Intro Model Analytics Quantitative results Conclusion Importing Skill-Biased Technology Ariel Burstein Javier Cravino Jonathan Vogel January 2012
Intro Model Analytics Quantitative results Conclusion Intro Motivation Observations Capital equipment (e.g. computers and industrial machinery): embodies skill-biased technology At …rm, sector, plant level, surveyed in Katz & Autor ’99 is highly traded and world production is highly concentrated Eaton and Kortum ’01 Implication Countries import skill-biased technology with equipment This paper To what extent does trade in equipment raise demand for skilled labor and increase skill premia in many countries?
Intro Model Analytics Quantitative results Conclusion Intro Framework Introduce capital-skill complementarity into a multi-country, multi-sector Ricardian model of trade Capital-skill complementarity: " in capital " demand for skilled relative to unskilled labor With trade, capital stock depends on domestic productivities and factor supplies, foreign productivities and factor supplies, and trade costs
Intro Model Analytics Quantitative results Conclusion Intro Preview of analytic results All changes in trade costs foreign technologies foreign factor supplies a¤ect domestic skill premium only through changes in domestic sectoral expenditure shares, π ii ( j ) Analytic 1 st -order approx for SS change in skill premium highlights intuition facilitates sensitivity analysis
Intro Model Analytics Quantitative results Conclusion Intro Preview of quantitative results Two counterfactuals taking changes in trade shares as given:
Intro Model Analytics Quantitative results Conclusion Intro Preview of quantitative results Two counterfactuals taking changes in trade shares as given: Counterfactual 1: Move to autarky E¤ect varies widely across countries in our sample Large in countries with comparative disadvantage in equipment Skill Premium falls: e.g., 16 % in median country, 5 % in US, 20 % in Chile
Intro Model Analytics Quantitative results Conclusion Intro Preview of quantitative results Two counterfactuals taking changes in trade shares as given: Counterfactual 1: Move to autarky E¤ect varies widely across countries in our sample Large in countries with comparative disadvantage in equipment Skill Premium falls: e.g., 16 % in median country, 5 % in US, 20 % in Chile Counterfactual 2: Feed observed changes in trade shares Moving from 2000 to 1963 trade shares, skill premium falls: e.g., 13 % in UK, 19 % in Canada Numbers signi…cant relative to observed changes in skill premia
Intro Model Analytics Quantitative results Conclusion Intro Related literature Evidence on trade and technology change: Pavcnik (’02), De Loecker (’10), Lileeva & Tre‡er (’10), Bustos (’11a) Evidence on trade and skill intensity: Verhoogen (’08), Bloom et. al. (’11), Bustos (’11b), Koren & Csillag (’11) Trade and SBTC: Acemoglu (2003), Yeaple (’05), Thoenig and Verdier (2003) Capital skill complementarity and skill premium Krusell et. al. (’00), Polgreen & Silos (’08) Quantitative trade models and inequality: Parro (’10), Burstein & Vogel (’10)
Intro Model Analytics Quantitative results Conclusion Model
Intro Model Analytics Quantitative results Conclusion Model Model: Overview I countries, 3 sectors (Manufacturing, Equipment and Services) M used for consumption and intermediate inputs S used for consumption, intermediate inputs and structures E used for capital equipment Production uses skilled and unskilled labor, H i and L i capital structures and equipment, K i ( S ) and K i ( E ) intermediate inputs, X i ( S ) and X i ( M ) Countries endowed with labor, capital is accumulated Factors and goods markets are perfectly competitive Iceberg trade costs
Intro Model Analytics Quantitative results Conclusion Model Model: Preferences and …nal output Preferences: h C i , t ( M ) φ C i , t ( S ) 1 � φ i ∞ β t u ∑ t = 0 Sectorial output is an aggregate of intermediates: � Z 1 � η / ( η � 1 ) 0 q i ( ω , j ) ( η � 1 ) / η d ω Y i ( j ) = Market clearing in …nal goods: Y i ( M ) = C i ( M ) + X i ( M ) Y i ( S ) = C i ( S ) + X i ( S ) + I i ( S ) Y i ( E ) = I i ( E )
Intro Model Analytics Quantitative results Conclusion Model Production of intermediate goods KORV production function—nested CES using H i , L i , K i ( S ) , K i ( E ) —w/ intermediate inputs & heterogeneous productivity
Intro Model Analytics Quantitative results Conclusion Model Production of intermediate goods KORV production function—nested CES using H i , L i , K i ( S ) , K i ( E ) —w/ intermediate inputs & heterogeneous productivity y i ( ω , j ) = A i ( j ) z i ( ω , j ) � [ Int . Inputs ] 1 � ζ � [ VA ] ζ Productivity: A i ( j ) sectoral, z i ( ω , j ) idiosyncratic: z i ( ω , j ) = u � θ , u � exp ( 1 )
Intro Model Analytics Quantitative results Conclusion Model Production of intermediate goods KORV production function—nested CES using H i , L i , K i ( S ) , K i ( E ) —w/ intermediate inputs & heterogeneous productivity y i ( ω , j ) = A i ( j ) z i ( ω , j ) � [ Int . Inputs ] 1 � ζ � [ VA ] ζ Productivity: A i ( j ) sectoral, z i ( ω , j ) idiosyncratic: z i ( ω , j ) = u � θ , u � exp ( 1 ) Int . Inputs = x ε S x 1 � ε M S χ 1 � α VA = k α 2
Intro Model Analytics Quantitative results Conclusion Model Production of intermediate goods KORV production function—nested CES using H i , L i , K i ( S ) , K i ( E ) —w/ intermediate inputs & heterogeneous productivity y i ( ω , j ) = A i ( j ) z i ( ω , j ) � [ Int . Inputs ] 1 � ζ � [ VA ] ζ Productivity: A i ( j ) sectoral, z i ( ω , j ) idiosyncratic: z i ( ω , j ) = u � θ , u � exp ( 1 ) Int . Inputs = x ε S x 1 � ε M S χ 1 � α VA = k α 2 � � σ � 1 1 σ 1 σ � 1 σ χ σ l σ + ( 1 � µ ) σ � 1 ! ε ( l , Υ 1 ) = σ χ 2 = σ µ 1 � � ρ ρ � 1 ρ � 1 ρ � 1 1 1 ρ k ρ ρ h χ 1 = + ( 1 � λ ) ! ε ( k E , h ) = ρ λ ρ E Capital skill complementarity if σ > ρ
Intro Model Analytics Quantitative results Conclusion Model Equilibrium Unit cost of producer ( ω , j ) : c i τ in ( j ) c in ( ω , j ) = A i ( j ) z i ( ω , j ) Prices: p n ( ω , j ) = min f c in ( ω , j ) g , i Price indexes: � Z 1 � 1 / ( 1 � η ) 0 p n ( ω , j ) 1 � η d ω P n ( j ) = . Trade share: R 1 0 p n ( ω , j ) 1 � η 1 I in ( ω , j ) d ω π in ( j ) = P n ( j ) 1 � η
Intro Model Analytics Quantitative results Conclusion Analytic Results
Intro Model Analytics Quantitative results Conclusion Skill Premium Following KORV: " # σ � ρ � K i ( E ) � ρ � 1 ( ρ � 1 ) σ � L i � 1 s i ρ σ 1 1 = κ λ + ( 1 � λ ) ρ ρ w i H i H i w i increasing in L i s i H i if σ > 0 w i increasing in K i ( E ) s i if σ > ρ H i
Intro Model Analytics Quantitative results Conclusion Skill Premium Following KORV: " # σ � ρ � K i ( E ) � ρ � 1 ( ρ � 1 ) σ � L i � 1 s i ρ σ 1 1 = κ λ + ( 1 � λ ) ρ ρ w i H i H i w i increasing in L i s i H i if σ > 0 w i increasing in K i ( E ) s i if σ > ρ H i K i ( E ) determined in equilibrium
Intro Model Analytics Quantitative results Conclusion Result 1 Result Proposition Given parameters, country i’s steady state skill premium can be calculated using only Domestic expenditure shares, π ii ( j ) ’s 1 Domestic technologies, A i ( j ) ’s 2 Domestic endowments, H i and L i 3 Implication : π ii ( j ) ’s are su¢cient statistics for all international forces Only need data on the domestic country for each counterfactual
Intro Model Analytics Quantitative results Conclusion Result 1 Broad Intuition In trade models with gravity, change in stock of consumption resulting from foreign shocks is a function of π ii Arkolakis, Costinot, Rodriguez-Clare (2011) e.g., in EK (2002), Q i _ A i π � θ ii Here, changes in skill premium depend on changes in K i ( E ) And K i ( E ) depends on A i ( j ) and π ii ( j ) in a related manner...
Intro Model Analytics Quantitative results Conclusion Approximation First-order approximation for the change in SP Log linearizing, the change in s i / w i is given by h i � � w i = ∑ b H i � b b b s i � b A i ( j ) � θ b � β 2 , i β 1 , i ( j ) π ii ( j ) L i j β 1 , i ( j ) , β 2 , i are functions of factor shares and parameters Two ways to increase stock of equipment: produce more ( b A i ( E ) > 0) import more ( b π ii ( E ) < 0)
Intro Model Analytics Quantitative results Conclusion Approximation First-order approximation for the change in SP Log linearizing, the change in s i / w i is given by h i � � w i = ∑ b H i � b b b s i � b A i ( j ) � θ b � β 2 , i β 1 , i ( j ) π ii ( j ) L i j β 1 , i ( j ) , β 2 , i are functions of factor shares and parameters Two ways to increase stock of equipment: produce more ( b A i ( E ) > 0) import more ( b π ii ( E ) < 0) h i b A i ( j ) � θ b " for j 6 = E ) stock of equipment " π ii ( j ) Production of equipment uses intermediates from j 6 = E
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