Measuring trade in value added with firm-level data

Bems, Rudolfs; Kikkawa, Ayumu Ken

doi:10.1016/j.jinteco.2021.103434

Cited by 17 publications

(10 citation statements)

References 18 publications

(9 reference statements)

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“…This means that their complex integrations in VA estimation turn out to be deceptively simple mathematical equations that we cannot predict, hereinafter referred to as the "paradox of intertwined trade." In this regard, this paper is in line with Bems and Kikkawa (2021) 4 to propose a different approach including vertical integration regarding production stages, and thus estimates optimal contribution to sectoral outcomes. These infinity/unknown production stages of vertical integration between industry-country pairs inspire the idea of the (knowledge) spillover effect and its contribution to VA.…”

Section: Introductionmentioning

confidence: 73%

“…Its contribution to VA as twice or infinity (see equation 5.1**) result in boosting extra by 0.8% (0.48x1.54x1.058=𝑣𝐵𝜁) to 72% (0.48x1,54x10000/9942), respectively, (see Table 1 and 2). We prefer to focus on average rather than sectoral contribution because aggregated sectoral data in I/O cause bias estimation due to the heterogeneity in export/import intensity across firms (Bems & Kikkawa, 2021).…”

Section: Results Of the Estimatementioning

confidence: 99%

“…Moreover, Blaum (2018) shows that exportoriented firms are likely to be more dependent on imports (see Bernard et al (2012) and Amiti et al (2014)). This also causes overstated trade in VA regarding GVC involvement because aggregated sectoral analysis does not characterize the heterogeneity in export/import intensity across firms (Bems & Kikkawa, 2021). Scholars have investigated where VA was created (Koopman et al, 2014;Wang et al, 2013) and the upstream effect of VA (Fally, 2012;Antràs et al, 2012;Alfaro et al, 2019), as well as measuring the VA exchange rate (Bems & Johnson 2017), factor content (Trefler & Zhu, 2010), international inflation spillover (Auer et al, 2019), and bias estimate of trade in VA (Bems & Kikkawa, 2021;Kee & Tang, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Estimation of VA relying on an aggregated group of countries (ROW) data shows a high coefficient value that causes overestimation bias (seeBems and Kikkawa (2021) for firm-level contribution). This result can change versa if the analysis relies on the Ghosh approach, see TableB4in the appendix for more information.16 However, GVC participation to pile up in the region due to the weak role of global governance (WTO) and regional FTAs.…”

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confidence: 99%

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Double Counting in Mystery: Journey of Intermediate Products in Multi-Country Trade

Biyik

2022

Preprint

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This study analyzes the production concept of the Value-Added (VA) chain by applying an alternative framework, as this concept is the main driver of the VA embodied in the intermediate export. VA supply chain spillover (upstream and downstream) of the production activities is investigated. The focal point of this study is to extend Global Value Chains (GVC) based on the multilevel and bilateral trade flow. In brief, there are two main contributions of this paper; the first begins with the originality of this work’s theory which reveals the intermediate input journey among sector-country pairs, and the second is to introduce the optimal GVC participation with the knowledge spillover effect regarding vertical (upstream and downstream) integration. In other words, this paper found that different forms of the VA with traveled products (among countries) provide more precise (sectoral/regional) integration and optimal estimation method. This paper relies on constructed Global Trade Analysis Project Multi-Region Input-Output (GTAP-MRIO) and patent panel datasets. As a result of the analysis, if the product crosses the border only twice as the product returns home to its origin country, the export/import coefficient of the sectoral linkages, which boosts VA and then causes double-counting, is about 1.54 % in terms of (single) country-level data. More importantly, with regard to its contributions with the knowledge spillover effect, traveling products among countries as twice or infinity is to contribute by 1.6% or 154%. Lastly, its contribution to VA presents 0.8% to 72%.

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Section: Introductionmentioning

confidence: 73%

Section: Results Of the Estimatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Double Counting in Mystery: Journey of Intermediate Products in Multi-Country Trade

Biyik

2022

Preprint

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“…On top of that, the data is reported every 5 years, which limit the analysis of the trend of DVAR. In order to overcome the above‐mentioned shortcomings of macro‐measurement method, some scholars have begun to use the micro‐method by applying merged data of China's customs transaction level and National Bureau of Statistics Annual Surveys of Industrial Firms data to measure the firms' DVAR (Bems & Kikkawa, 2021; Kee & Tang, 2016; Upward et al., 2013), which is gradually accepted and imitated by academia. These scholars carry out research from the perspectives of Foreign Direct Investment (FDI), Renminbi (RMB) exchange rate appreciation, minimum wage standards, upstream monopoly, factor market distortion, and industrial agglomeration.…”

Section: Introductionmentioning

confidence: 99%

Agglomeration, resource reallocation and domestic value‐added ratio in exports

2022

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Using micro merged data of China's customs transaction and National Bureau of Statistics Annual Surveys of Industrial Firms data, this paper analyzes the effect of industrial agglomeration on domestic value‐added in exports to gross exports (DVAR). It is found that industrial agglomeration significantly improves DVAR. Mechanism tests show that industrial agglomeration promotes DVAR by increasing the relative price of imported intermediate inputs and domestic inputs (relative price effect), as well as restrains DVAR by restraining cost markup of firm (cost markup effect). However, the promotion effect of the “relative price effect” is greater than the restraint effect of the “cost markup effect”. In addition, accession to the World Trade Organization and exchange reform strengthen the promotion effect of industrial agglomeration. The result of dynamic decomposition shows that 74.62% of the industry's DVAR increase is attributed to the resource re‐allocation effect, which is an important channel for industrial agglomeration to promote DVAR.

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Determinants of global value chain participation: cross-country analysis

Banerjee

Zeman

2022

Ind. Econ. Rev.

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This paper examines the factors that influence the five most common measures of GVC participation for the sample of countries included in the World Input Output Database (WIOD). For this sample, backward linkage is stronger than forward linkage and is the main channel for integration into GVCs. Also, a stronger backward linkage is associated with a relatively more downstream position in GVCs. Country size and openness to inward FDI are important determinants of GVC indicators. Of all the industry groupings, the influence on all the GVC indicators is strongest for high-tech manufacturing. In both manufacturing and services, the higher is the share of the high-tech categories the greater is the backward linkage and GVC participation rate, and the GVC position is relatively more downstream. The real exchange rate is positively associated with the share of domestic value added in gross exports (VAX ratio), which is a manifestation of the exchange rate elasticity of value-added exports being smaller than the exchange rate elasticity of gross exports.

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Measuring trade in value added with firm-level data

Cited by 17 publications

References 18 publications

Double Counting in Mystery: Journey of Intermediate Products in Multi-Country Trade

Double Counting in Mystery: Journey of Intermediate Products in Multi-Country Trade

Agglomeration, resource reallocation and domestic value‐added ratio in exports

Determinants of global value chain participation: cross-country analysis

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