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A car exported from Mexico contains Japanese engines, German steel, and US software. A pair of jeans shipped from Bangladesh embeds Chinese yarn, Indian dye, and Italian design. Gross trade statistics count each crossing at face value and double-count intermediates; trade in value addedstrips gross flows back to the domestic labour and capital they actually compensate. This page uses the OECD TiVA 2023 edition (Koopman, Wang & Wei 2014; Timmer et al. 2014) to map global value chain participation across 86 economies.
Before 2008, global value chains expanded monotonically: firms sliced production into ever finer stages and scattered them across borders to exploit wage, scale, and specialisation differences. Timmer, Erumban, Los, Stehrer & de Vries (2014) document this as the 'second unbundling.' After the global financial crisis, the foreign value-added share of gross exports stopped rising, what Antràs & Chor (2018) call the GVC slowdown. Tariff escalation (2018-19), Covid (2020), and reshoring narratives since have kept it flat rather than reversed it.
Total GVC participation is the sum of backward (imported VA embedded in a country's own exports) and forward (domestic VA re-exported by partners) shares. High totals flag economies deeply stitched into cross-border production networks: either as downstream assemblers (Vietnam, Czechia, Slovakia), or as upstream suppliers of intermediate inputs (Taiwan, Korea, Germany). Small, open, specialised economies dominate the top of the list; large, resource-rich, or mostly-services economies (USA, Brazil, Australia) sit lower. The ranking reproduces the style of OECD's TiVA country notes.
Manufacturing sectors with heavy intermediate-input requirements sit at the top of the FVA ranking: transport equipment, electronics, basic metals. Sector location matters as much as country position: a country specialised in transport equipment will mechanically score high on backward participation whether or not it is 'deep' in a GVC, because everyone's transport equipment is import-heavy. Services sectors are nearer the foreign-content frontier only when indirectly counted through manufacturing inputs (Miroudot & Cadestin 2017).
Antràs & Chor (2013, Journal of Political Economy) showed that a country's upstreamness in global production is pinned down by a combination of contracting frictions, factor intensity, and the degree of complementarity along the chain. The empirical correlation with GDP per capita is weak and often non-monotonic: both resource-rich upstream suppliers (Saudi Arabia, Norway) and advanced-economy input producers (Korea, Germany) sit above average upstreamness, while final assemblers (Vietnam, Mexico) and consumer-economies (Japan, UK) sit below. The scatter below uses a rank-preserving TiVA proxy, upstreamness = 1 + 3 × fwd / (fwd + bwd), against World Bank GDP per capita (current USD).
The position index (fwd − bwd) / (fwd + bwd) maps every economy onto a line from pure downstream assembler (−1) to pure upstream supplier (+1). The distribution is bimodal in TiVA: a cluster of commodity exporters and high-value input suppliers on the right, a cluster of downstream assemblers on the left, and thin density in the middle. This pattern , specialisation by stage rather than uniform integration, is the central stylised fact of the second-unbundling literature (Timmer et al. 2014; Baldwin 2016).
(fwd − bwd) / (fwd + bwd). Negative buckets are downstream assemblers; positive buckets are upstream input suppliers. The tails are populated; the centre (−0.2 to 0.2) holds mid-stage industrial hubs such as Germany and Japan.Plot every country's total GVC participation against its GDP per capita and the cross-section separates geographically. European economies cluster in the upper-middle-income, high-participation quadrant, reflecting the dense intra-EU production networks Baldwin (2006) called the 'Factory Europe' block. Asian manufacturing-specialised economies sit high regardless of income. African economies cluster low and to the left, outside the main fragmentation networks (UNCTAD 2013, World Investment Report: Global Value Chains). The income gradient within each continent is real but modest; a country's continent often predicts its GVC position better than its GDP per capita.
The rankings above show the top tail. The map below shows every TiVA-reporting economy on one canvas, a spatial view of the second unbundling. Quintile bins of total participation (backward + forward, % of gross exports) make the regional patterns visible at a glance: dense intra-EU production networks in the upper band; the East Asian manufacturing corridor from Korea and Japan through coastal China and into Vietnam and Malaysia; NAFTA integration knitting Canada, the US, and Mexico; resource-exporting economies with high forward but low backward content. Countries not in OECD TiVA are greyed.
Small changes in the position index are noisy: a 0.05 shift in (fwd − bwd)/(fwd + bwd)is within the measurement error of the underlying ICIO table. A quintile-to-quintile move is not. For each year we rank economies by position and bin into quintiles (1 = most downstream, 5 = most upstream); we then keep only economies whose quintile moved by 2 or more between 2005 and 2020. This is the 'stage relocation' tail the second-unbundling literature pays attention to (Antràs & Chor 2018; Baldwin & Freeman 2022).
Two economies with the same total GVC participation can sit on opposite sides of the production network: one as a deep-backward assembler (Vietnam, Mexico, Czechia), the other as an upstream intermediates supplier (Korea, Saudi Arabia, Norway). Income explains a non-trivial slice of that asymmetry. Baldwin & Lopez-Gonzalez (2015, The World Economy 38(11): 1682-1721; NBER WP 18957, 2013) document that the second unbundling concentrated low-skill assembly stages in lower-income emerging economies and high-skill upstream stages in advanced ones, the 'supply-chain trade' pattern. The bars below split TiVA-reporting economies into within-sample GDP-pc terciles (lower / middle / upper) and report the cross-country mean of backward (FVA share) and forward (IDC share) participation in 2020.
Each of the 86economies in TiVA 2023 has an individual page with five figures: its backward and forward participation trajectory since 1995, the sectoral decomposition of its FVA embed, its GVC position index (upstream vs downstream) in the style of Antràs & Chor (2018), the Johnson-Noguera VAX ratio, and total participation alongside an Antrà s-Chor (2013) upstreamness proxy. Start with a few reference economies:
References. Antrà s, P. & Chor, D. (2018). 'On the Measurement of Upstreamness and Downstreamness in Global Value Chains.' In World Trade Evolution: Growth, Productivity and Employment, ed. L. Y. Ing & M. Yu. Routledge. Also in the Handbook of Commercial Policy vol 1B (2016), North-Holland. Baldwin, R. (2016). The Great Convergence: Information Technology and the New Globalization. Harvard University Press. Baldwin, R. & Freeman, R. (2022). 'Risks and Global Supply Chains: What We Know and What We Need to Know.' Annual Review of Economics 14: 153-180. Koopman, R., Wang, Z. & Wei, S.-J. (2014). 'Tracing Value-Added and Double Counting in Gross Exports.' American Economic Review 104(2): 459-494. Miroudot, S. & Cadestin, C. (2017). 'Services in Global Value Chains: From Inputs to Value-Creating Activities.' OECD Trade Policy Papers 197. Timmer, M. P., Erumban, A. A., Los, B., Stehrer, R. & de Vries, G. J. (2014). 'Slicing Up Global Value Chains.' Economic Policy 29(80): 613-661.