extensive literature has converged to best-practice recommendations for estimating structural gravity models (Head and Mayer 2014, Yotov et al. 2016).

This paper follows the literature and performs estimations using data for 1995-2018. As the gravity model is separable across products, estimations are performed for each 6-digit product.11 To avoid overloading the notation, product subscripts are not reported in the equations below.

First, tariff elasticities are estimated with a panel data specification including the appropriate structure of fixed effects. Time subscripts (?) are added to account for the panel structure. As multilateral resistance terms are not observable, all exporter-year-specific and importer-year-specific variables are replaced by origin-year and destination-year fixed effects. These fixed effects capture production, expenditure, and multilateral resistance.

Second, estimations are performed using the Poisson Pseudo Maximum Likelihood (PPML) estimator. Santos Silva and Tenreyro (2006) show that log-linear estimations of the gravity equation can be inconsistent and recommend an estimation in multiplicative form using the PPML estimator. The PPML estimator also allows zero trade flows to be included while they disappear through log-linearization.

Product-level tariff elasticities are estimated using two specifications: with gravity controls and with pair fixed effects. In both, standard errors are clustered at the country-pair level.13

a. Specification with Gravity Controls

The specification with gravity controls includes observable bilateral features known to affect trade costs. Bilateral trade costs are specified as follows:

11 To remain consistent with structural gravity, estimating elasticities on a dataset including all products would entail applying importer-exporter-product, importer-product-year, and exporter-product-year fixed effects. This would require computing power not available to the authors.

12The estimations are made using ppmlhdfe, a Stata command for gravity estimations with high-dimensional fixed effects (Correia, Guimaraes, and Zylkin 2020).

13Following a suggestion from an anonymous referee and to address an issue related to incidental parameter problems associated with the PPML estimator, the analytical bias correction described by Weidner and Zylkin (2021) was implemented using the associated ppmlj'e bias Stata command. When estimating the gravity equation with two-way fixed effects, this procedure corrects downward-biased standard errors, while point estimates remain unchanged. Given the long processing time (requiring 5,300 estimations for each of the two methods that we use), the correction was implemented on a subsample of 10 randomly selected HS chapters: 04, 12, 14, 30, 31, 46, 48, 64, 66, and 80. Within these chapters, out of the 160 6-digit products for which negative and 95% significant elasticities had originally been estimated, 17 turned nonsignificant after implementing the correction (10.6%), out of which only two turned nonsignificant at the 10% level (1.3%). If this command had been available at the time of writing, we would certainly have implemented it for all estimations. However, given the test above performed on a subsample, we are fairly confident that our results were not driven by not having made this adjustment.

Customs unions are a deeper form of regional integration than FTAs. They typically involve convergence of nontariff measures, besides removal of tariffs. The customs union dummy is intended to capture these additional, nontariff effects. We did estimate product-level elasticities including both the customs union and FTA dummies. The latter, however, absorbs most of the tariff variation and the results are thus not reported.

The elasticities estimated with both specifications are available at https://sites.google.com/site/ juleshugot/research.

where Tariff ^ is the bilateral product-level tariff. CU,^, Conti^, Cornl^, and Colo^ are dummy variables accounting for, respectively, customs unions, common borders, common languages, and colonial relationships as of 1945.14 Dist^ is the population-weighted bilateral distance between the most populated cities in countries / and j.

Plugging equations (4) and (5) into equation (1) and replacing nonbilateral variables by fixed effects leads to the following specification:

b. Specification with Pair Fixed Effects

Both bilateral trade and bilateral tariffs vary across country pairs and years. This enables estimation of the tariff elasticity while including country-pair fixed effects to control time-invariant bilateral trade costs (e.g., geography and cultural commonalities), ff country-pair fixed effects are included, time-invariant trade costs can be removed from equation (6), leading to the following specification:

This specification is implied by the structural gravity equation as it controls for any bilateral time-invariant features, not only those included in equation (6).

Tariffs mostly vary across country pairs (between variance), not across time (within variance) (Figure 1). Bilateral fixed effects thus leave little variation to identify tariff elasticities. This generates nonsignificant tariff elasticities for many products. The empirical exercise applied to Armenia thus relies on elasticities from the specification with gravity controls.15

c. Residual Endogeneity

The bilateral effects control for endogeneity arising from time-invariant pair-specific features, but a possibility remains that time-varying bilateral factors correlated with tariffs also affect bilateral trade. Nontariff barriers would be the most obvious such factor. However, they often apply to all partners-such as labeling requirements-and would largely be absorbed by the country-year effects.

The nontariff barriers that could be sources of endogeneity are those that vary across both country pairs and time. This could include measures that are positively correlated with tariffs. For example, when a tariff cannot be raised further, a government might resort to raising nontariff barriers instead. Nontariff barriers could also be negatively correlated with tariffs. For example, a government might raise nontariff barriers to protect an industry from increased competition arising from an FTA. A positive (negative) correlation between nontariff barriers and tariffs would result in an upward (downward) bias in the estimated trade elasticities.16

16Other sources of potential residual endogeneity include correlation between tariffs and importer-product-year fixed effects, and reverse causality from trade flows to tariffs (Boehm, Levchenko, and Pandalai-Nayar 2020).

2. Step 2: Counterfactuals

The second step of the partial equilibrium tool combines the tariff scenarios with the trade elasticities to generate counterfactuals and quantify the trade impact of each scenario.

Let superscripts 0 and CF denote baseline and counterfactual variables. For baseline and counterfactual trade, equation (6) is rewritten, respectively, as follows:

The variables in equations (8) and (9) are either available from the data or the counterfactual scenarios or estimated in the first step.

For Armenia's trade within the EEU, the Customs Union (CU) dummy should switch to 1 in the counterfactual equation (9) after 2015. However, the coefficient captures an average impact, while EEU members were already particularly integrated before 2015-through FTAs-but more fundamentally as they belonged to the same country (the former Soviet Union) before 1992. Also, the trade impact of customs unions partly captures easier border crossing. This effect is reduced for Armenia as it is landlocked and does not border any other EEU member. Integrating the customs union status in the empirical exercise would thus yield implausibly large impacts for trade between Armenia and its EEU partners. Instead, the customs union dummy is excluded from the counterfactuals, which thus reflect the sole impact of tariff changes. The product-level trade impact of tariff changes is as follows:

The numerator and the denominator of equation (10) are identical when tariff and customs union status both remain the same. This partial equilibrium analysis thus ignores the effects of tariff changes for other partners.

C. General Equilibrium Tool

In the structural gravity equation, indirect effects to third partners are channeled through the multilateral resistance terms. For example, a reduction in bilateral tariffs

between two partners decreases their relative accessibility for other partners. Producer prices are also allowed to adjust. For example, when tariffs increase, producers need to lower their prices to keep exporting.

Assessing these effects requires solving the gravity model. But given the nonlinearities introduced by the multilateral resistance terms, it cannot be solved analytically. Instead, the general equilibrium tool relies upon the method implemented by Baier, Yotov, and Zylkin (2019) through the ge_gravity Stata command and inspired by Head and Mayer (2014).17 The method implements an algorithm to find fixed points for the system of equations and solves for the full general equilibrium trade impact. It then iterates until the parameters stop changing. The Stata module provides both the resulting change in welfare for each country (i.e., the new level of welfare divided by the old level of welfare) and the new level of trade for each pair of countries. To obtain trade levels, which are nominal variables, the algorithm requires normalization, this is done assuming that total world output does not change from the baseline to counterfactual scenario.

To calculate trade diversion across partners-but also with oneself-the method also requires data on domestic production. These data, however, are not available at the product level.18 Instead, aggregate data are used for domestic production, bilateral trade, and tariffs.19 Also, the domestic production data are limited to manufacturing. For commodity-dependent economies, this approach might therefore underestimate the overall impact of tariff changes. Lastly, the product-level trade elasticities from the partial equilibrium tool cannot be used. Instead, we use a single trade elasticity set to -4, which is standard in the trade literature and in line with the median product-level elasticity we estimate (-4.47).20

The last step consists of computing the impact of trade changes on welfare, as measured by real income. For this, our approach relies on Arkolakis, Costinot, and Rodriguez-Clare (2012), who show that in a wide class of micro-founded trade models leading to structural gravity equations, change in welfare (W) can be expressed from two sufficient statistics: changes in the share of expenditure on domestic goods (A) and in trade elasticity (e):

17The current version of the command does not incorporate tariff revenues.

Data are available at the sector level but are often not reliable as subtracting exports from output results in negative domestic consumption for many observations.

19Tariffs are aggregated by averaging across products.

Head and Mayer (2014) find a median elasticity of -3.78 in their meta-study of structural gravity estimates of trade elasticity. Simonovska and Waugh (2014) find an elasticity of -4.14.

To obtain the counterfactual share of expenditure on domestic goods and thus calculate A, the procedure proposed by Baier, Yotov, and Zylkin (2019) is used. Their method solves numerically for the general equilibrium effects of changes in trade policies by imposing market clearing on the gravity equation. Their routine is also implemented via ge_gravity.

D. Data

Bilateral trade. Bilateral product-level trade data are from the BACI database (footnote 1), which reconciles mirror trade flows reported in the United Nations (UN) Comtrade database. BACI reports data in US dollars for 5,020 products, defined using the 1992 version of the HS 6-digit classification. Missing values are set to 0 as BACI only reports trade exceeding $1,000. Finally, BACI reports trade flows as "free on board" (i.e., excluding freight, insurance costs, and tariffs).

Bilateral tariffs. Bilateral HS 6-digit level tariffs are from the Trade Analysis Information System database. This database converts most non-ad-valorem tariffs to ad valorem equivalents. Tariffs are set to the minimum of the resulting ad valorem MFN or preferential tariff, and converted to the 1992 version of the HS classification for matching with trade flows. Data on the EEU CET were provided by the Eurasian Economic Commission.

Domestic trade. Domestic manufacturing trade is computed as manufacturing output minus exports. Domestic output data are from the Industrial Statistics database, which reports output at the 2-digit industry level using the International Standard Industrial Classification Revision 3 classification.24 This data only covers manufacturing (codes 15-37), notably excluding agriculture and mining.

Customs unions. Data on customs unions is from the Economic Integration Agreements database (Baier, Bergstrand, and Feng 2014). The latest version from

21BACI attributes a weight to each reporter capturing its reliability. This weight increases with the proximity of a country's reporting to the reporting of its partners for the same flows. Trade flows are taken from the UN Comtrade Database, https://comtrade.un.org/ (accessed 1 February 2022).

22UN Conference on Trade and Development. Trade Analysis Information System, https:// databank.worldbank.org/source/unctad-%5E-trade-analysis-information-system-(trains) (accessed 1 February 2022).

An ad valorem tariff is set as a percentage of the price. The methodology to compute ad valorem equivalents covers specific tariffs (per unit); compound tariffs (combination of ad valorem and specific); and mixed tariffs (either ad valorem or specific, depending on certain conditions) (Stawowy 2021).

UN Industrial Development Organization. Industrial Statistics Database, https://stat.unido.org/ (accessed 1 February 2022).

25Data were also adjusted to incorporate EEU membership after 2012. The latest version of the database from July 2021 extends the coverage to 2018: The changes that are not incorporated in our estimation are Croatia joining the EU from an FTA in 2014, Honduras and Guatemala forming a customs union in 2016, and Colombia and Chile jointly entering a customs union with Mexico and Peru in 2016.

26For example, the data include trade between Montenegro and Italy, because Italy is among the 100 economies despite Montenegro not being one of them. The 100 economies include the following, identified by their respective International Organization for Standardization 3-digit codes: AGO, ARE, ARG, ARM, AUS, AUT, AZE, BEL, BGD, BGR, BLR, BOL, BRA, CAN, CHE, CHL, CIV, COD, COL, CRI, CYP, CZE, DEU, DNK, DOM, DZA, ECU, EGY, ESP, EST, ETH, FIN, FRA, GEO, GBR, GHA, GRC, GTM, HKG, HRV, HUN, IDN, IND, IRL, IRN, IRQ, ISR, ITA, JOR, JPN, KAZ, KGZ, KEN, KOR, KWT, LBN, LTU, LVA, MAC, MAR, MDA, MEX, MLT, MMR, MYS, NGA, NLD, NOR, NZL, OMN, PAK, PAN, PER, PHL, POL, PRT, QAT, ROU, RUS, SAU, SGP, SRB, SVK SVN, SWE, SYR, THA, TJK, TKM, TUR, TZA, UKR, URY, USA, UZB, VEN, VNM, ZAF; as well as the PRC.

See the "Trade Elasticities Estimated with Gravity Controls" and "Trade Elasticities Estimated with Pair Fixed Effects" spreadsheets at https://sites.google.com/site/juleshugot/research.

2017 reports trade agreements in force in all years from 1950 to 2012, classified across the depth of these agreements, including customs unions.25

Gravity dummies. Data on contiguity, common language (spoken by at least 9% of the population), colonial relationship post-1945, and population-weighted bilateral distances are from the gravity database of the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) (Head, Mayer, and Ries 2010).

Sample coverage. For the partial equilibrium tool, the final dataset covers 100 economies, including the pairs they form with any partner.26 These economies account for 99.5% of Armenia's total trade. Trade flows are matched with tariffs for 96.8% of Armenia's trade. The general equilibrium tool also requires domestic output data. The sample was thus restricted to manufacturing as domestic output is only available for manufacturing. Observations are excluded when internal trade (domestic output minus exports) is negative-possibly due to the underreporting of output. Finally, market clearing requires a balanced dataset. Together, these restrictions reduce the coverage to 60 economies for 2006-2018, accounting for 83% of Armenia's total trade during this period.

III. Product-Level Estimations of Trade Elasticity A. Distribution of Estimated Elasticities

The estimated elasticities for each HS 6-digit product are available online.27 Figure 2 shows the distribution of all the elasticities estimated through the preferred specification (see equation [6], with gravity controls). The estimations generate 5,014 elasticities, of which 82.4% are negative, as expected. Out of these, 73.7%> are

significant at the 10% level, 64.8% at the 5% level, and 49.9% at the 1% level.28 The distribution of elasticities across levels of significance are broadly similar, although they tend to be more distant from 0 at higher levels of confidence.

The elasticities that are negative and significant at the 10% level range from - 151.0 to -0.7. The median negative-significant elasticity is -7.0. This range broadly compares with those from Caliendo and Parro (2015) (-69.3 to -0.5) and Fontagne, Guimbard, and Orefice (2019) (-131.8 to -0.1).

The spreadsheets also provide the elasticities obtained with country-pair fixed effects (equation [7]). As it only relies on the variance of tariffs across time, this specification results in fewer significant elasticities. The paper obtains 4,942 elasticities, of which 65.0% are negative. Out of these, 58.1% are significant at the 10% level, 44.8% at the 5% level, and 25.1% at the 1% level. The distribution of these elasticities is shifted to the right, closer to 0 (Figure 3). The median negative-significant elasticity is -3.3, less than half its counterpart from the specification with gravity controls. This suggests that unobserved trade costs-those not controlled for in

The corresponding shares in Fontagne, Guimbard, and Orefice (2019) are 78% at the 10% level, 72% at the 5% level, and 61% at the 1% level.

the specification with gravity controls-are positively correlated with tariffs, biasing the elasticities with gravity controls downward.

The distribution of elasticities varies across sectors. Elasticities are the largest for commodities, such as precious stones and metals, and chemicals (Figure 4). This is consistent with suppliers for these products being easily replaced. Conversely, elasticities tend to be small for distinctive products such as foodstuff and beverages.

Positive-significant elasticities account for 4.0% of the elasticities obtained with gravity controls and 8.7% of those from the pair fixed-effect specification. These elasticities, however, concentrate in sectors where market forces are not the main driver of trade-including luxury goods such as pearls and watches-or in sectors where supply is concentrated such as rare metals used in the aerospace and nuclear industries. Tariff changes for products with positive or nonsignificant elasticities are assumed to have no impact on trade in the partial equilibrium tool.

B. Customs Union Effect

Controlling for customs unions (and any form of deeper trade integration) avoids that the tariff elasticities reflect the lower nontariff barriers associated with customs

unions. FTAs, however, are not explicitly controlled for. To the extent that new generation FTAs include nontariff features, such as public procurement and intellectual property rules, the positive correlation between tariff and nontariff features could potentially bias the estimated tariff elasticities. This is, however, unlikely to be a significant concern to the extent that new-generation FTAs only started becoming mainstream in the mid-201 Os, and they remain uncommon, while our estimation window is from 1995 to 2018. The median estimate for the customs union dummy is 0.8, which translates into a 123% boost to trade for the median products beyond the effect through tariff reductions. Other control variables also have the expected sign and magnitude for most products.

C. Coverage for Armenia

Figure 5 shows that the trade elasticities from the gravity-controls specification covered close to 60% of Armenia's trade since 2007, about twice the share covered by the elasticities from the pair fixed-effects specification. Figure 6 shows that the coverage is good for certain sectors-such as chemicals and plastics, and

29This implies that their impact is assumed to be entirely channeled through the tariff reductions observed in the data.

transportation-but is limited for precious stones and metals, and jewelry; and beverages, prepared food, tobacco.

IV. Changes in Armenia's Tariffs A. Eurasian Economic Union

The EEU was created on 1 January 2015 and included Belarus, Kazakhstan, and Russia. Armenia became a member on 2 January 2015 and the Kyrgyz Republic on 6 August 2015. The EEU is an economic union-that is, a form of deep economic integration that combines a customs union and a common market. As a customs union, the EEU involves duty-free trade among members, a CET, and common regulations to allow the entry of products.

The EEU builds upon the customs union of Belarus, Kazakhstan, and Russia, which had been in force since 2010. Isakova, Koczan, and Plekhanov (2016) analyze the early impact (2009-2010) of the customs union and find statistically significant negative effects-although modest in magnitude-on Kazakhstan's imports from the PRC. They conclude that tariff barriers might be less important than expected and suggest that the lowering of nontariff barriers within the EEU could bring net trade benefits from membership. Gnutzmann and Gnutzmann-Mkrtchyan (2020) examine the trade impact of the customs union before the creation of the EEU and isolate tariffs from nontariff impacts. They find that it led to a 35% increase in trade within the customs union relative to external partners, with 20% due to tariff increases for outside partners and 15% due to lower nontariff barriers within the customs union.

Armenia initially received exemptions from the EEU CET for 772 products. These products account for 6.3% of tariff lines but 38.0% of Armenia's imports from outside the EEU (WTO 2018). These exemptions were justified by Armenia's low tariffs before joining the EEU. A transition schedule specifies the pace of the convergence until January 2022. Most tariff changes took place in January 2015, but changes also occurred in succeeding years, particularly on 1 January 2020. Tariff increases due to the convergence process affected mostly vehicles in 2020, pharmaceuticals and textiles in 2021, and live animals in 2022. Further complexity in the convergence process comes from changes in the CET itself. These changes

See Vinokurov (2017) for a detailed overview of the EEU timeline.

31 See WTO (2018) for a detailed discussion on Armenia's exceptions and convergence to the CET.

32Average tariffs are calculated across the tariff lines for which Armenia records imports for at least 1 year during 2008-2017.

notably enable Russia to comply with commitments made to lower its tariffs upon its WTO accession in 2012. The largest changes in tariffs took place in 2015 as the average tariff increased from 3.7% to 7.0% (Figure 7).32 Armenia's average tariff then decreased during 2016-2017 as the CET converged to the bound tariffs that Russia committed to in the framework of the WTO, but it increased again in 2018 as tariffs further converged to the CET. The trade-weighted average tariff did not increase as much as the simple average until 2017, as the largest tariff increases hit products with low import volumes in Armenia. The trade-weighted average tariff got closer to the simple average tariff in 2018 as Armenia's tariff continued its alignment with the EEU CET.

The EEU CET has also made Armenia's tariff structure more complex. The share of duty-free tariff lines fell from 62% to 17% with the EEU accession. While 99.6% of tariffs were either 0% or 10% before 2015, they took many more values as of 2022, with tariff peaks at 0%, 5%, 10%, and 15% (Figure 8).

The convergence to the CET affects product categories differently (Figure 9). Tariff increases for animals and animal products; arms and ammunition; and precious stones and metals, and jewelry are particularly striking. On the other hand, tariffs declined for vegetables, footwear, and machinery.

B. Other Trade Agreements Affecting Armenia

Armenia had FTAs in place with all EEU members before 2015 (Table 2). This included bilateral FTAs with Belarus, Kazakhstan, and the Kyrgyz Republic, as well as membership in the Commonwealth of Independent States FTA, which ensured free trade with Russia. These FTAs were superseded by the EEU.

The FTAs with other countries in force before EEU accession remain in place as permitted by the EEU Treaty. This enables Armenia to maintain duty-free trade with Georgia, Moldova, Tajikistan, Turkmenistan, and Ukraine.

Armenia and the EU signed the Comprehensive and Enhanced Partnership Agreement on 24 November 2017, and some chapters have been applied since 1 June 2018. The agreement aims to strengthen cooperation but does not involve preferential tariffs. This makes it significantly different from the deep and comprehensive FTAs signed by the EU with Georgia, Moldova, and Ukraine.

Besides having been a beneficiary of the EU's GSP+ scheme until 31 December 2021, Armenia also benefits from GSP programs provided by Canada, Japan, Norway, Switzerland, and the US.

C. Trade Agreements between the Eurasian Economic Union and Other Economies

The EEU has FTAs with Viet Nam (since October 2016) and with Serbia (since July 2021). It signed an FTA with Singapore in 2019, but the agreement is not yet in

force. The EEU is also negotiating FTAs with Egypt, India, and Israel, and has initiated discussions with Bangladesh, Cambodia, Chile, Ecuador, Indonesia, Jordan, Mongolia, Peru, the Republic of Korea, and Thailand.

The empirical section assesses the impact of hypothetical FTAs that the EEU might sign with Iran and the PRC. With Iran, an interim agreement came into force in October 2019 for 3 years and was then extended to 2025. This period allows for negotiations for an FTA, while the interim agreement reduces tariffs for 4% of tariff lines but does not eliminate them.33 A Trade and Economic Cooperation Agreement with the PRC came into force in October 2019. This agreement increases the transparency of regulations and simplifies trade procedures but does not lower tariffs.34

Although EEU membership increases tariffs on imports from other economies, it may also create new trading opportunities for Armenia. From the point of view of potential FTA partners, a trading bloc, such as the EEU, is more attractive than a small economy like Armenia for negotiating an FTA. The larger market size of the EEU can also increase bargaining power in FTA negotiations.

V. Impact of Tariffs Changes on Armenia

This section reports the estimated impact of three actual or potential series of tariff changes affecting Armenia. These scenarios include (i) full convergence to the EEU CET by 1 January 2022; (ii) FTAs between the EEU and Iran, and between the EEU and the PRC; and (iii) the loss of eligibility for the EU's GSP and GSP+ preferential tariff schemes.

This empirical section reports general equilibrium results (aggregated across products and limited to manufacturing) and partial equilibrium results (product-level detail). For the partial equilibrium tool, tariff changes are assumed to have no impact on trade if the tariff elasticity is not significant at the 5% level.

The changes are computed using baseline data for 2018-the last year for which all the required data are available. For the EEU CET convergence scenario, this means that both pretreatment trade (in 2014) and posttreatment trade (in 2022) are computed

Annex 1 of the interim agreement specifies tariff reductions by the EEU for 502 tariff lines and by Iran for 360 tariff lines. The EEU-Iran FTA was eventually signed on 25 December 2023 (EEU. https://eec. eaeunion.org/upload/medialibrary/77b/FTA-EAEU Iran.pdf, accessed 10 January 2024). It eliminates tariffs for 87% of products and lowers them for many others.

34See ADB (2021) for more details on preexisting bilateral tariffs between the EEU and potential FTA partners. Prior to FTA negotiations (and the interim agreement, in the case of EEU-Iran trade), trade took place without special preferences.

using equations (8) and (9). The trade impact is thus the difference between two counterfactual scenarios, both based on trade patterns as of 2018.

For the two FTA scenarios, the impacts are estimated assuming duty-free trade for all goods. The scenario for the EEU-Iran FTA thus goes beyond the interim agreement already in place. For the last scenario (loss of EU's GSP and GSP+ eligibility), EU tariffs imposed on Armenia's exports are assumed to revert to the MFN level as of 2018.

A. Convergence to the Eurasian Economic Union Common External Tariff 1. General Equilibrium Welfare and Trade Impact

Results suggest that the convergence to the EEU CET will decrease Armenia's welfare by 1.6% (Figure 10). The largest impact comes from the tariff changes that occurred before 2019 (-1.42%). The tariff changes of January 2021 also had a negative impact-although more moderate (-0.11%)-while the changes that occurred in 2020 and 2022 are expected to have minimal impacts. To obtain the phase-in effects, we first calculate the impact relative to the baseline year (2018) of two different years. And then calculate the incremental change as the difference between two counterfactuals.

As converging to the CET requires increases to most tariffs, it is estimated that there will be a decrease in manufacturing imports from outside the EEU and Armenia's FTA partners by $145 million. This decrease, however, is expected to be partially compensated for by a $92 million increase in manufacturing imports from EEU and FTA partners: This is the trade diversion effect. Convergence to the CET is thus expected to result in a $54 million net decline in manufacturing imports, which constitutes 1.5% of manufactured imports. As for welfare, most of the trade impact is driven by the tariff changes that occurred during 2015-2019, particularly in 2015.

In terms of partners, manufacturing imports from the EU are expected to decrease by $59 million, followed by the PRC (-$27 million) and Turkiye (-$16 million) (Figure 11). The beneficiaries from import diversion are expected to be the Russian Federation ($73 million) and Ukraine ($12 million).

2. Partial Equilibrium Impact

The partial equilibrium impact of the convergence to the CET is one order of magnitude higher than the general equilibrium impact at $592 million (-12% of Armenia's total imports). This larger impact is due to a combination of factors. First, the general equilibrium analysis is restricted to manufacturing, which only accounts for about two-thirds of Armenia's imports. Second, the general equilibrium tool gauges imports diversion, which mitigates import reductions from economies other than EEU members and Armenia's other FTA partners. Third, the partial equilibrium tool relies on product-level tariff elasticities, which may magnify the overall trade impact compared to the general equilibrium tool, which relies on a single, moderate elasticity.

Results suggest that imports of chemicals, plastics, and rubber should decline the most, by $201 million (-25%), followed by transport equipment at $169 million (-36%) (Figure 12). Analysis at the product level shows that imports of medicaments should be particularly affected (-$51 million), as well as imports of plastics, notably those used as containers for food and beverages (-$28 million). Imports of prefabricated buildings-including greenhouses-should also fall by $23 million (-30%). Within transport equipment, imports of diesel trucks should fall by $18 million (-62%). Imports of television sets, cement, and coffee are expected to increase. These increases arise as tariffs on these products will be lower in 2022 than

they were in 2014. These impacts, however, are limited: $19 million for television sets, $8 million for cement, and $7 million for coffee.

B. Free Trade Agreement between the Eurasian Economic Union and Iran

Armenia runs a trade deficit with Iran, but exports have increased rapidly since 2015. Mineral products accounted for 48% of imports ($121 million) in 2018, including natural gas ($71 million), cement ($21 million), and crude oil ($13 million). Lamb accounted for 79% of Armenia's exports to Iran ($14 million) in 2018 and has driven the increase in bilateral exports since 2015.

1. General Equilibrium Welfare and Trade Impacts

The general equilibrium analysis shows that an FTA with Iran would increase Armenia's welfare by 0.3% (Figure 13). Armenia would reap the largest benefits among EEU members, far ahead of other members. Manufacturing exports to Iran would increase by $22 million (125.2%) at the expense of a modest reduction in exports to Russia (-$2 million) and the rest of the world (-$6 million).

Manufacturing imports from Iran would increase by $42 million (26.0%), also partly at the expense of imports from Russia (-$10 million) and the rest of the world (-$18 million). As for other general equilibrium exercises in this paper, however, these results rely on data for manufactured goods only. Given the heavy role of mineral products and natural gas in Iran's exports, this narrow focus may underestimate the potential impact of an EEU-Iran FTA.

2. Partial Equilibrium Impact

The partial equilibrium tool suggests that exports from Armenia to Iran would be multiplied by 2.6, from $18 million to $56 million. The impact of the convergence to the EEU CET from the partial equilibrium tool is about two times larger than the impact obtained from the general equilibrium tool. Armenia's imports from Iran would increase by $90 million (37%), which is more than twice the $42 million general equilibrium impact.

Armenia's exports would particularly increase for transportation equipment (Figure 14). Product-level analysis shows that the impact would be entirely for small trucks due to Iran's elevated tariff for this product (40%). Exports would also increase for chemicals and plastics, and-to some extent-for foodstuff (mostly chocolate products and biscuits). Armenia's imports would also increase by $43 million for mineral products and base metals, including $19 million for petroleum bitumen

(asphalt). Imports would also increase by $36 million for chemicals and plastics, including polyethylene ($16 million), a plastic used in food containers.

C. Free Trade Agreement between the Eurasian Economic Union and the People's Republic of China

Armenia runs a trade deficit with the PRC. Machinery accounted for 45% of its imports from the PRC in 2018, followed by textiles and footwear (16%). Copper accounts for most of Armenia's exports to the PRC ($95 million in 2018), with the share of textiles and footwear increasing since 2016.

1. General Equilibrium Welfare and Trade Impacts

The general equilibrium analysis indicates that an FTA with the PRC would increase Armenia's welfare by 0.7%-more than twice the impact of an FTA with Iran (Figure 15). Among EEU members, the Kyrgyz Republic would reap the largest welfare gains (2.9%), followed by Kazakhstan (1.1%).35 Armenia's manufacturing exports to the PRC would increase by about 40.5% ($12 million), largely at the expense of exports to Russia (-$8 million). Manufacturing imports from the PRC would increase by 20.4% ($98 million), largely at the expense of imports from the rest of the world (-$64 million).

2. Partial Equilibrium Impact

As in previous cases, the partial equilibrium trade impact exceeds the general equilibrium impact. Armenia's exports to the PRC would increase by 38% ($48 million), against 10% in the general equilibrium. At the same time, imports from the PRC would increase by 32% ($155 million), against 20% in the general equilibrium.

Armenia's exports would mostly increase for transport equipment (Figure 16), entirely accounted for by medium-sized cars ($46 million). This might, however, be an artifact as the counterfactual exercise relies on 2018 as the baseline when Armenia's reexports of used cars were abnormally large. The increase in imports would be more diversified, starting with machinery ($53 million), including television sets ($24 million), refrigerators ($4 million), and bicycles and washing machines ($3 million each). Imports would also increase for chemicals, plastics, and rubber

'The PRC accounts for 23% of Kazakhstan's imports and 53% of the Kyrgyz Republic's imports.

($25 million); and transport equipment ($19 million), including mid-sized cars ($6 million) and tires ($5 million).

D. Loss of Eligibility for the European Union's Generalised Scheme of Preferences

Armenia imports a wide range of products from the EU, but it mostly exports copper, both nonmanufactured ores and manufactured metal. The bilateral trade balance with the EU remains in deficit despite increased copper exports since 2015. Imports from the EU fell during 2015-2016 as GDP growth slowed in Armenia but then quickly recovered. This recovery in imports was particularly strong for industrial machinery, notably to support the development of the domestic agribusiness sector.

1. General Equilibrium Welfare and Trade Impacts

The general equilibrium tool indicates that the loss of eligibility for the EU's GSP and GSP+ will decrease Armenia's welfare by 0.1% and its manufacturing exports to the EU by 11.5% (-$35 million) (Figure 17). Exports to Germany will be the most affected (-$23 million), followed by exports to Italy (-$6 million). Part of the foregone exports to the EU should be reallocated to other markets, notably Russia. In terms of net impact, Armenia's manufacturing exports are expected to decline by 0.9% (-$17 million).

This minimal impact is mostly due to the limited tariff reductions provided by GSP+, given the product composition of Armenia's exports to the EU. Although it is not a manufactured good-and thus excluded from the general equilibrium analysis- copper accounted for 45% of Armenia's exports to the EU in 2018, and the EU does not impose tariffs on copper. The EU also does not impose tariffs on raw copper, which accounts for 7% of Armenia's bilateral exports. For ferromolybdenum alloys (23% of Armenia's exports to the EU), GSP+ only provides a minimal advantage as the EU's MFN tariff is 2.7%. The moderate effect of GSP+ on exports to the EU thus mostly arises from aluminum foil and apparel, which are also significant bilateral exports.

2. Partial Equilibrium Impact

The partial equilibrium tool suggests that the loss of GSP+ eligibility should reduce Armenia's exports to the EU by 19% (-$156 million), an impact slightly larger than the reduction in manufacturing exports from the general equilibrium tool (-12%).

Exports of metals-ferromolybdenum and aluminum foil-should decline the most (-$139 million), followed by textiles (-$16 million) (Figure 18). These products all make up a large share of Armenia's exports to the EU and exports are either very elastic to tariff increases (e.g., ferromolybdenum alloy has a tariff elasticity of -21) or will face high tariffs once Armenia's exports are subject to the EU's MFN (e.g., the EU's MFN tariff for nonknitted clothing is 12%).

VI. Conclusion

The general and partial equilibrium tools used in this paper are both derived from the structural gravity literature, and they complement each other. The general equilibrium tool quantifies the impact of tariff changes on welfare and trade, including trade diversion from or to partners with which tariffs have not been modified. Data limitations do not allow the assessment of impacts at the product level; the resulting impacts only pertain to tariffs affecting trade in manufactured goods. The partial equilibrium tool also only allows quantifying the direct trade impact of tariff changes, but this impact can be broken down across 5,020 products.

As an intermediate input into the partial equilibrium tool, product-level trade elasticities are estimated using two specifications. The elasticities from the specification with country-pair fixed effects are novel, while the elasticities from the specification with explicit gravity controls align with other elasticities from the structural gravity literature. Given the better product coverage of the latter, the partial equilibrium tool relies on those.

When applied to four scenarios of tariff changes affecting Armenia, these tools allow the following conclusions. The convergence of Armenia's tariffs to the EEU CET by 1 January 2022 should have reduced Armenia's welfare by 1.6%, given that the EEU further constrains Armenia's access to foreign suppliers. Market access within the EEU does not improve, however, as Armenia already traded with all other members on a duty-free basis. This assessment only considers the impact of the EEU through tariff changes, excluding changes that may affect nontariff barriers and FTA opportunities within a larger trading bloc. In terms of trade impact, the CET is expected to reduce Armenia's manufacturing imports by 1.5% (general equilibrium tool) and total exports by 12.3% (partial equilibrium tool). The impact is expected to be concentrated in Armenia's imports from the EU, the PRC, and Turkiye; chemicals and plastics; and transportation equipment.

An EEU-Iran FTA would increase Armenia's welfare by 0.3%. General equilibrium results indicate that manufacturing exports would increase by 0.7% as

they would more than double to Iran. Manufacturing imports would increase by 0.4%, with imports from Iran rising by 26.0%. Partial equilibrium results suggest that exports would mostly increase for transportation equipment, and chemicals and plastics, and that imports would mostly increase for chemicals and plastics, and mineral products. An EEU-PRC FTA would increase Armenia's welfare by 0.7%. General equilibrium results indicate that manufacturing exports would increase by 0.6%, as they would increase to the PRC by 40.5%. Manufacturing imports would increase by 0.3%, with imports from the PRC rising by 20.4%. Partial equilibrium results suggest that exports would mostly increase for transportation equipment and that imports would mostly increase for machinery. These two potential FTAs alone would cancel out close to two-thirds of the negative welfare impact of the EEU. This suggests that the welfare impact of the EEU might be positive in the longer term to the extent that it provides opportunities to improve Armenia's market access through FTAs negotiated by the EEU itself. Given the structure of Armenia's trade, liberalizing trade between the EEU and its partners, such as Iraq and Switzerland, would also bring significant gains. Finally, the loss of eligibility for the EU's GSP scheme should reduce welfare in Armenia by about 0.1%, decreasing manufacturing exports by 0.9% (general equilibrium) and total exports by 3.3% (partial equilibrium), and decreasing manufacturing exports to the EU by 11.5% (general equilibrium) and total exports to the EU by 18.7% (partial equilibrium). ORCID Arevik Gnutzmann-Mkrtchyan https://orcid.org/0000-0002-4222-6177 Jules Hugot (c) https://orcid.org/0000-0002-6029-6611

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