Putting industries into the Eaton–Kortum model

Shikher, Serge

doi:10.1080/09638199.2010.539704

“…To consider how productivity growth in China may affect U.S. industries, we apply the Eaton and Kortum (2002) model of trade in a multisectoral setting (e.g., Chor, 2010;Shikher, 2011). 18 Using their framework, total output by U.S. industry j, Q uj , can be written as as the sum in demand for U.S. goods across destination markets:…”

Section: Industry Trade Shocksmentioning

confidence: 99%

Trade Adjustment: Worker Level Evidence

Autor

¹

,

Dorn

²

,

Hanson

³

et al. 2013

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In the past two decades, China's manufacturing exports have grown spectacularly, U.S. imports from China have surged, but U.S. exports to China have increased only modestly. Using representative, longitudinal data on individual earnings by employer, we analyze the effect of exposure to import competition on earnings and employment of U.S. workers over 1992 through 2007. Individuals who in 1991 worked in manufacturing industries that experienced high subsequent import growth garner lower cumulative earnings and are at elevated risk of exiting the labor force and obtaining public disability benefits. They spend less time working for their initial employers, less time in their initial two-digit manufacturing industries, and more time working elsewhere in manufacturing and outside of manufacturing. Earnings losses are larger for individuals with low initial wages, low initial tenure, low attachment to the labor force, and those employed at large firms with low wage levels. Import competition also induces substantial job churning among high-wage workers, but they are better able than low-wage workers to move across employers with minimal earnings losses, and are less likely to leave their initial firm during a mass layoff. These findings, which are robust to a large set of worker, firm and industry controls, and various alternative measures of trade exposure, reveal that there are significant worker-level adjustment costs to import shocks, and that adjustment is highly uneven across workers according to their conditions of employment in the pre-shock period.

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“…To consider how productivity growth in China may affect U.S. industries, we apply the Eaton and Kortum (2002) model of trade in a multisectoral setting (e.g., Chor, 2010;Shikher, 2011). 18 Using their framework, total output by U.S. industry j, Q uj , can be written as as the sum in demand for U.S. goods across destination markets:…”

Section: Industry Trade Shocksmentioning

confidence: 99%

Trade Adjustment: Worker Level Evidence

Autor¹,

Dorn²,

Hanson³

et al. 2013

View full text Add to dashboard Cite

In the past two decades, China's manufacturing exports have grown spectacularly, U.S. imports from China have surged, but U.S. exports to China have increased only modestly. Using representative, longitudinal data on individual earnings by employer, we analyze the effect of exposure to import competition on earnings and employment of U.S. workers over 1992 through 2007. Individuals who in 1991 worked in manufacturing industries that experienced high subsequent import growth garner lower cumulative earnings and are at elevated risk of exiting the labor force and obtaining public disability benefits. They spend less time working for their initial employers, less time in their initial two-digit manufacturing industries, and more time working elsewhere in manufacturing and outside of manufacturing. Earnings losses are larger for individuals with low initial wages, low initial tenure, low attachment to the labor force, and those employed at large firms with low wage levels. Import competition also induces substantial job churning among high-wage workers, but they are better able than low-wage workers to move across employers with minimal earnings losses, and are less likely to leave their initial firm during a mass layoff. These findings, which are robust to a large set of worker, firm and industry controls, and various alternative measures of trade exposure, reveal that there are significant worker-level adjustment costs to import shocks, and that adjustment is highly uneven across workers according to their conditions of employment in the pre-shock period.

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“…In contrast, this paper uses a disaggregated data set and estimates the technology parameters at commodity level. Similar to Shikher (), it applies a disaggregated EK model. However, it identifies both technology parameters jointly from a bigger sample.…”

Section: Introductionmentioning

confidence: 99%

Technology, Trade Costs and Export Sophistication

Weldemicael

¹

2013

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INTRODUCTIONT HE new economic geography literature explores the importance of technology and geography in determining specialisation patterns and comparative advantage of countries. In particular, Eaton and Kortum (2002), henceforth referred to as EK, provide a general equilibrium model to analyse the relative importance of technology and geography. Fieler (2011) extends the model by assuming that technology (productivity) varies more widely across countries for high income elasticity goods. 1 This implies that the composition of exports is directly related to the level of productivity that exists in a country. Similarly, the composition of exports also determines the level of export sophistication, which indicates the similarity of export bundles of a country with exports of high income countries (Hausmann et al., 2007).This paper explores the relative importance of technology and trade costs on export sophistication and welfare in a general equilibrium framework. It uses an estimation strategy developed by Balistreri and Hillberry (2007) that employs a structural estimation technique to calibrate a general equilibrium model. It also incorporates non-homothetic preferences using a Stone-Geary utility function. The tradable sector is modelled using a disaggregated EK framework that estimates bilateral trade at commodity level. 2 Key parameters of the model are calibrated.The result shows that the marginal expenditure shares exhibit substantial variation across commodities, with the highest share in services. Moreover, the efficiency dispersion parameter varies moderately across commodities consistent with their expected spread of efficiency. That is, more (less) sophisticated commodities have larger (smaller) spread of efficiency. Similarly, there is variation in the trade costs parameter across commodities, which indicates a difference in sensitivity. For example, commodities such as machinery and electrical equipment are less sensitive than others. Furthermore, the implied distance elasticities from the calibrated model are similar to estimates from a log-linear regression for most of the commodities.With regard to the country-specific technology parameter, the result shows that it varies across commodities within a country and also across countries. A country can have a large value in some commodities, but not in others. The overall pattern indicates that low-income countries are at the bottom end in the ranking. Similarly, the average weighted income of a commodity (PRODY) varies consistently with its level of sophistication. Moreover, export sophistication shows a clear upward trend with GDP per capita. Further, counterfactual experiments are also conducted to assess the effects of changes in technology and trade costs for the bottom quinitile countries. The findings indicate that these countries relatively have a I thank the associate editor, anonymous referee, Russell Hillberry, Roland Hodler, Michael Coelli, Philip McCalman, Reshad Ahsan, and various seminar and conference participants for helpful comment...

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Putting industries into the Eaton–Kortum model

Cited by 34 publications

References 9 publications

Trade Adjustment: Worker Level Evidence

Trade Adjustment: Worker Level Evidence

Trade Adjustment: Worker Level Evidence

Technology, Trade Costs and Export Sophistication

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