metadata: authors: 0: author: Chan,H. Ron
1: author: Manderson,Edward John Matlock
2: author: Zhang,Fan
count: World
docna: 0: docna: Energy prices and international trade :
            incorporating input-output linkages
docty: Policy Research Working Paper
subtopic: Construction Industry,Oil Refining & Gas Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Energy Policies & Economics,International Trade and Trade Rules,Energy Demand,Energy and Environment,Energy and Mining,Business Cycles and Stabilization Policies
teratopic: Energy,Environment,Poverty Reduction,Macroeconomics and Economic Growth,Social Protections and Labor
abstracts: cdata!: This paper examines the effect of energy
            costs on industry export competitiveness. Most studies in
            the literature use direct energy consumption (energy
            consumption at the final stage of production) and domestic
            energy prices to compute energy costs faced by domestic
            industries. Using multi-country input-output information,
            this study measures the effect of aggregate energy costs on
            export performance, where aggregate energy costs include not
            only direct energy costs, but also indirect energy costs
            passed on through the upstream supply chain. This study
            develops a theoretical trade model that incorporates
            tradable intermediate goods to inform its empirical
            strategy. It then estimates a reduced-form model using a
            panel data for 10 manufacturing sectors in 43 countries from
            1991 to 2012. The analysis finds that ignoring input-output
            relationships can lead to significant over- or
            underestimates of the effect of energy price shocks on
            exports, depending on intermediate factor intensities and
            trade relationships. Using estimated trade elasticities, the
            study simulates the economic consequences of energy
            cross-subsidies and carbon taxes. The results show that
            energy cross-subsidies that raise energy tariffs on industry
            to support lower rates for households and farmers in India
            could reduce the country's net manufacturing exports by
            $6.1 billion a year. Similarly, a carbon tax that
            unilaterally increases energy prices by 10 percent in the
            European Union could reduce European Union-wide net
            manufacturing exports by 1.9 percent annually.
repnme: repnme: Energy prices and international trade :
            incorporating input-output linkages
display_title: Energy prices and international
            trade : incorporating input-output linkages
listing_relative_url: /research/2017/05/27521463/energy-prices-international-trade-incorporating-input-output-linkages
topicv3: Manufacturing Sectors m1326521 1281,Energy and Extractives m1326595 507,Trade m1326924 698,Power and Electricity Sector m1327094 515,Natural Gas m1583894 4712
publishtoextweb_dt: 2017-05-30T00:00:00Z
historic_topic: Energy,Environment,Poverty Reduction,Macroeconomics and Economic Growth,Social Protections and Labor
no_of_pages: 43
author_keys: 0: author_key: 1483173
1: author_key: 905148
model_extractions: WPS8076 Policy Research Working Paper 8076 Energy Prices and International Trade Incorporating Input-Output Linkages _H. Ron Chan Edward Manderson Fan Zhang_ South Asia Region Office of the Chief Economist May 2017
Policy Research Working Paper 8076

# **Abstract**

This paper examines the effect of energy costs on industry export competitiveness. Most studies in the literature use direct energy consumption (energy consumption at the final stage of production) and domestic energy prices to compute energy costs faced by domestic industries. Using multi-country input-output information, this study measures the effect of aggregate energy costs on export performance, where aggregate energy costs include not only direct energy costs, but also indirect energy costs passed on through the upstream supply chain. This study develops a theoretical trade model that incorporates tradable intermediate goods to inform its empirical strategy. It then estimates a reduced-form model using a panel data for 10 manufacturing sectors in 43 countries from 1991 to 2012. The analysis finds that ignoring input-output relationships can lead to significant over- or underestimates of the effect of energy price shocks on exports, depending on intermediate factor intensities and trade relationships. Using estimated trade elasticities, the study simulates the economic consequences of energy cross-subsidies and carbon taxes. The results show that energy cross-subsidies that raise energy tariffs on industry to support lower rates for households and farmers in India could reduce the country’s net manufacturing exports by $6.1 billion a year. Similarly, a carbon tax that unilaterally increases energy prices by 10 percent in the European Union could reduce European Unionwide net manufacturing exports by 1.9 percent annually.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Energy Prices and International Trade: Incorporating Input-Output Linkages**<sup>_∗_</sup>

H. Ron Chan<sup>_†_</sup> Edward Manderson<sup>_‡_</sup> Fan Zhang<sup>§</sup> University of Manchester University of Manchester World Bank Keywords: Energy cross-subsidization, carbon leakages, energy costs, exports, competitiveness, input-output linkages JEL Classifications: Q4, Q5, F1

> _∗_ This paper is part of a broader analytical effort to assess the cost of power sector distortions in South Asia being conducted by the Office of the Chief Economist of the World Bank South Asia Region. We thank Vivien Foster, Martin Rama, Yadviga Viktorivna Semikolenova, Govinda Timilsina, Maria Vagliasindi, and seminar participants at the University of Toulouse for helpful comments on earlier drafts. Jiarui Wang provided excellent research assistance. Financial support from the Trade and Competitiveness Multi-Donor Trust Fund and the Partnership for South Asia Trust Fund is greatly appreciated.

> _†_ School of Social Sciences, University of Manchester, Arthur Lewis Building-3.078, Oxford Road, Manchester, M13 9PL, United Kingdom. Contact: ron.chan@manchester.ac.uk

> _‡_ School of Social Sciences, University of Manchester, Arthur Lewis Building-3.078, Oxford Road, Manchester, M13 9PL, United Kingdom. Contact: edward.manderson@manchester.ac.uk

> §World Bank, 1818 H Street NW, Washington, DC, USA. Contact: fzhang1@worldbank.org
# **1 Introduction**

Changes in energy prices are usually thought to have implications for the competitiveness of economies. Policies leading to higher energy prices therefore tend to be subjected to close scrutiny and much debate. Prominent examples include climate change policies such as environmental taxes and cap and trade systems. There are concerns that these policies, when unilaterally enforced, could hinder the ability of domestic industry to compete in export markets, especially in the short term and for energy-intensive sectors. Through their effects on energy prices, they may even induce firms to shift production to countries where emission constraints are less stringent, a response that may lead to an increase in global carbon emissions – the so-called carbon leakage effect. Another policy that may lead to unintended consequences for trade through energy price adjustments is energy crosssubsidization. Many countries, especially in Eastern Europe and South Asia, impose higher energy tariffs on industry to support lower rates for residential or agricultural consumers. Energy cross-subsidies may therefore hamper the international competitiveness of industries, although this has received little attention in the discussion on the economic incidence of energy subsidies.

In this paper, we examine the effect of energy costs on manufacturing exports.<sup>1</sup> Most studies in the literature have used direct energy consumption (energy consumption at the final stage of production) and domestic energy prices to compute energy costs faced by domestic industries. We contribute to the literature by considering both direct and indirect energy costs (energy consumption at intermediate stages of production). Taking into account indirect energy costs is important because even if a sector is not energy intensive on the basis of direct energy consumption, it may still be vulnerable to energy price shocks if it uses energy-intensive intermediate inputs. Furthermore, a sector that uses imported intermediate goods may also be indirectly affected by energy price changes in the origin countries.

Our paper relates to two strands of empirical literature. One strand consists of a small number of recent studies that analyze the relationship between energy prices and international trade using econometric techniques. For example, Aldy and Pizer (2015) use historical electricity price data for the United States to investigate how production and net imports

> 1Changes in energy prices could also affect transport costs, which in turn has implications for international trade flows (Bridgman, 2008; Chen and Hsu, 2012). Analysis on this aspect is beyond the scope of this paper.

2
change in response to energy prices. At the global level, Sato and Dechezleprêtre (2015) use energy price indices from 42 countries during 1996–2011 to study the impact of changes in energy price differences between trading partners on trade flows. Based on direct energy costs alone, these studies find a significant yet small negative effect of higher energy costs on export performance.

Using input-output information, another strand of literature on the trade-energy nexus emphasizes the importance of accounting for the indirect energy consumption of intermediate inputs. For example, Bordigoni, Hita and Le Blanc (2012) find that the energy content embodied in imported intermediate inputs is close to the total direct energy consumption of European industry. A significant part of the embodied energy costs of manufactured products in Europe is therefore not affected by domestic energy price changes. Sato et al. (2015) evaluate the impact of embodied energy on energy security. They find that the geographic diversity of embodied energy imports is much greater than that of direct energy imports, and that there is considerable variation across countries in the diversification of embodied energy imports.

This paper builds on both strands of literature by combining an econometric model of international trade with multi-country input-output information to measure the effect of energy costs aggregated over all stages of production on export performance. We first develop a theoretical framework based on a Melitz-type trade model in which each country produces one variety in each sector and there are multiple countries and sectors with heterogeneous productivity. We incorporate tradable intermediate goods in the model to inform our empirical strategy. We then compute the embodied cost (direct plus indirect cost) of factor inputs (electricity, natural gas and labor) using the OECD inter-country input-output tables for 10 sectors of production. The key parameters of interest are estimated by Poisson pseudo maximum likelihood using panel data for 43 countries from 1991 to 2012. We control for exporter-by-importer fixed effects, exporter-year fixed effects, importer-year fixed effects and sector-year fixed effects to sweep out variations due to bilateral trade resistance (such as distance, and differences in language and culture) and time-varying shocks at country and sector levels (such as exchange rate fluctuations and regulatory and technology changes). The causal effect of energy costs on trade is identified by country-sector-year variation including differences in electricity prices, sector-specific energy intensity, and inter-country 3
inter-sectoral production linkages. We also conduct various sensitivity analyses, such as considering alternative levels of sector disaggregation, accounting for the delayed response of trade to changes in input costs, allowing for heterogeneous effects of energy costs based on characteristics of the trading partners, and controlling for country-sector-year fixed effects to avoid potential bias from possible omitted variables (such as adjustment in firms’ markup).

We find a robust and asymmetric effect of the energy costs of exporting and importing countries on trade. A one percent increase in the exporter’s (importer’s) aggregate electricity costs is associated on average with an approximately 0.07 percent decrease (0.10 percent increase) in exports for a sector of average electricity intensity. Aggregate natural gas costs also have a significant impact on trade, although of a smaller magnitude. Using the estimated trade elasticities, we simulate the economic consequences of energy cross-subsidies in Bulgaria and India and those of a carbon tax in the European Union. We find that energy cross-subsidies imposing a 15 percent implicit electricity tax on industry in India could reduce its net manufacturing exports by about $6.1 billion a year. Similarly, a carbon tax unilaterally increasing energy prices by 10 percent in the European Union could reduce EU-wide net manufacturing exports by 1.9 percent annually.

Our results suggest that input-output relationships are important for understanding the energy-trade relationship. Some sectors use significantly more indirect energy (at intermediate stages) than direct energy. For these sectors, when intermediate goods are mostly non-traded, a country’s own energy price change may have a substantial multiplier effect on its exports through domestic sectoral linkages. A case in point is India’s machinery sector, whose indirect energy intensity is almost five times its direct energy intensity. Because India is largely self-sufficient in intermediate inputs, a model that ignores inter-sectoral linkages would underestimate the impact of higher energy costs on the net exports of the country’s machinery sector by a factor of almost four. Conversely, if indirect energy is mostly imported, changes in domestic energy prices would be relatively less important for a sector’s exports. This is the case for Bulgaria. For an EU-wide carbon tax, we find that a model accounting only for direct energy consumption would underestimate the impact of higher energy prices on EU-wide net manufacturing exports by a factor of two. This reflects the intensive trading of intermediate goods within the European Union, which increases each 4
sector’s exposure to higher energy prices through regional multiplier effects.

The rest of this paper is organised as follows. Section 2 outlines the theoretical framework. Section 3 discusses the empirical approach and data. Section 4 presents empirical and simulation results. Section 5 concludes.

# **2 Theoretical Framework**

The objective of this section is to motivate an empirical model in which we can test the effect of energy prices on trade. We build a Melitz-type model in which there are multiple countries and sectors, and in which firms are heterogeneous in productivity. Most of the discussions here follows Eaton and Kortum (2002); Anderson and van Wincoop (2003, 2004); Chaney (2008); Arkolakis, Costinot and Rodríguez-Clare (2012); Costinot, Donaldson and Komunjer (2012); Head and Mayer (2014); Costinot and Rodríguez-Clare (2014); and Caliendo and Parro (2015). We outline the basic model with multiple sectors (and factors) and then move on to include tradable intermediate goods.

## **2.1 Many Sectors and Factors**

We consider a partial equilibrium framework in which there are _N_ countries and _K_ sectors, and we treat input prices as exogenous. In the following, subscript _i_ ( _j_ ) refers to country _i_ ( _j_ ) and subscript _k_ to sector _k_ . Following Costinot, Donaldson and Komunjer (2012), Costinot and Rodríguez-Clare (2014), and others, the representative consumer has a twotier utility function in which the upper tier follows Cobb-Douglas and the lower tier follows constant elasticity of substitution (CES) utility function. Formally, the utility function for the representative consumer in country _j_ is given by: where: 5
where _βk_ is the income share of sector _k_ , _ω_ represents each variety, Ω is the entire variety space, and _σk_ is the elasticity of substitution for varieties within sector _k_ . Each firm needs to pay a fixed cost for each variety, has increasing returns, and has no extra cost to diversify horizontally. Therefore, firms in each country produce that country’s own variety; in other words, each variety is sourced from one country only. We can therefore rewrite (2) as: where Ω _ij_ , _k_ is the variety space in sector _k_ that country _j_ imports from country _i_ ; both Ω and Ω _ij_ , _k_ are endogenously determined, as described below. Assume that the representative consumer in country _j_ receives an income of _Yj_ . The total value of exports (of good _k_ with variety _ω_ ) from country _i_ to country _j_ is given by: where _Pj_ , _k_ is the price index for sector _k_ in country _j_ , defined as: and: where _pij_ , _k_ ( _ω_ ) is the price charged by each firm _ω_ .

Each firm pays a fixed cost of exports (and production) _fij_ , and a variable cost of production depends on the cost of _M_ factors. In equilibrium, each firm chooses to export to only one country because of increasing returns. The total cost for a firm in country _i_ , producing<sup>2</sup> quantity _q_ and shipping to country _j_ , is given by: where _m_ = 1, 2, ..., _M_ denotes each primary factor; _τ_ is iceberg transport cost between _i_ and

> 2 The resulting cost function (7) is an equilibrium product of a producer with a Cobb-Douglas production function.

6
_j_ ; _φ_ is the productivity term drawn from a continuous distribution function _Gk_ ( _φ_ ); _αm_ , _k_ is the factor intensity in _m_ , which is allowed to differ from sector to sector and meets the condition ∑ _m αm_ , _k_ = 1; _wm_ , _i_ is the input price for factor _m_ in country _i_ ; and the firm is assumed to be a price taker in the factor markets. Assume that all factors are used in a perfectly competitive, nontradable, homogenous-good sector in all countries and that the returns to factors, _w_ , are predetermined. Define _Wi_ , _k ≡_ ∏ _m_<sup>_M_</sup> =1<sup>_w_</sup> _m_<sup>_αm_</sup> , _i_<sup>,</sup><sup>_k_asthemarginalcost.Sincethedemandfor</sup> each variety in sector _k_ is isoelastic (with price elasticity of _σk_ ), monopolistically competitive firms price a markup over marginal cost: Following Helpman, Melitz and Yeaple (2004), Chaney (2008), and Costinot and RodríguezClare (2014), we assume that firms’ productivity follows a Pareto distribution: where _γk_ is the shape parameter. Because of the presence of a fixed cost of exporting, lowproductivity firms will choose not to produce and export (extensive margins). Applying the demand and price functions described in equations (4) and (8) and setting profits equal to zero, we therefore have the cutoff productivity: where _Aσk_ is a parameter that depends on _σk_ only. The _aggregate_ exports from country _i_ to country _j_ in sector _k_ are given by: Plugging equations (4), (8), (9) and (10) into equation (11), we have: 7
Taking a logarithmic transformation on both sides gives:<sup>3</sup> The first term on the right-hand side of equation (13) takes into account the demand-side factors while the fourth term takes into account the bilateral trade cost between countries _i_ and _j_ . We hope to control for these two factors using with the first factor controlled by an importer-year fixed effect and the last one by an exporter-importer fixed effect. We will explain more on this in Section 3. The second term, log _Wi_ , _k_ , takes into account the input costs facing sector _k_ in country _i_ . Namely: which is the main variable of interest in our empirical model.

The third term, log _Pj_ , _k_ , represents the multilateral resistance term first defined in Anderson and van Wincoop (2003). The price index in an importing country, as illustrated in equation (5), depends on the prices (and thus costs and trade barriers) of country _j_ and all other countries. In the empirical model, we use factor input costs of importing countries as a proxy for log _Pj_ , _k_ . Note that even though the model suggests that the coefficients of log _Wi_ , _k_ and log _Pj_ , _k_ have the same size (and opposite signs), the effects of exporters’ and importers’ energy costs on trade are likely to differ, because we are using importers’ costs to approximate for the price index. Our empirical model described in Section 3 allows for asymmetric effects of exporters’ and importers’ costs on trade.

# **2.2 Intermediate Goods**

In the previous subsection, each sector uses a bundle of factors at different factor intensities. However, there are no linkages among sectors. Each sector produces its good using _M_ factors and each sector can be analyzed separately. In this subsection, we relax this restriction and allow each sector to use all the factors as well as intermediate goods from other sectors. Following the literature, we assume that there are a number of perfectly competitive firms

> 3log( _Aσs_ , _γs_ ) is a constant that captured by sector(-by-year) fixed effect. It is omitted in equation (13) to simplify the notation.

8
that produce a composite intermediate good _Hk_ . The aggregation of this intermediate good _Hk_ originated from sector _k_ is produced in exactly the same way as the final consumption good from sector _k_ , that is: The composite intermediate good producer in country _i_ that produces _H_ by procuring intermediate goods (domestically and abroad) has the following demand for each variety of goods: Here the price index _Pi_ , _k_ represents both the price index for the composite intermediate goods and the price index for final (consumption) goods, as the same set of firms export their varieties to the composite intermediate goods producer and to country _i_ for final goods. Following the literature, we also assume that the producer of a variety produces the same proportion of that variety for both final goods and intermediate goods. The production of a variety in sector _k_ depends on all _M_ factors and all intermediate goods from the other _K_ sectors. Therefore, in the presence of intermediate goods, the total production and export cost, described in equation (7), is modified as follows: The assumption on intermediate goods aggregation leads to the same price elasticity of demand for intermediate goods as that for final goods. We therefore have the same format of markup-over-marginal-cost relationship as described in equation (8); the only difference is that marginal cost ( _Wi_ , _k_ ) now also depends on the price of intermediate goods in the form of a Cobb-Douglas cost function.

After incorporating intermediate goods, the estimation model will look almost exactly like equation (13) except that we will now replace _Wi_ , _k_ , which represents the direct costs of primary inputs, with _W_<sup>�</sup> _i_ , _k_ , which represents the aggregate (direct plus indirect) costs of the factor inputs. The estimation equations for each of the _K_ sectors derived from equation (13) 9
are now interlinked and are no longer independent. In other words, price indices _{Pi_ , _k}_ depend on the input costs of other sectors. In the next section, we explain how to use an input-output matrix to compute the _aggregate_ cost of factor inputs which is a function of factor input prices and factor intensities.

# **3 Empirical Strategy**

In this section, we first estimate the theoretical model developed in Section 2.1 in which each sector uses its own factors of production but there are no linkages between sectors. In this case, we consider the impact of only the the direct costs of factor inputs on trade. We then extend this analysis by estimating the theoretical model developed in Section 2.2, where each sector purchases intermediate goods produced by other sectors as described by input-output linkages. This more realistic approach allows us to estimate the effect of aggregate energy costs on export performance.

## **3.1 Baseline Model**

Our baseline model measures the effects of direct electricity, natural gas and labor costs on bilateral exports at the sector level.<sup>4</sup> We estimate the model using the Poisson pseudomaximum likelihood (PPML) estimator proposed by Santos Silva and Tenreyro (2006). This is a weighted nonlinear least squares estimator, and although it is a count data estimator, it can be used for continuous variables (such as exports) because it does not require the data to strictly follow a Poisson distribution. We use this estimator because it delivers consistent estimates of gravity equations under very general conditions and performs well even when the dependent variable displays a relatively large proportion of zero observations. Using the example of a gravity model, Santos Silva and Tenreyro (2006) demonstrate that the PPML estimator outperforms a log-linear specification estimated by ordinary least squares by providing estimates that are robust to different patterns of heteroskedasticity.

Our baseline empirical model is derived directly from the theoretical analysis. From equa-

> 4 Ideally, we would also like to examine the effect of coal prices on international trade. However, coal prices are determined mostly through privately-negotiated contracts and are largely not publicly available.

10
tion (13), the model to be estimated takes the following form: where _Exportsijkt_ represents annual bilateral trade flows between exporting country _i_ and importing country _j_ in sector _k_ in year _t_ . _ElectricityCost_ , _NGCost_ , and _LaborCost_ measure the direct unit input costs of electricity, natural gas, and labor respectively. These cost terms are calculated by interacting input prices with intensities, defined as input expenditure per unit of value added. Following our theoretical framework, we expect negative coefficients for the input costs of exporters ( _α_ 1 _i_ , _α_ 2 _i_ , and _α_ 3 _i_ ), and positive coefficients for the input costs of importers ( _α_ 1 _j_ , _α_ 2 _j_ , and _α_ 3 _j_ ). Equation (18) also includes _SectorOutput_ as a measure of the gross economic output of sector _k_ in exporting country _i_ . It controls for the possibility that the volume of exports is greater the bigger the scale of the sector. Thus we expect _α_ 4 to be positive.

An advantage of our panel specification is that it allows for the inclusion of various fixed effects that can help mitigate biases from omitted variables. In particular, _δij_ represents fixed effects that control for time-invariant determinants of trade flows between each country pair. These include distance, common border, and differences in religion, culture, and language between trading partners. _δit_ and _δjt_ are exporter-year and importer-year fixed effects, respectively, which capture all time-varying country-specific factors that affect the trade flows of the exporting and importing countries. These include exchange rate regimes, infrastructure investments, and regulatory changes that affect export performance. _δkt_ represents sector-time effects that capture sector-specific shocks to bilateral trade. Finally, _εijkt_ denotes an error term.

Our estimation strategy relies on energy prices having an effect on trade that is conditional on the energy intensities of the sector. Since energy prices are observed at the national level, it is the interaction with the electricity intensity and natural gas intensity that generates inter-sectoral variation and allows the coefficients _α_ 1 _i_ , _α_ 1 _j_ and _α_ 2 _i_ , _α_ 2 _j_ to be identified.

11
Our econometric model includes separate input cost terms for exporters and importers to allow for potential asymmetric effects of the energy costs of the exporting and importing countries on bilateral trade. The theoretical framework suggests that there may be asymmetries in the magnitudes of the estimated cost impacts. The reason is that domestic costs of production are expected to have a direct impact on the exports of country _i_ , while input costs in country _j_ will indirectly affect exports from country _i_ to country _j_ by affecting the prevailing product price in country _i_ . In our econometric model, we test for statistically significant differences between (the absolute values of) _α_ 1 _i_ and _α_ 1 _j_ for electricity costs and between (the absolute values of) _α_ 2 _i_ and _α_ 2 _j_ for natural gas costs.

# **3.2 Input-Output Extended Model**

The econometric model developed in the previous subsection estimates the effect of direct energy costs on trade. Higher factor prices may also have an indirect effect on trade flows by raising the cost of production of intermediate inputs, if these costs are then passed on by input suppliers. Furthermore, if a sector’s intermediate inputs are imported, then both domestic and foreign factor prices and the factor intensities of intermediate and direct factor inputs would affect the domestic costs of production. Because we observe inputoutput linkages across countries, we assume that each sector in each country produces a specific intermediate good. Each sector uses outputs from other sectors (as well as its own output) as intermediate inputs; that is, there are _N × K_ intermediate goods. In this case the Cobb-Douglas cost function for sector _k_ in country _i_ can be written as: where _W_ is the cost of direct factor inputs and _Pj_ , _s_ is the price of intermediate inputs produced by sector _s_ in country _j_ , and _αs_ , _j_ , _k_ is sector _k_ ’s intensity of the intermediate good produced by sector _s_ in country _j_ . Thus in total there are _NK_ + 1 inputs to production. Taking logarithms on both sides of equation (19) gives: 12
The price of intermediate goods from sector _s_ in country _j_ ( _Pj_ , _s_ ) is assumed to be the sum of production costs and a markup, that is, _Pj_ , _s_ = (1 + _ηj_ , _s_ ) _Cj_ , _s_ , where _ηj_ , _s_ is the markup: Substituting equation (21) for the price term in equation (20) gives: Equation (22) can be rewritten in matrix form as the following: where the elements within _A_<sup>�</sup> include the _α_ terms (i.e. input-output linkages among sectors) and the elements within Ψ<sup>�</sup> include the markup terms. The first item on the right-hand side of this equation calculates the dot product of _β_<sup>�</sup> and _W_<sup>�</sup> . Assuming that the mark-up Ψ<sup>�</sup> is stable over time and can be controlled for by a set of fixed effects, we can rewrite equation (23) as the following by dropping the markup term: Using the input-output relationship (matrix _A_<sup>�</sup> ), the factor intensities of all sectors (vector _β_ ) and a vector of input prices _W_ , we are able to compute the direct plus indirect cost of factor inputs. In the input-output extended model, we replace the direct cost terms (calculated as the interaction terms of factor intensities and input prices) with the direct plus indirect costs described in equation (24). It is worth emphasizing that by using inputoutput tables, we assume that intersectoral linkages are exogenous – a (small) change in input prices would not shift the input-output relationship drastically. We do not account for substitution possibilities among primary factors and intermediate inputs. In this way, we should interpret the results from the input-output extended model as an estimation of short-term effects.

13
# **3.3 Data**

We estimate our empirical model using energy and bilateral trade data for 10 manufacturing sectors in 43 countries from 1991 to 2012. The full set of countries included in our analysis is listed in Table 1.

We use data on electricity and natural gas prices from the International Energy Agency (IEA) Energy Prices and Taxes database, the Energy Regulators Regional Association (ERRA) Tariff Database, and various government and media reports. The IEA database records after-tax prices for residential and industrial consumers, while the ERRA database records after-tax prices for residential and non-residential consumers. For a subset of nine countries covered by both the IEA and the ERRA, we cross-check the price data from the two datasets and find that the data are fairly consistent. We use IEA data on industrial energy prices wherever available. We use ERRA data on nonresidential energy prices for one country (Saudi Arabia) that is not covered by the IEA. Table 2 lists the sources of energy price data for each country.

Energy consumption data are from the IEA World Energy Statistics and Balances database. This database reports final electricity and natural gas consumption for 10 manufacturing sectors at the two-digit level of the international Standard Industrial Classification, Revision 3.1 (ISIC Rev. 3.1). One of the sectors is labelled “non-specified". According to IEA documentation, this sector includes rubber and rubber products as well as any manufacturing activities not covered by the other nine sectors. Because there may be measurement errors associated with the energy intensity of this miscellaneous category, we conduct a robustness check by excluding the “non-specified” sector from the sample.<sup>5</sup> We gather data on manufacturing value added, employment costs and number of employees from the United Nations Industrial Development Organization (UNIDO) Industrial Statistics Database (INDSTAT2). The sector-specific wages are calculated by dividing employment costs by the total number of total employees. Factor intensities are measured as the value of factor inputs divided by value added.<sup>6</sup>

> 5The IEA notes that for some countries, “non-specified” could be a sector average where a sector-wise breakdown is not possible.

> 6About 1.5 percent of the observations have factor intensity values below zero or greater than one. We dropped these observations as outliers.

14
The multicountry inter-sectoral input-output relationships are obtained from the OECD Inter-Country Input-Output Database (ICIO). The ICIO tables are available for the years 1995, 2000, 2005, 2008, 2009, 2010 and 2011. As our sample spans the period from 1991 to 2012, we use the 1995 edition of ICIO table for samples in 1991-1995, the 2000 edition for 1996-2000, the 2005 edition for 2001 to 2005, and the 2011 edition for 2011 and 2012. While both the IEA and the ICIO tables rely on ISIC Rev 3.1 to classify industries, ICIO sectors are slightly more disaggregated than IEA sectors because some IEA sectors are aggregated from multiple ISIC two-digit sectors. For the main analysis, we aggregate ICIO sectors to match the IEA data. We also apply different levels of sector disaggregation for robustness checks. Table 3 shows the correspondence between IEA and ICIO sectors.

Bilateral trade data are obtained from the United Nations COMTRADE Database. Trade data are reported at the six-digit Harmonized Commodity Description and Coding System (HS) level. Using the concordance provided by Pierce and Schott (2012), we convert the data from the HS level to the ISIC Rev. 3.1. We always use the trade values reported by the exporters.

Manufacturing value added, employment costs, export value and IEA and ERRA energy prices are reported in current prices denominated in US dollars. Energy prices from government reports are reported in current prices denominated in local currencies. We convert local currencies into U.S. dollars using the exchange rates reported by the World Bank. We convert values for different years into 2010 prices using country-specific GDP deflators obtained from the International Monetary Fund.

# **3.4 Descriptive Statistics**

Table 4 provides summary statistics for the key variables. Table 5 shows how intensively each row sector uses inputs from each column sector. The numbers are the weighted average shares of factor inputs across all countries in the sample in 2010. While the production of most sectors relies largely on inputs from subsectors within their own two-digit classification, there are strong interdependencies among the machinery, transport equipment and metals sectors.<sup>7</sup> For example, for the manufacture of transport equipment, 12 percent of

> 7In addition, because the “non-specified’" sector includes rubber and plastic products, a large share of its inputs are from the chemicals sector.

15
inputs are from the metals sector, and 24 percent from the machinery sector.

Table 6 lists average direct factor intensity and aggregate factor intensity by sector. It shows that for sectors that rely heavily on intermediate goods from energy-intensive sectors, there is a substantial difference between direct and indirect energy intensities. For example, the indirect energy intensity of the machinery and transport equipment sectors is two to three times higher than their direct energy intensity. Ignoring indirect energy consumption would therefore significantly underestimate the true energy cost faced by these sectors.

There is also significant variation in countries’ trade dependency on intermediate goods, as shown in Figure 1. In Bulgaria, for example, about 50 percent of intermediate goods for production are imported and close to 60 percent of the outputs produced as intermediate goods are destined for the export market. In India, in contrast, less than 20 percent of intermediate goods are imported or exported.

# **4 Empirical and Simulation results**

## **4.1 Regression results**

Table 7 reports the main estimation results. Columns (1) and (3) correspond to regressions for the baseline model, which accounts for direct factor costs only. Columns (2) and (4) correspond to regressions for the input-output extended model, which accounts for direct and indirect energy costs based on equation (24). In addition, columns (1) and (2) show results for the full sample estimation, while columns (3) and (4) show results for regressions that exclude the “non-specified" sector. Because our cost variables involve interaction terms of factor prices and intensities, the size of the impact of factor price changes on exports depends on factor intensities. To ease interpretation of the coefficients, each factor intensity is scaled by its sample average so that the value is equal to 1 for a sector of average factor intensity. This means that the coefficients of factor costs in the baseline model can be interpreted as the estimated percentage change in bilateral exports for a 1 percent increase in the factor price for a sector of average factor intensity. For consistency, factor intensities in the input-output extended model are scaled by the same sample mean.

Table 7 shows that bilateral exports are negatively correlated with origin countries’ electric16
ity costs and positively correlated with destination countries’ electricity costs. The coefficients are statistically significant in all models. Evaluated at the average electricity intensity, the baseline model suggests that a 1 percent increase in exporters’ electricity cost is associated with a reduction in exports of 0.11 percent. In contrast, the input-output extended model predicts a smaller export reduction of 0.07 percent. The estimated export elasticity of importers’ electricity cost is 0.14 based on the baseline model, and 0.10 based on the input-output extended models. Excluding the “non-specified" sector from the regression produces similar results.

It is not surprising that the estimated trade elasticities are different between the baseline and input-output extended models. In the input-output model, the electricity cost term measures direct plus indirect electricity costs. Thus a 1 percent increase in this cost could reflect an increase in domestic electricity costs or an increase in the electricity costs of upstream trading partners that supply intermediate inputs – or both. In the baseline model, in contrast, a 1 percent increase in electricity costs is entirely an increase in the domestic electricity price. The coefficients of electricity costs in the two models may therefore differ substantially for a sector if a large share of its electricity costs are embodied in intermediate inputs. Such differences become more salient when we conduct simulation analysis for different countries and sectors.

The trade elasticities of natural gas costs are smaller than those of electricity costs. The input-output extended model predicts that a 1 percent increase in aggregate natural gas cost for origin countries would cause a 0.04 percent reduction in exports, while a 1 percent increase for destination countries’ would cause a 0.04 percent increase in exports. These coefficients are statistically significant. The coefficients estimated by the baseline model are of the expected sign but are not statistically significant.

Coefficients on the labor cost terms are all significant and of the expected sign. The effect of labor costs on trade is in general much greater than the effect of energy costs. The export elasticity of origin countries’ labor costs is between -0.16 and -0.22 across various models, while the elasticity of destination countries’ labor costs ranges between 0.15 and 0.22.

Table 7 also reveals evidence of asymmetric effects of exporter and importer factor costs, especially for electricity. In particular, the importer factor costs are found to have a larger 17
absolute effect on bilateral trade than the exporter factor costs across all models and the difference (of their magnitudes) is statistically significant. Finally, consistent with predictions of the gravity model, estimates of all models show that sector output is significantly positively correlated with export and the elasticity is close to 1.

# **4.2 Robustness Check**

In this subsection, we conduct sensitivity analysis using alternative model specifications. First, we consider the possibility that sector output may be endogenous to export value, especially for export-oriented sectors. As a robustness check, we move sector output to the left-hand side of equation (18) and redefine our dependent variable as export intensity, that is, the ratio of exports to sector output. The results are reported in Table 8. The resulting coefficients are almost identical to those of the main model. This is expected, since the estimated trade elasticity of sector output in the main model is close to 1.

Second, we consider the possibility that bilateral trade may not respond to price changes immediately as a result of market imperfections or adjustment costs. There could also be contemporaneous feedback from exports to energy prices if export-oriented sectors dominate domestic industrial energy consumption. To account for delayed responses and the potential endogeneity of energy prices, we control for one-year lagged explanatory variables rather than contemporaneous variables. The resulting coefficients, shown in Table 9 are very similar in significance and size.

Third, we consider alternative levels of sector disaggregation. In previous regressions, we mapped export, ICIO, and value added data into the 10 manufacturing sectors used by the IEA so as to match energy consumption data. To account for potential aggregation bias in the factor content of trade (Feenstra and Hanson, 1999), we reestimate trade elasticities according to the sector aggregation used by the ICIO tables (15 sectors) as well as a sector aggregation based on the three-digit ISIC Rev. 3.1 (54 industries). Because energy consumption data are not available at these more disaggregated levels, we assume that the energy intensity of industries at the two-digit and three-digit levels of ISIC Rev 3.1 is the same if they belong to the same IEA sector. Regression results based on the alternative disaggregation levels, reported in Tables 10 and 11, are fairly close to the main results, although with 18
a lower goodness of fit.

Fourth, we consider alternative treatment of firms’ markup. Our estimation strategy relies on the assumption that markup is stable over time so that it is absorbed in the country pair fixed effects. If this assumption is violated, our estimation of the input-output model could be biased because markup could be correlated with both energy cost and trade value. To control for potential variation in markup over time, we estimate an alternative model with two additional sets of fixed effects: importer-sector-year fixed effects and exporter-sectoryear fixed effects. Now variations in the factor input costs of exporters and importers, as well as in the sector outputs of exporters, would also be absorbed in these fixed effects. To identify the effects of energy costs on trade value, we control for a cost gap term (exporter cost minus importer cost) rather than separate exporter and importer cost terms. The regression results using cost gap terms, with and without this alternative set of fixed effects, are reported in Tables 12 and 13 respectively. The coefficients of electricity, natural gas and labor cost gap terms in the input-output extended model are all negative and statistically significant. In addition, their sizes are well within the range of the importer and exporter trade elasticities from the main model. These results suggest that potential biases from the omitted variable of firms’ markup may not be a concern.

Finally, we investigate potential heterogeneous effects of factor costs on trade according to the characteristics of pairs of trading partners. In particular, we include in equation (18) interaction terms between factor costs and the following variables: a dummy variable equal to 1 if the trading partners are contiguous and 0 otherwise; a dummy variable equal to 1 if the trading partners belong to the same trading bloc (such as European Union or NAFTA) and 0 otherwise; a dummy variable equal to 1 if the trading partners are both in the euro zone and 0 otherwise; a continuous variable measuring the absolute distance between the trading partners; and a continuous variable measuring the relative distance between the trading partners.<sup>8</sup> To simplify notation, we report estimation results with controls for cost gap terms rather than separate cost terms for exporters’ and importers’ factor inputs. The 8Relative distance is a measure of GDP-weighted distance from the perspective of exporting country, that is,

> where _Yj_ is the importing country’s GDP, _Y_<sup>_W_</sup> is the world’s GDP, _Distij_ is the great circle distance (measured in km) between two countries _i_ and _j_ .

19
results are reported in Table 14.

The results indicate that while including interaction terms for contiguous countries and trading blocs does not change the estimation results from the input-output extended model, it tends to increase the absolute value of trade elasticities of direct factor costs in the baseline model. That is, when two countries share a common border or belong to the same trading bloc, the effect of the energy or labor cost gap is increased. Another finding from Table 14 is that distance could exacerbate the effect of energy costs on trade. According to the inputoutput extended model, a one-mile increase in the relative or absolute distance between two trading partners is associated with a 0.01 percentage point increase in the effect of the electricity cost gap. In other words, the greater the distance the more important is the effect of electricity cost on trade.

# **4.3 Simulation Analysis**

Next we analyze the economic consequences of two policies using the estimated trade elasticities reported in Section 4.1.<sup>9</sup> The first policy concerns the use of energy cross-subsidies and its impact on industrial competitiveness. Energy cross-subsidies place an implicit tax on industry. In India, for example, the electricity price for industry is on average 15 percent higher than the cost of supply, to support lower tariffs for residential and agriculture consumers. In this counterfactual, we ask how manufacturing exports in Bulgaria and India would change with the removal of a 15 percent implicit tax on electricity. Bulgaria and India are selected for the analysis because they represent two different patterns of trade in intermediate goods, as discussed in Section 3.4: India has a strikingly low level of imports and exports of intermediate goods, while Bulgaria is much more engaged in global value chains (see Figure 1). The simulation analysis shows that a country’s level of participation in global value chains significantly affects the correlation between domestic energy price shocks and exports.

The second counterfactual considers the implications of carbon pricing for international trade. The EU Emissions Trading System created the world’s first and largest carbon market. A number of countries outside Europe, such as China and the United States, have adopted

> 9Counterfactuals based on alternative estimates of trade elasticities reported in Section 4.2 are similar and do not change our main findings.

20
similar programs. Carbon trading schemes put a price on carbon emissions and would increase the cost of fossil-fuel based energy in countries where they are in force. There is concern that a unilateral adoption of carbon regulation could lead to carbon leakage. Indeed, the European Commission considers that energy- and trade-intensive sectors face a higher risk of carbon leakage. To protect their competitiveness, these sectors are granted a higher share of free emissions allowances. To gauge the potential magnitude of carbon leakage, we simulate the impact of a 10 percent unilateral price increase for electricity in the European Union on EU manufacturing exports.

Table 15 describes the impact of removing energy cross-subsidies on export and net export values. According to the input-output extended model, removing a 15 percent implicit electricity tax on industry would increase India’s net manufacturing exports by 9.5 percent, or around $6.1 billion a year. Since India is quite self-sufficient in the production of intermediate inputs, multiplier effects of energy price shocks are accumulated along the supply chain. The baseline model, which considers energy requirements only at the final stage of production, therefore underestimates the impact of energy price shocks by a factor of almost two. The pattern becomes more conspicuous for the machinery and transport equipment sectors, for which indirect energy consumption accounts for a large share of total energy consumption. For the machinery sector, ignoring indirect energy consumption would result in an underestimation of the impact of energy price shocks by a factor of four.

The story is different for Bulgaria. Because Bulgaria has extensive exchanges of intermediate inputs with other countries, the competitive advantage for its exporters from a decline in energy prices is partially offset by the decline in the input costs of its trading partners. The estimation from the input-output extended model indicates an increase in net manufacturing exports of 5.7 percent, while the estimation from the baseline model shows an increase of 7.0 percent. At the sectoral level, the difference between the baseline and input-output estimations for the machinery sector is somewhat smaller than that for India. The reason is that Bulgaria’s greater reliance on inputs from outside the country means smaller multiplier effects within the domestic supply chain.

Table 16 describes the effects of a 10 percent across-the-board increase in electricity prices in the European Union on the exports of EU and non-EU countries. Exports decline for all sectors in the European Union. And for each sector, the input-output model predicts a larger 21
decline than the baseline model does. The impact on net exports ranges from 0.9 percent for the transport equipment sector (with the lowest aggregate energy intensity) to 4.4 percent for the basic metals sector (with the highest). For the European Union as a whole, the inputoutput model predicts an overall impact of a 1.9 percent reduction in net manufacturing exports annually. This is almost three times the size of the reduction predicted by the baseline model. The difference reflects multiplier effects within the European Union where there is intensive trade in intermediate goods between EU firms.

# **5 Conclusion**

This paper seeks to contribute to the literature on energy prices and international trade by taking into account the effect of embodied energy costs (costs of direct plus indirect energy consumption) on exports. We start with the premise that energy price shocks affect not only the cost of direct energy consumption (at the final stage of production) but also the cost of indirect energy consumption embodied in intermediate inputs. In addition, with the rapid expansion of global value chains, domestic energy prices may not tell the full story if a firm relies heavily on imported intermediate goods that are energy intensive. We develop a theoretical framework that incorporates tradable intermediate inputs in a Meliz-type trade model and apply the theoretical model to energy, trade, and multi-country input-output data for 10 manufacturing sectors in 43 countries from 1991 to 2012.

We identify a robust effect of energy costs on export performance. We use the estimated trade elasticities to simulate two counterfactuals: the removal of energy-cross subsidies in Bulgaria and India, and a unilateral 10 percent increase in electricity prices in the European Union. We find that removing cross-subsidies which impose a 15 percent implicit electricity tax on industry in India could increase its net manufacturing exports by as much as 9.5 percent, or $6.1 billion a year. A carbon tax that unilaterally increases electricity prices by 10 percent in the European Union could cause an EU-wide reduction in net manufacturing exports of 1.9 percent annually.

The simulation analysis also yields a nuanced understanding of the causal link between energy price shocks and exports. First, even if a sector is not energy intensive on the basis of direct energy consumption, it may still be vulnerable to energy price shocks if 22
it uses energy-intensive intermediate inputs. Second, if a country is not actively engaged in global value chains, domestic price shocks could have a multiplier effect on its overall competitiveness. However, it is worth noting that in cases where the input-output model predicts a larger effect for energy costs, the effect is still small in magnitude compared to that of labor costs. The predicted trade elasticity of labor costs is several times higher than that of energy costs in all cases.

Our results should be interpreted as an upper-bound estimation because we do not account for substitution possibilities among primary factors and intermediate inputs, as well as the substitution of intermediates imports among countries. Analysis of long-run dynamic impacts such as those based on a computable general equilibrium model could yield new insights into the effect of energy price changes on industrial competitiveness in the international market.

23