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Structural Change and Global Trade. (2020). Zhang, Jing ; Sposi, Michael ; Monarch, Ryan ; Lewis, Logan.
In: Working Paper Series.
RePEc:fip:fedhwp:92684.

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  1. Structural Change With Long?Run Income and Price Effects. (2021). Mestieri, Martí ; Comin, Diego ; Lashkari, Danial.
    In: Econometrica.
    RePEc:wly:emetrp:v:89:y:2021:i:1:p:311-374.

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  2. Structural change and global trade flows: Does an emerging giant matter?. (2020). Can, Talan B ; Dennis, Benjamin N.
    In: Review of International Economics.
    RePEc:bla:reviec:v:28:y:2020:i:5:p:1191-1231.

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References

References cited by this document

  1. , “Engel’s Law in the Global Economy: Demand-Induced Patterns of Structural Change, Innovation, and Trade,” Econometrica, 2019, 87 (2), 497–528.
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  5. A Appendix A.1 Data Appendix This section describes the data used to construct the empirical counterfactual in Section 2 and to estimate the model in Section 4. These data cover 1970–2015 for 27 countries /regions: Australia, Austria, Belgium-Luxembourg, Brazil, Canada, China, Cyprus, Denmark, Finland, France, Germany, Greece, India, Indonesia, Ireland, Italy, Japan, South Korea, Mexico, Netherlands, Portugal, Spain, Sweden, Turkey, United Kingdom, and United States, plus a “Rest of World”. The empirical counterfactual requires time series of 1) total exports and imports of goods and services and 2) final expenditure in goods and services. The model estimation requires these series as well as 3) bilateral goods and services trade data; 4) input-output coefficients; 5) value added to gross output ratios; 6) sectoral price indices; and 7) the aggregate wage bill, and 8) total employment.
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  6. Alvarez, Fernando and Robert E. Lucas, “General equilibrium analysis of the Eaton–Kortum model of international trade,” Journal of Monetary Economics, September 2007, 54 (6), 1726–1768.
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  9. Bilateral goods and services trade by sector and country As with total goods trade, when not taken directly from the WIOD, goods trade between two regions in our sample is generated by splicing importer-reported bilateral goods trade data in the IMF DOTS database with WIOD data, using the same three-year splicing factors as above. Bilateral services data are sparse, so instead of splicing, we simply apply average bilateral shares (e.g., U.S. imports from Canada as a share of U.S. imports from the world) over three year periods to the total services trade data calculated as above. Again, for all years prior to 1995, we use average bilateral shares from 1995-1997, and for all years after 2011, we use average bilateral shares from 2009-2011.
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  10. Boppart, Timo, “Structural Change and the Kaldor Facts in a Growth Model with Relative Price Effects and Non-Gorman Preferences,” Econometrica, 2014, 82 (6), 2167–2196.

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  12. Caron, Justin, Thibault Fally, and James R. Markusen, “International Trade Puzzles: A Solution Linking Production and Preferences *,” The Quarterly Journal of Economics, August 2014, 129 (3), 1501–1552.

  13. Comin, Diego, Danial Lashkari, and Martí Mestieri, “Structural Change with Long-run Income and Price Effects,” 2018.
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  14. Costinot, Arnaud and Andrés Rodríguez-Clare, “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” Handbook of International Economics, January 2014, 4, 197–261.

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  17. Fieler, Ana Cecília, “Nonhomotheticity and Bilateral Trade: Evidence and a Quantitative Explanation,” Econometrica, July 2011, 79 (4), 1069–1101.

  18. For each country we linearly interpolate between the observed years and then splice the shares series to that from the WIOD data in 1995. If these data do not overlap with 1995, we linearly extrapolate forward until 1995, using data from the latest two years, and then splice. This gives us an unbalanced panel from which we can observe variation across countries and over time prior to 1995. This helps provide discipline on the estimated shares that we impute prior to 1995 for countries that have no data. Using our unbalanced panel we first estimate: log λikt 1−λikt = α +β log(yit) +εit, (A.1) and then impute the ratio, log( λikt 1−λikt ), for missing country-years using the observed GDP per worker, yit and the estimates of α and β. The imputed value for λikt is ensured to be between 0 and 1. We follow a similar procedure to impute values for γigst and γisst, and then set γiggt = 1−γigst and γisgt = 1−γisst.
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  19. Hanoch, Giora, “Production and Demand Models with Direct or Indirect Implicit Additivity, ” Econometrica, 1975, 43 (3), 395–419.

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  21. Hoekman, Bernard, “Trade and growth – end of an era?,” in “The Global Trade Slowdown: A New Normal?,” VoxEU.org, June 2015, pp. 3–19.
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  22. Hottman, Colin J. and Ryan Monarch, “Estimating Unequal Gains across U.S. Consumers with Supplier Trade Data,” International Finance Discussion Paper, January 2018, 2018 (1220), 1–51.

  23. IMF, “Direction of Trade Statistics,” 2016.
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  24. Inklaar, Robert and Marcel P. Timmer, “The Relative Price of Services,” Review of Income and Wealth, December 2014, 60 (4), 727–746.

  25. Input-output coefficients and value added to gross output ratios To construct γikn, the country-specific share of intermediate inputs that sector k sources from sector n, we use the numbers directly from WIOD for 1995-2011. The value added to gross output ratio in sector k, λik is also a straightforward manipulation of data in the WIOD for 1995-2011. 27Results are qualitatively similar defining construction as a goods category, but given the lack of direct trade in construction, categorizing it as a service will make goods sectoral openness higher and services sectoral openness lower. Both the model-based counterfactual and especially the reduced-form counterfactual would be smaller in magnitude relative to the data.
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  26. Irwin, Douglas A., “Long-run trends in world trade and income,” World Trade Review, 2002, 1 (1), 89–100.

  27. Kehoe, Timothy J., Kim J. Ruhl, and Joseph B. Steinberg, “Global Imbalances and Structural Change in the United States,” Journal of Political Economy, December 2017.
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  28. Kongsamut, Piyabha, Sergio Rebelo, and Danyang Xie, “Beyond Balanced Growth,” The Review of Economic Studies, 2001, 68 (4), 869–882.

  29. Krugman, Paul, Richard N. Cooper, and T. N. Srinivasan, “Growing World Trade: Causes and Consequences,” Brookings Papers on Economic Activity, 1995, 1995 (1), 327–377.

  30. Labor endowment by country We take total employment data in the Penn World Table as our measure of Li. These data correspond to the number of workers engaged in market activity. Since these data only go through 2014, we splice WDI total employment data in 2015 to the Penn World Table data in order to estimate the model through 2015. Wage by country Dividing aggregate value added in current US$ by the labor endowment gives the imputed wage wi.
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  31. Levchenko, Andrei A. and Jing Zhang, “The evolution of comparative advantage: Measurement and welfare implications,” Journal of Monetary Economics, April 2016, 78, 96–111.

  32. Markusen, James R., “Explaining the Volume of Trade: An Eclectic Approach,” The American Economic Review, 1986, 76 (5), 1002–1011.

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  36. Our strategy is to work with the World Input-Output Database (WIOD) from 19952011, which is described in Timmer, Dietzenbacher, Los, Stehrer and de Vries (2015), then build the rest of the time sample out from those numbers using splicing techniques with other longer-running datasets. For pre-1995 trade data, we compute the annual changes from the relevant data source then extrapolate the levels off of the 1995 value from WIOD.
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  37. Rose, Andrew K., “Why Has Trade Grown Faster Than Income?,” The Canadian Journal of Economics / Revue canadienne d’Economique, 1991, 24 (2), 417–427.

  38. Simonovska, Ina, “Income Differences and Prices of Tradables: Insights from an Online Retailer,” The Review of Economic Studies, October 2015, 82 (4), 1612–1656.

  39. Since the WIOD covers only 1995-2011, we impute γikn and λik for the remaining years by (i) supplementing the WIOD with incomplete country-specific input-output tables to create a longer, yet unbalanced, panel and (ii) imputing all missing country-year observations based on an estimated relationship between the shares and real income per worker.
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  40. Świ˛ ecki, Tomasz, “Determinants of structural change,” Review of Economic Dynamics, March 2017, 24, 95–131.
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  41. Sposi, Michael, “Evolving comparative advantage, sectoral linkages, and structural change,” Journal of Monetary Economics, May 2019, 103, 75–87.

  42. Teignier, Marc, “The role of trade in structural transformation,” Journal of Development Economics, January 2018, 130, 45–65.

  43. The specific countries and years that we add are the following: Japan (1970, 1973-1995, from the Japan Industrial Productivity Database); South Korea (1970, 1975, 1980, 1985, 1986, 1987, 1988, 1990, 1993, 1995, from the Bank of Korea); United States (1972, 1977, 1982, 1987, 1992, 1997, from the Bureau of Economic Analysis);. We also appeal to data from the OECD: Australia (1968, 1974, 1986, 1989); Canada (1971, 1976, 1981, 1986, 1990); Denmark (1972, 1977, 1980, 1985, 1990); France (1972, 1977, 1980, 1985, 1990); (West) Germany (1978, 1986, 1988, 1990); Italy (1985); Netherlands (1972, 1977, 1981, 1986); United Kingdom (1968, 1979, 1984, 1990).
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  44. Timmer, Marcel P., Erik Dietzenbacher, Bart Los, Robert Stehrer, and Gaaitzen J. de Vries, “An Illustrated User Guide to the World Input-Output Database: the Case of Global Automotive Production: User Guide to World Input-Output Database,” Review of International Economics, August 2015, 23 (3), 575–605.

  45. Total exports and imports by country For each of the 27 groupings above, we take total goods and services exports and imports from the WIOD from 1995-2011. Then, for all other years (i.e. 1970-1994 and 2012-2015), we splice with other data. The splicing procedure divides the average of three years of the WIOD data by the average of three years of a longer dataset to generate a splicing factor, then applying that splicing factor to the longer dataset in non-WIOD years. The averages are calculated from 1995-1997 for all years before 1995, and from 2009-2011 for all years after 2011. For goods trade, we splice the WIOD trade data with total trade from the IMF Direction of Trade Statistics (IMF 2016a) database from 1970 to 2015. For services, we use aggregate services exports and imports data from the World Development Indicators (WDI) from 1970 to 2015 as the comparison. If WDI data on services are not available, we supplement in growth rates where necessary with OECD services data.
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  46. UN, “UN National Accounts Main Aggregates Database,” 2017.
    Paper not yet in RePEc: Add citation now
  47. Uy, Timothy, Kei-Mu Yi, and Jing Zhang, “Structural change in an open economy,” Journal of Monetary Economics, September 2013, 60 (6), 667–682.

  48. Value added by sector and country For value added data, we rely on the UN Main Aggregates Database (UN 2017). We take nominal goods value added in a country to be the combination of expenditure in “Agriculture, hunting, forestry, fishing” and “Mining, Manufacturing, Utilities”, while services value added is expenditure in “Construction”, “Wholesale, retail trade, restaurants and hotels”, “Transport, storage, and communication”, and “Other Activities”.27 Years: 1970-2015.
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  49. We do the same thing for post-2011 data. This ensures that the WIOD-based input-output coefficients generate sensible expenditure shares during WIOD years—otherwise, the inputoutput coefficients would be applied to trade data that may not match the underlying WIOD data used to generate those coefficients.
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  50. Yi, Kei-Mu, “Can Vertical Specialization Explain the Growth of World Trade?,” The Journal of Political Economy, February 2003, 111 (1), 52–102.

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