Abstract
We propose a comprehensive decomposition of changes in global market shares that accounts for the value-added content. We find that the ongoing globalization process affects market shares directly by shifting production from developed to developing countries. Moreover, apparent improvements in the relative quality of exported goods from most new EU member states and developing countries occur to some extent from higher quality imported inputs. Hence, the process of outsourcing high-quality production from developed countries plays an important role and reduces the contribution of residual non-price factors in explaining market share gains of developing countries.
Appendix A
Consumers utility function and import price index
The “value added in gross exports” (VAS) decomposes gross exports by producer countries:
where VAS is a K × K matrix that provides disaggregated value added by producer country in gross exports for each exporting country; K is the number of countries. V is a K × KN block-diagonal matrix, and Vr is a 1 × N direct value-added coefficient vector where each element gives the share of direct domestic value added in total output of country r in each sector (r = 1,…, K); N represents the number of sectors. Input-output coefficients are comprised in the KN × KN matrix A, which is constructed from the N × N blocks Ars. Those blocks contain information on intermediate use by country s of the goods produced in country r. X is a KN × K matrix of gross exports, and Xr is a N × K matrix of country r’s exports by sector and partner. Finally, B is the Leontieff inverse matrix B = (I – A)–1, and u is a 1 × N unity vector.
The “value-added exports” or “value-added trade” (VAX) reflects how a country’s exports are used by importers:
where VAX is a K × K matrix that provides disaggregated value added by producer country in final consumption of each country. Y denotes a KN × K final demand matrix: it contains blocks Ysr, which is the N × 1 final demand vector representing demand in country r for final goods shipped from country s. Countries’ total value-added exports are evaluated as the row sum of all off-diagonal elements of VAX (thus excluding value added produced and consumed in the same country).
Appendix B
Extracting the geographical demand effect from the intensive margin
An exporter’s world market share is further affected by changes in the structure of world trade over time. Thus, as a first step in the decomposition of the intensive margin, we distinguish between the bilateral intensive margin and changes in the global weight of each exporter’s bilateral trading partner. To account for the latter, we explicitly allow for different growth rates of various destination markets. The term dDS(i)t captures changes in the intensive margin due to shifts in the recipient country’s share in world imports:
where
Appendix C
Consumers utility function and import price index
Following Broda and Weinstein (2006), we use a constant elasticity of substitution (CES) utility function for a representative household from importing country i consisting of three nests:
where D(i)t is the domestic good, M(i)t is composite imports and κ(i) is the elasticity of substitution between domestic and foreign goods, M(i)g,t is the subutility from consumption of imported good g, γ(i) is elasticity of substitution among import goods, Q(i)gc,t is the taste and quality parameter, and σ(i)g is elasticity of substitution among varieties of good g.
The minimum unit-cost functions is represented by
where P(i)g,t denotes minimum unit-cost of import good g, P(i)t is minimum unit-cost of total imports, and C(i)g,t is the subset of all varieties of goods consumed in period t. The import price index for a good g is defined as π(i)g,t = P(i)g,t/P(i)g,0, while total import price index – as π(i)t = P(i)t/P(i)0.
Benkovskis and Wörz (2014) extend the work by Feenstra (1994) and Broda and Weinstein (2006) by relaxing the assumption of unchanged taste or quality. They introduce an import price index that adds a term to capture changes in taste and quality:
where π(i)gc,t = P(i)gc,t/P(i)gc,0 and Sato-Vartia weights w(i)gc,t and w(i)g,t are computed using cost shares s(i)Mgc,t and s(i)Mg,t in the two periods as follows:
while λ(i)g,t and λ(i)g,0 are Feenstra (1994) index accounting for changes in variety:
Appendix D
Decomposition of the intensive margin of value-added export market share changes
The share of country k’s VAS of goods in total imports of a country i, IM(i)k,tVASG, can be rearranged in the following way:
After solving the first order conditions of the consumer utility maximization problem (9)–(11) s.t. budget constraints, rearranging and summing over c we obtain:
where λ(i)t is Lagrange multiplier.
From equations (12) and (13) it follows that country k’s value-added share in total imports of a country i is driven by minimum unit-costs, taste and quality parameters and the value-added share of country k in the production of various goods exported to destination market i:
Since
Combining equation (17) with the import price index in (13), one can obtain the VAS in goods market share decomposition described in equation (4).
Appendix E
Elasticities of substitution between varieties
We estimate elasticities of substitution between varieties according to the methodology proposed by Feenstra (1994). Feenstra (1994) specifies demand and supply equations in relative terms and exploits the insight of Leamer (1981) assuming independence of errors in demand and supply equations to obtain the following equation:
where ω(i)g ≥ 0 is the inverse supply elasticity assumed to be the same across partner countries, δ(i)gc,t is an error term of supply equation which is assumed to be independent of ε(i)gc,t, error of the demand equation.
Broda and Weinstein (2006) argue that it is possible to obtain consistent estimates by exploiting the panel nature of data and define a set of moment conditions for each good g. If estimates of elasticities are imaginary or of the wrong sign the grid search procedure is implemented. However, Soderbery (2010, 2012) reports that this methodology generates severely biased elasticity estimates, and proposes the use of a Limited Information Maximum Likelihood (LIML) estimator instead. Where estimates of elasticities are not feasible
Elasticities of substitution between varieties for goods (top 10 world importers in 2014).
| No. of estimated elasticities | Mean | Minimum | Maximum | 25th percentile | Median | 75th percentile | |
|---|---|---|---|---|---|---|---|
| United States | 4145 | 27.73 | 1.0152 | 51,490 | 1.64 | 2.29 | 3.74 |
| China | 4259 | 38.24 | 1.0014 | 88,842 | 2.13 | 3.15 | 5.58 |
| Germany | 4689 | 24.86 | 1.0011 | 68,978 | 2.20 | 3.30 | 5.99 |
| Japan | 4266 | 52.86 | 1.0026 | 38,064 | 1.84 | 2.79 | 5.20 |
| United Kingdom | 4834 | 34.26 | 1.0224 | 96,781 | 1.74 | 2.45 | 3.96 |
| France | 4795 | 11.40 | 1.0182 | 6590 | 1.98 | 2.98 | 5.26 |
| Korea | 4303 | 22.12 | 1.0128 | 41,122 | 2.13 | 3.14 | 5.19 |
| Netherlands | 4395 | 38.68 | 1.0012 | 95,075 | 1.85 | 2.62 | 4.45 |
| Italy | 4807 | 25.67 | 1.0053 | 26,838 | 1.89 | 2.82 | 4.92 |
| Canada | 3611 | 49.50 | 1.0013 | 82,431 | 2.33 | 3.80 | 7.55 |
Source: WIOD, UN Comtrade, authors’ calculations.
Calculated using UN Comtrade data for disaggregated imports of 187 countries using equation (18). The estimates are based on data between 1996 and 2014 for 237 exporters.
Growth of VAS of goods, VAS and VAX measures for the US and China, 2001–2014, %.
Source: WIOD, UN Comtrade, authors’ calculations.
Notes: VAS of goods (UN Comtrade) calculated using equation (1), VAS of goods (WIOD) – using equation (6) and assuming that gross exports of service sectors equals zero, VAS (WIOD) and VAX (WIOD) come from equation (6) and (7) respectively.
Decomposition of VAS of goods market share changes between 2000 and 2014 using elasticities of substitution estimated by Ossa (2015).
Source: WIOD, UN Comtrade, authors’ calculations, Ossa (2015).
Notes: The decomposition is performed using equations (3)–(5). Other factors include extensive margin, set of competitors and shift in demand structure. Results denote cumulative log-changes of global market shares.
Decomposition of VAS of machinery, electrical equipment and transportation goods market share changes between 2000 and 2014.
Source: WIOD, UN Comtrade, authors’ calculations.
Notes: The decomposition is performed using equations (3)–(5) restricting set of goods (G) to machinery, electrical equipment and transportations (2-digit HS codes 84–89). Other factors include extensive margin, set of competitors and shift in demand structure. Results denote cumulative log-changes of global market shares.
Decomposition of VAS of goods market share changes on the US market between 2000 and 2014.
Source: WIOD, UN Comtrade, authors’ calculations.
Notes: The decomposition is performed using equations (3)–(5) restricting set of destinations (I) to the US only. Other factors include extensive margin, set of competitors and shift in demand structure. Results denote cumulative log-changes of global market shares.
Decomposition of VASG market share changes between 2000 and 2014.
| Country | VASG market share changes | Extensive margin | Price competitiveness | Non-price competitiveness | Shift in production chains | Set of competitors | Shift in demand structure |
|---|---|---|---|---|---|---|---|
| Australia | 0.43 | −0.03 | −0.58 | 0.89 | 0.03 | 0.02 | 0.16 |
| Austria | 0.05 | 0.02 | 0.09 | −0.03 | −0.05 | 0.00 | 0.05 |
| Belgium | −0.21 | 0.00 | −0.06 | 0.08 | −0.18 | 0.00 | −0.05 |
| Brazil | 0.38 | 0.04 | −0.19 | 0.48 | 0.07 | −0.01 | 0.15 |
| Bulgaria | 0.81 | 0.12 | −0.17 | 0.50 | 0.38 | 0.00 | 0.04 |
| Canada | −0.29 | 0.01 | 0.00 | −0.20 | 0.03 | −0.01 | −0.07 |
| China | 0.87 | −0.01 | 0.15 | 0.37 | 0.29 | −0.01 | −0.02 |
| Croatia | 0.36 | 0.23 | 0.07 | −0.15 | 0.22 | −0.01 | 0.00 |
| Cyprus | 0.28 | 0.01 | −0.02 | 0.09 | 0.12 | −0.02 | 0.00 |
| Czech Republic | 0.55 | 0.06 | −0.24 | 0.53 | 0.13 | 0.00 | 0.10 |
| Denmark | −0.19 | 0.02 | −0.01 | −0.12 | −0.10 | 0.00 | −0.03 |
| Estonia | 0.34 | 0.30 | 0.03 | −0.32 | 0.31 | −0.01 | 0.03 |
| Finland | −0.50 | 0.03 | 0.07 | −0.40 | −0.15 | −0.01 | 0.06 |
| France | −0.29 | 0.01 | 0.02 | −0.14 | −0.11 | 0.00 | −0.02 |
| Germany | −0.03 | 0.00 | 0.03 | 0.01 | −0.05 | 0.00 | 0.03 |
| Greece | 0.04 | 0.06 | −0.08 | 0.08 | −0.03 | 0.00 | 0.08 |
| Hungary | 0.36 | 0.08 | −0.07 | 0.17 | 0.16 | 0.00 | 0.09 |
| India | 0.58 | 0.13 | 0.02 | 0.21 | 0.11 | 0.00 | 0.01 |
| Indonesia | 0.14 | 0.14 | −0.11 | 0.03 | 0.03 | 0.06 | 0.09 |
| Ireland | −0.34 | −0.03 | −0.12 | 0.00 | −0.11 | 0.00 | −0.12 |
| Italy | −0.16 | 0.01 | −0.03 | −0.08 | −0.03 | 0.00 | 0.03 |
| Japan | −0.73 | −0.06 | 0.22 | −0.66 | −0.30 | −0.01 | 0.25 |
| Korea | 0.20 | −0.10 | −0.16 | 0.16 | 0.00 | −0.01 | 0.24 |
| Latvia | 0.52 | 0.18 | −0.88 | 1.10 | 0.21 | −0.01 | 0.09 |
| Lithuania | 0.91 | 0.26 | −0.18 | 0.31 | 0.33 | −0.02 | 0.13 |
| Luxembourg | 0.03 | 0.03 | 0.11 | −0.02 | 0.01 | 0.00 | −0.04 |
| Malta | −0.14 | −0.06 | 0.23 | −0.32 | 0.04 | −0.01 | 0.04 |
| Mexico | −0.02 | 0.01 | −0.04 | 0.02 | 0.06 | 0.03 | −0.09 |
| Netherlands | −0.09 | 0.00 | −0.02 | 0.09 | −0.13 | 0.00 | −0.06 |
| Norway | −0.12 | 0.05 | −0.37 | 0.25 | 0.00 | 0.02 | −0.09 |
| Poland | 0.78 | 0.09 | −0.13 | 0.52 | 0.26 | −0.01 | 0.09 |
| Portugal | −0.05 | 0.09 | −0.03 | 0.05 | 0.06 | −0.01 | −0.11 |
| Romania | 0.94 | 0.21 | −0.20 | 0.45 | 0.40 | 0.00 | 0.01 |
| Russia | 0.68 | 0.08 | −0.41 | 0.60 | 0.18 | 0.01 | 0.16 |
| Slovakia | 0.73 | 0.13 | −0.13 | 0.33 | 0.21 | 0.00 | 0.15 |
| Slovenia | 0.28 | 0.14 | 0.06 | 0.02 | 0.12 | 0.01 | 0.08 |
| Spain | −0.03 | 0.05 | 0.04 | 0.01 | −0.03 | 0.00 | −0.07 |
| Sweden | −0.32 | 0.01 | 0.08 | −0.24 | −0.04 | −0.01 | 0.00 |
| Switzerland | 0.06 | −0.02 | 0.05 | −0.34 | 0.04 | 0.00 | 0.09 |
| Turkey | 0.48 | 0.18 | 0.01 | 0.20 | 0.07 | 0.00 | 0.03 |
| United Kingdom | −0.48 | 0.04 | 0.14 | −0.36 | −0.15 | 0.00 | −0.03 |
| United States | −0.38 | −0.02 | 0.15 | −0.38 | −0.06 | −0.01 | 0.03 |
| Average for 42 countries | 0.15 | 0.06 | −0.06 | 0.09 | 0.06 | 0.00 | 0.04 |
Decomposition of gross export market share changes between 2000 and 2014.
| Country | Gross export market shares | Extensive margin | Price competiti veness | Nonprice competiti veness | Set of competitors | Shift in demand structure |
|---|---|---|---|---|---|---|
| Australia | 0.39 | −0.04 | −0.62 | 0.90 | 0.03 | 0.17 |
| Austria | 0.10 | 0.02 | 0.01 | 0.05 | 0.00 | 0.05 |
| Belgium | −0.06 | 0.00 | −0.09 | 0.12 | 0.00 | −0.07 |
| Brazil | 0.37 | 0.05 | −0.20 | 0.46 | −0.01 | 0.17 |
| Bulgaria | 0.77 | 0.18 | −0.31 | 0.83 | −0.01 | 0.05 |
| Canada | −0.45 | 0.01 | −0.04 | −0.27 | 0.00 | −0.11 |
| China | 0.78 | −0.01 | 0.21 | 0.55 | −0.02 | −0.03 |
| Croatia | 0.32 | 0.41 | 0.02 | −0.14 | −0.01 | −0.04 |
| Cyprus | 0.39 | 0.10 | −0.20 | 0.33 | −0.04 | −0.06 |
| Czech Republic | 0.72 | 0.07 | −0.30 | 0.83 | −0.01 | 0.12 |
| Denmark | −0.15 | 0.02 | 0.01 | −0.15 | −0.01 | −0.04 |
| Estonia | 0.29 | 0.45 | −0.29 | −0.03 | −0.02 | 0.04 |
| Finland | −0.42 | 0.04 | 0.04 | −0.56 | −0.01 | 0.08 |
| France | −0.29 | 0.01 | 0.08 | −0.31 | −0.01 | −0.03 |
| Germany | 0.00 | −0.01 | 0.06 | −0.03 | 0.00 | 0.04 |
| Greece | 0.11 | 0.07 | −0.17 | 0.21 | 0.00 | 0.09 |
| Hungary | 0.30 | 0.11 | −0.09 | 0.19 | 0.00 | 0.09 |
| India | 0.75 | 0.15 | 0.07 | 0.48 | −0.01 | 0.02 |
| Indonesia | 0.12 | 0.18 | −0.12 | 0.01 | 0.06 | 0.08 |
| Ireland | −0.37 | −0.02 | −0.18 | −0.04 | 0.00 | −0.16 |
| Italy | −0.17 | 0.00 | −0.07 | −0.06 | 0.00 | 0.04 |
| Japan | −0.57 | −0.07 | 0.31 | −0.96 | −0.01 | 0.28 |
| Korea | 0.24 | −0.12 | −0.24 | 0.18 | −0.02 | 0.29 |
| Latvia | 0.57 | 0.23 | −0.96 | 1.44 | −0.01 | 0.13 |
| Lithuania | 1.02 | 0.39 | −0.25 | 0.55 | −0.03 | 0.17 |
| Luxembourg | 0.05 | 0.11 | 0.15 | −0.22 | −0.01 | −0.10 |
| Malta | −0.09 | 0.02 | 0.24 | −0.47 | −0.01 | 0.08 |
| Mexico | −0.07 | 0.00 | 0.02 | 0.01 | 0.03 | −0.11 |
| Netherlands | 0.00 | −0.01 | 0.00 | 0.13 | 0.00 | −0.09 |
| Norway | −0.05 | 0.03 | −0.42 | 0.28 | 0.02 | −0.12 |
| Poland | 0.93 | 0.12 | −0.14 | 0.83 | −0.01 | 0.10 |
| Portugal | −0.11 | 0.13 | −0.04 | 0.16 | −0.01 | −0.14 |
| Romania | 0.90 | 0.29 | −0.33 | 0.94 | 0.00 | 0.01 |
| Russia | 0.62 | 0.10 | −0.46 | 0.66 | 0.01 | 0.18 |
| Slovakia | 0.91 | 0.23 | −0.02 | 0.41 | 0.00 | 0.17 |
| Slovenia | 0.31 | 0.19 | −0.09 | 0.28 | 0.02 | 0.07 |
| Spain | 0.00 | 0.06 | 0.04 | 0.03 | 0.00 | −0.09 |
| Sweden | −0.36 | 0.01 | 0.09 | −0.31 | −0.01 | −0.02 |
| Switzerland | 0.24 | −0.03 | −0.07 | −0.31 | 0.01 | 0.12 |
| Turkey | 0.69 | 0.27 | 0.05 | 0.39 | 0.00 | 0.03 |
| United Kingdom | −0.47 | 0.05 | 0.03 | −0.38 | 0.00 | −0.03 |
| United States | −0.36 | −0.03 | 0.08 | −0.39 | −0.01 | 0.04 |
| Average for 42 countries | 0.19 | 0.09 | −0.10 | 0.16 | 0.00 | 0.03 |
References
Amiti, M., and C. Freund. 2010. “An Anatomy of China’s Export Growth..”. In Feenstra, R. C., and S. J. Wei (Eds.), China’s Growing Role in World Trade. 35–56. Chicago, IL: University of Chicago Press.Search in Google Scholar
Armington, P. S. 1969. “A Theory of Demand for Products Distinguished by Place of Production.” IMF Staff Papers 16: 159–178.10.2307/3866403Search in Google Scholar
Bayoumi, T., M. Saito, and J. Turunen. 2013. “Measuring Competitiveness: Trade in Goods or Tasks?” IMF Working Paper, 13/100, Washington.10.5089/9781484325803.001Search in Google Scholar
Bems, R., and R. C. Johnson. 2017. “Demand for Value Added and Value-Added Exchange Rates.” American Economic Journal: Macroeconomics 9: 45–90.10.3386/w21070Search in Google Scholar
Benkovskis, K., and J. Wörz. 2014. “How Does Quality Impact on Import Prices?” Review of World Economics 150: 665–691.10.1007/s10290-014-0196-3Search in Google Scholar
Benkovskis, K., and J. Wörz. 2018. “What Drives the Market Share Changes? Price Versus Non-Price Factors.” Structural Change and Economic Dynamics 45: 9–29. DOI: 10.1016/j.strueco.2017.12.001.Search in Google Scholar
Benkovskis, K., S. Berzina, and L. Zorgenfreija. 2016. “Evaluation of Latvia’s Re-exports Using Firm-Level Trade Data.” Baltic Journal of Economics 16: 1–20.10.1080/1406099X.2016.1163891Search in Google Scholar
Besedes, T., and T. J. Prusa. 2011. “The Role of Extensive and Intensive Margins and Export Growth.” Journal of Development Economics 96: 371–379.10.1016/j.jdeveco.2010.08.013Search in Google Scholar
Broda, C., and D. E. Weinstein. 2006. “Globalization and the Gains from Variety.” Quarterly Journal of Economics 121: 541–585.10.1162/qjec.2006.121.2.541Search in Google Scholar
Cheptea, A., L. Fontagné, and S. Zignago. 2014. “European Export Performance.” Review of World Economics 150: 25–58.10.1007/s10290-013-0176-zSearch in Google Scholar
Daudin, G., C. Rifflart, and D. Schweisguth. 2011. “Who Produces from Whom in the World Economy?” Canadian Journal of Economics 44: 1403–1437.10.1111/j.1540-5982.2011.01679.xSearch in Google Scholar
Dennis, A., and B. Shepherd. 2007. “Trade Costs, Barriers to Entry, and Export Diversification in Developing Countries.” OECD Economic Department Working Paper, 29.10.1596/1813-9450-4368Search in Google Scholar
Feenstra, R. C. 1994. “New Product Varieties and the Measurement of International Prices.” American Economic Review 84: 157–177.Search in Google Scholar
Feenstra, R. C., and J. Romalis. 2014. “International Prices and Endogenous Quality.” The Quarterly Journal of Economics 129: 477–527.10.1093/qje/qju001Search in Google Scholar
Felbermayr, G. J., and W. Kohler. 2006. “Exploring the Intensive and Extensive Margins of World Trade.” Review of World Economics 14: 642–674.10.1142/9789814440196_0004Search in Google Scholar
Flach, L. 2016. “Quality Upgrading and Price Heterogeneity: Evidence from Brazilian Exporters.” Journal of International Economics 102: 282–290.10.1016/j.jinteco.2016.07.005Search in Google Scholar
Galstyan, V., and P. R. Lane. 2008. “External Imbalances and the Extensive Margin of Trade.” Economic Notes 37: 241–257.10.1111/j.1468-0300.2008.00200.xSearch in Google Scholar
Hallak, J. C., and P. K. Schott. 2011. “Estimating Cross-Country Differences in Product Quality.” The Quarterly Journal of Economics 126: 417–474.10.1093/qje/qjq003Search in Google Scholar
Henn, C., C. Papageorgious, and N. Spatafora. 2013. “Export Quality in Developing Countries.” IMF Working Paper, WP/13/108, Washington.10.5089/9781484351635.001Search in Google Scholar
Hummels, D., and P. Klenow. 2005. “The Variety and Quality of a Nation’s Exports.” American Economic Review 95: 704–723.10.1257/0002828054201396Search in Google Scholar
Hummels, D., J. Ishii, and K.-M. Yi. 2001. “The Nature and Growth of Vertical Specialization in World Trade.” Journal of International Economics 54: 75–96.10.1016/S0022-1996(00)00093-3Search in Google Scholar
Johnson, R. C., and G. Noguera. 2012. “Accounting for Intermediates: Production Sharing and Trade in Value Added.” Journal of International Economics 86: 224–236.10.1016/j.jinteco.2011.10.003Search in Google Scholar
Koopman, R., W. Powers, Z. Wang, and S.-J. Wei. 2010. “Give Credit where Credit is Due: Tracing Value Added in Global Production Chains.” NBER Working Paper, 16426, Cambridge.10.3386/w16426Search in Google Scholar
Koopman, R., Z. Wang, and S.-J. Wei. 2014. “Tracing Value-Added and Double Counting in Gross Exports.” American Economic Review 104: 459–494.10.1257/aer.104.2.459Search in Google Scholar
Leamer, E. E. 1981. “Is it a Demand Curve, or is it a Supply Curve? Partial Identification Through Inequality Constraints.” Review of Economics and Statistics 63: 319–327.10.2307/1924348Search in Google Scholar
Los, B., M.P. Timmer, and G. J. de Vries. 2015. “How Global are Global Value Chains? A New Approach to Measure International Fragmentation.” Journal of Regional Science 55: 66–92.10.1111/jors.12121Search in Google Scholar
Manova, K., and Z. Zhang. 2012. “Export Prices Across Firms and Destinations.” The Quarterly Journal of Economics 127: 279–436.10.3386/w15342Search in Google Scholar
McGuirk, A. K. 1987. “Measuring Price Competitiveness for Industrial Country Trade in Manufactures.” IMF Working Paper, 87/34, Washington.10.5089/9781451978704.001Search in Google Scholar
Mellens, M. C., H. G. A. Noordman, and J. P. Verbruggen. 2007. “Re-exports: International Comparison and Implications for Performance Indicators.” CPB Document No. 149, CPB Netherlands Bureau for Economic Policy Analysis.Search in Google Scholar
Narayanan, B., A. Aguiar, and R. McDougall. 2012. Global Trade, Assistance, and Production: The GTAP 8 Data Base. Center for Global Trade Analysis, Purdue University.Search in Google Scholar
Ossa, R. 2015. “Why Trade Matters After All.” Journal of International Economics 97: 266–277.10.1016/j.jinteco.2015.07.002Search in Google Scholar
Pula, G., and D. Santabárbara. 2011. “Is China Climbing up the Quality Ladder? Estimating Cross Country Differences in Product Quality Using Eurostat’s COMEXT Trade Database.” ECB Working Paper, 1310, Frankfurt.10.2139/ssrn.1773214Search in Google Scholar
Romer, P. M. 1994. “New Goods, Old Theory, and the Welfare Costs of Trade Restrictions.” NBER Working Paper, 4452, Cambridge.10.3386/w4452Search in Google Scholar
Schott, P. K. 2008. “The Relative Sophistication of Chinese Exports.” Economic Policy 53: 5–49.10.3386/w12173Search in Google Scholar
Sheu, G. 2014. “Price, Quality, and Variety: Measuring the Gains from Trade in Differentiated Products.” American Economic Journal: Applied Economics 6: 66–89.10.1257/app.6.4.66Search in Google Scholar
Soderbery, A. 2010. “Investigating the Asymptotic Properties of Import Elasticity Estimates.” Economics Letters 109: 57–62.10.1016/j.econlet.2010.08.006Search in Google Scholar
Soderbery, A. 2012. Estimating Import Supply and Demand Elasticities: Analysis and Implications. Department of Economics, Krannert School of Management, Purdue University.10.2139/ssrn.2272749Search in Google Scholar
Stehrer, R. 2012. “Trade in Value Added and the Value Added in Trade.” WIIW Working Paper, 81, Vienna.Search in Google Scholar
Streicher, G., and R. Stehrer. 2012. “Whither Panama? Constructing a Consistent and Balanced World SUT System Including International Trade and Transport Margins.” WIOD Working Paper, 13.Search in Google Scholar
Thorbecke, W. 2011. “Investigating the Effect of Exchange Rate Changes on China’s Processed Exports.” Journal of the Japanese and International Economies 25: 33–46.10.1016/j.jjie.2010.12.001Search in Google Scholar
Timmer, M. P., E. Dietzenbacher, B. Los, R. Stehrer, and G. J. de Vries. 2015. “An Illustrated User Guide to the World Input–Output Database: The Case of Global Automotive Production.” Review of International Economics 23: 575–605.10.1111/roie.12178Search in Google Scholar
Timmer, M. P., B. Los, R. Stehrer, and G. J. de Vries. 2016. An Anatomy of the Global Trade Slowdown Based on the WIOD 2016 Release. GGDC research memorandum number 162, University of Groningen.Search in Google Scholar
Verhoogen, E. A. 2008. “Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector.” The Quarterly Journal of Economics 123: 489–530.10.1162/qjec.2008.123.2.489Search in Google Scholar
Wang, Z., and S.-J. Wei. 2010. “What Accounts for the Rising Sophistication of China’s Exports?” In China’s Growing Role in World Trade, edited by R. C. Feenstra and S. J. Wei, 63–104. Chicago, IL: University of Chicago Press.Search in Google Scholar
WTO. 2014. World Trade Report 2014. World Trade Organization, https://www.wto.org/english/res_e/booksp_e/wtr14-2c_e.pdf. Last accessed on May 3, 2018.Search in Google Scholar
Yi, K.-M. 2003. “Can Vertical Specialization Explain the Growth of World Trade?” Journal of Political Economy 111: 52–102.10.1086/344805Search in Google Scholar
Article note
The views expressed in this research are those of the authors and do not necessarily reflect the official viewpoints of the ECB, Latvijas Banka, Stockholm School of Economics in Riga or Oesterreichische Nationalbank.
©2018 Walter de Gruyter GmbH, Berlin/Boston
