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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions HOV with technology and consumption dissimilarity Neil Foster and Robert Stehrer The Vienna Institute for International Economic Studies (wiiw) Version:

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

HOV with technology and consumption dissimilarity

Neil Foster and Robert Stehrer The Vienna Institute for International Economic Studies (wiiw)

Version: 2012-04-25

Study carried out within the 7th EU framework program project ’WIOD’ Grant agreement number 225 281.

April 24-26, 2012 - WIOD conference, Groningen, The Netherlands

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Overview

  • Heckscher-Ohlin-Vanek and Ricardo
  • Testing HOV with technology differences

∗ Technology differences ∗ Accounting for traded intermediates ∗ Role of consumption patterns

  • Introducing consumption dissimilarity
  • Results preliminary
  • Conclusions/further steps

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Heckscher-Ohlin model

  • Ricardo: Technology differences determine patterns of trade

∗ 2 countries, 2 sectors, 1 factor (labour) ∗ Countries specialize in goods with relative productivity advantage ∗ England: Textiles; Portugal: Wine

  • Heckscher-Ohlin: Factor endowments determine patterns of trade

∗ 2 countries (L, C), 2 sectors (textiles, computers), 2 factors ∗ Same technologies across countries ∗ Identical and homothetic preferences ∗ Factors can move between sectors but not between countries

  • Note: Heckscher and Ohlin have been well aware of the strictness of

assumptions and limitations (see Baldwin, 2010)

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

  • Country L: Labor abundant; country C: capital abundant
  • Autarky patterns

∗ Relative price of labor lower in country L ∗ Country L produces relatively more of the labor intensive good ∗ Textiles in country L relatively cheaper

  • Heckscher-Ohlin: Factor endowments determine patterns of trade

∗ Free trade

⋆ Country L specializes in textiles ⋆ Relative price of textiles increases in country L ⋆ Relative price of textiles decreases in country C ⋆ Factor price convergence (Stolper-Samuelson) and equalization ⋆ County L exports textiles; Country C exports computers

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

  • Issues

∗ Notorious problems with generalizations ∗ Strong assumptions (relaxations might make predictions ambiguous) ∗ Nonetheless: model became workhorse for decades

  • Looking at trade in factors helps

∗ Vanek, 1968; Travis, 1964; Melvin, 1968 ∗ Allows to increase dimensionality of model in any direction ∗ Example above: County L is net exporter of labor

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

  • However

∗ Still little empirical support of model in factor trade

⋆ Little empirical support (e.g. Leontief paradoxon, 1953 and 1956)

US was net exporter of labor (though capital abundant country)

⋆ Leontief, 1953 and 1956; many others ⋆ Results on testing predictions: ”... like a coin toss ...” (Trefler,

1993)

∗ Allowing for technology differences helps

⋆ Trefler, 1993 and 1995; others ⋆ Supporting original explanation given by Leontief (’Leontief was

right!’)

As US workers are more productive, US is - in productivity adjusted terms - labor abundant

∗ Davis and Weinstein (2002); Reimer (2006); Trefler and Zhu (2010)

  • Including traded intermediates

∗ Trefler and Zhu (2010); Deardorff (1982)

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Testing HOV with traded intermediates tr

f = d′ f (I − A)−1t = b′ f t

  • MeasuredFCT

= b′

f˜

fr − sr

f b′ f f = V r f − sr f Vf

  • PredictedFCT

∗ tr

f ... Measured net trade in factor f

∗ df ... NC × 1 vector direct factor inputs ∗ bf ... NC × 1 vector direct and indirect factor inputs ∗ I ... Identity matrix ∗ A ... NC × NC coefficients matrix ∗ tr ... Country r trade vector (exports positive, imports negative) ∗ V r

f ... Country r’s endowment with factor f

∗ sr ... Share of country r in world consumption ∗ Vf ... World endowment with factor f

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

  • Interpretation

∗ Predicted FCT: V r

f − sr f Vf

∗ Measured FCT: d′

f (I − A)−1tr

∗ Underlying assumptions

⋆

Identical and homothetic preferences

⋆

Factor-price equalization (in productivity adjusted terms)

⋆

Measuring FCT allows for productivity differences and trade in intermediates

  • Strong consumption similarity (SCS): fpr = srf

∗ Country consumes proportion sr of final goods produced by every country ∗ Necessary and sufficient condition that Vanek prediction holds

  • Weak consumption similarity (WCS): fr = srf

∗ Country r’s consumption of good is proportional to what world consumes

(regardless of where good is produced)

∗ Standard HOV: Condition if technologies are equal

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Empirical applications tr

f = d′ f (I − A)−1tr = V r f − sr f Vf

  • Assuming same technologies: HOV prediction is like a ’coin toss’

Trefler, 1993 and 1995; Davis and Weinstein, 2001; Trefler and Zhu, 2010

  • Allowing for technology differences: HOV prediction is met quite will

Various tests: sign test, rank correlation test, regression

  • However, large amount of ’missing trade’

i.e. predicted trade is larger than measured trade in factors

∗ Home bias ∗ Non-homothetic preferences ∗ Quality of products traded bilaterally ∗ Solutions:

⋆

Trefler and Zhu (2010): Imposing structures on selected sectors

⋆

Cassing and Nishioka (2010): Introducing a vector of consumption differences) Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Accounting for consumption dissimilarity tr

f

= d′

f (I − A)−1tr = V r f − sr f Vf

= b′

f˜

fr − b′

f sr f f

= V r

f − b′ f sr f If

= V r

f − b′ f Srf

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Assumptions on consumption structures: Sr

Case Preferences Sourcing structures SCS Homothetic Proportional (1) Non-homothetic Proportional (2) Homothetic Empirical sourcing at country level (3) Homothetic Empirical sourcing at country-industry level (4) Non-homothetic Empirical sourcing at country level HOV Non-homothetic Empirical sourcing at country-industry level

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Tests

  • Sign test (90% and more)
  • Rank correlations are high and significant (ρ = 0.9)
  • Already with assumption of SCS
  • Only slight improvements when introducing modifications
  • Regression tests:

FCTmeasured,r = α + β FCTpredicted,r + εr

Various specifications (OLS, FE, RE, ...)

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Strong consumption similarity: Total employment

AUS AUT BEL BGRBRA CAN CYP CZE DEU DNK ESP EST FIN FRA GBR GRCHUN IRL ITA KOR LTU LUX LVA MEX MLT NLD POL PRT ROU RUS SVK SVN SWE TUR TWN

  • 150
  • 100
  • 50

50

  • 150
  • 100
  • 50

50

_1, EMP

Measured FCT Predicted FCT

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Non-homothetic and country-level sourcing (selected sample): Total employment

AUS AUT BEL BGR BRA CAN CYP CZE DEU DNK ESP EST FIN FRA GBR GRC HUN IRL ITA KOR LTU LUX LVA MEX MLT NLD POL PRT ROU RUS SVK SVN SWE TUR TWN

  • 30
  • 20
  • 10

10

  • 30
  • 20
  • 10

10

_4, EMP

Measured FCT Predicted FCT

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Non-homothetic and country-level sourcing: Total employment

CHN EU IDN IND JPN OTH USA

  • 200
  • 100

100 200

  • 200
  • 100

100 200

_4, EMP

Measured FCT Predicted FCT

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Results (RE)

Table 12 - Slope coefficients - Random effects Total sample Reduced sample Total High Medium Low Capital Total High Medium Low Capital Strong consumption similarity FCTp 0.139∗∗∗ 0.136∗∗∗ 0.149∗∗∗ 0.134∗∗∗ 0.108∗∗∗ 0.127∗∗∗ 0.136∗∗∗ 0.142∗∗∗ 0.127∗∗∗ 0.094∗∗∗ s.e. 0.005 0.004 0.005 0.005 0.003 0.003 0.004 0.003 0.003 0.003 R2 0.920 0.871 0.916 0.920 0.628 0.959 0.871 0.954 0.965 0.362 Non-homothetic preferences, proportionality FCTp 0.179∗∗∗ 0.156∗∗∗ 0.169∗∗∗ 0.186∗∗∗ 0.113∗∗∗ 0.156∗∗∗ 0.156∗∗∗ 0.162∗∗∗ 0.180∗∗∗ 0.100∗∗∗ s.e. 0.006 0.004 0.006 0.006 0.003 0.004 0.004 0.004 0.004 0.003 R2 0.926 0.860 0.916 0.925 0.726 0.941 0.860 0.946 0.963 0.527 Homothetic, Non-proportionality at country level FCTp 0.053∗∗∗ 0.009∗∗∗ 0.023∗∗∗ 0.115∗∗∗ 0.106∗∗∗ 0.064∗∗∗ 0.009∗∗∗ 0.032∗∗∗ 0.105∗∗∗ 0.110∗∗∗ s.e. 0.005 0.002 0.003 0.006 0.003 0.003 0.002 0.003 0.005 0.004 R2 0.827 0.577 0.727 0.883 0.665 0.891 0.577 0.818 0.908 0.456 Homothetic, Non-proportionality at country-industry level FCTp 0.569∗∗∗ 0.608∗∗∗ 0.484∗∗∗ 0.234∗∗∗ 0.610∗∗∗ 0.746∗∗∗ 0.608∗∗∗ 0.786∗∗∗ 0.766∗∗∗ 0.616∗∗∗ s.e. 0.024 0.021 0.013 0.025 0.011 0.012 0.021 0.009 0.010 0.015 R2 0.861 0.476 0.476 0.811 0.815 0.830 0.476 0.979 0.978 0.696 Non-homothetic, Non-proportionality at country level FCTp 0.039∗∗∗ 0.011∗∗∗ 0.015∗∗∗ 0.108∗∗∗ 0.114∗∗∗ 0.048∗∗∗ 0.011∗∗∗ 0.022∗∗∗ 0.083∗∗∗ 0.130∗∗∗ s.e. 0.004 0.002 0.003 0.006 0.003 0.003 0.002 0.002 0.004 0.005 R2 0.786 0.601 0.681 0.875 0.757 0.873 0.601 0.795 0.886 0.670

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

A bilateral test of HOV

  • Derived from world general equilibrium condition

(Hakura, 2001)

  • Note: Not to be confused with bilateral factor content of trade

(Helpman, 1984; Staiger, 1986; Choi and Krishna, 2004; Lai and Zhu, 2007; Foster and Stehrer, 2012)

b(tr − tc) = (V r − V c) + b

  • Sc(Sr)−1 − I
  • fr

= (V r − V c) + b

  • Sc(Sr)−1 − I
  • Srf

= (V r − V c) + b

  • Sc − Sr

f

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Results (RE)

Table 17 - Slope coefficients - RE Total sample Reduced sample Emp. High Medium Low Capital Emp. High Medium Low Capital Strong consumption similarity FCTp 0.139∗∗∗ 0.137∗∗∗ 0.150∗∗∗ 0.135∗∗∗ 0.107∗∗∗ 0.128∗∗∗ 0.137∗∗∗ 0.142∗∗∗ 0.128∗∗∗ 0.102∗∗∗ s.e. 0.001 0.001 0.001 0.001 0.000 0.000 0.001 0.000 0.000 0.000 R2 0.921 0.872 0.917 0.920 0.628 0.960 0.872 0.957 0.967 0.528 Non-homothetic preferences, proportionality FCTp 0.180∗∗∗ 0.157∗∗∗ 0.170∗∗∗ 0.186∗∗∗ 0.113∗∗∗ 0.158∗∗∗ 0.157∗∗∗ 0.163∗∗∗ 0.182∗∗∗ 0.107∗∗∗ s.e. 0.001 0.001 0.001 0.001 0.000 0.001 0.001 0.001 0.001 0.001 R2 0.926 0.861 0.917 0.926 0.726 0.943 0.861 0.949 0.966 0.652 Homothetic, Non-proportionality at country level FCTp 0.055∗∗∗ 0.009∗∗∗ 0.024∗∗∗ 0.118∗∗∗ 0.108∗∗∗ 0.067∗∗∗ 0.009∗∗∗ 0.034∗∗∗ 0.111∗∗∗ 0.108∗∗∗ s.e. 0.001 0.000 0.001 0.001 0.001 0.000 0.000 0.000 0.001 0.001 R2 0.829 0.577 0.730 0.884 0.670 0.898 0.577 0.826 0.917 0.593 Homothetic, Non-proportionality at country-industry level FCTp 0.572∗∗∗ 0.612∗∗∗ 0.486∗∗∗ 0.244∗∗∗ 0.612∗∗∗ 0.744∗∗∗ 0.612∗∗∗ 0.781∗∗∗ 0.762∗∗∗ 0.614∗∗∗ s.e. 0.004 0.003 0.002 0.004 0.002 0.002 0.003 0.001 0.002 0.002 R2 0.862 0.476 0.477 0.812 0.817 0.831 0.476 0.980 0.978 0.772 Non-homothetic, Non-proportionality at country level FCTp 0.040∗∗∗ 0.011∗∗∗ 0.015∗∗∗ 0.112∗∗∗ 0.115∗∗∗ 0.051∗∗∗ 0.011∗∗∗ 0.024∗∗∗ 0.089∗∗∗ 0.119∗∗∗ s.e. 0.001 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.001 0.001 R2 0.789 0.602 0.683 0.877 0.764 0.880 0.602 0.804 0.897 0.728

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen

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Overview Ricardo and HOV - A refresher Testing HOV Testing HOV bilaterally Conclusions

Conclusions

  • HOV was - empirically - never alive
  • HOV + Ricardo + Home bias + Gravity + Linder are doing the job
  • Give credit to other aspects of H&O (see Baldwin, 2010):

∗ Scale effects ∗ Specialization dynamics ∗ Role of factor endowment changes and technology upgrading

Neil Foster and Robert Stehrer, wiiw WIOD 2012, Groningen