Trade and Inequality:           From Theory to EstimationElhanan Helpman   Oleg Itskhoki    Marc Muendler    Stephen Reddi...
Motivation• Neoclassical trade theory (H-O, SF, R)   — sector-level comparative advantage   — focus on “between” effects• N...
This Paper1   Uses matched employer-employee data from Brazil for    1986-1998     — increase in inequality     show     —...
Related Literature• Long and large tradition in labor literature• “New view” empirics:    ◦ Bernard and Jensen (1995). . ....
Brazilian RAIS Data• Matched employer-employee data from 1986–1998   — All workers employed in the formal sector   — Focus...
Sectors • Twelve aggregate sectors (IBGE) 1986-1998     Industry                           Empl’t    Rel. mean    Fraction...
Occupations• Five aggregate occupations 1986-1998        Occupation                    Employment   Relative mean         ...
Non-structural Evidence                                  Within and Between Inequality• Decompose overall wage inequality ...
Within and Between Inequality                                              Sector-occupation bins                         ...
Within and Between Inequality                                                              Sector-occupation bins         ...
Observable and Residual Inequality• Mincer regression:                          wit = zit ϑt + νit ,• Observables: (zit ) ...
Observable and Residual Inequality        .12        .1.04 .06 .08                           Log Wage Dispersion Decomposi...
Observable and Residual Inequality                        Within-Group Wage Inequality Decomposition (Sector-Occupations) ...
Observable and Residual Inequality                                           Level    Growth            Overall wage inequ...
Between-firm Inequality• Mincer log-wage regression with firm fixed effect:                        wit = zit ϑ t + ψj t + νit ...
Between-firm Inequality                                     Within sector-occupation bins                                  ...
Between-firm Inequality                                             Within sector-occupation bins        .08        .06Inde...
Between-firm Inequality                                          Size and exporter wage premia              ωj   t   = λot ...
Between-firm Inequality                                          Size and exporter wage premia                             ...
Non-structural Evidence                                                     Summary1   Within sector-occupation component ...
Structural Exercise• Empirical model of (h, w , ι):                                                       h = αh + µh ι ...
Structural Model                                               Extension of HIR• Melitz (2003) product market:            ...
Model Predictions• A firm with idiosyncratic shock {θ, η, ε}:                                                     β(1−γk)  ...
Econometric Model• Empirical model of Xj = {hj , wj , ιj }j :                                   hj = αh + µh ιj + uj ,  ...
Econometric Model                                           Additional Restrictions1   Positive premia (market access) and...
Shape of the Likelihood Function                         log L−0.415 −0.42−0.425 −0.43−0.435 −0.44                        ...
Shape of the Likelihood Function          0.75                               −                              −33.96        ...
Maximum Likelihood and GMM Fit                                   Unconstrained and Restricted Models        Specification  ...
Parameters                                                                            Structural specification             ...
Model Fit: Firm-level Moments                                Data                Model                                    ...
Model Fit: Worker-level Moments                                    Data                Model                              ...
Employment and Wage Distributions            Log Employment                                     Log Firm Wages 0.5        ...
Worker Wage Distribution       Log Worker Wages                  Exporters vs Non-Exporters                     Data      ...
Dynamic Fit                                                           Dispersion of worker Wages                          ...
Counterfactuals                                                                        Out of sample: variation in τ      ...
Conclusions• Neoclassical trade theory emphasizes wage inequality between  occupations and industries• In contrast, new th...
26 / 25
Wage Inequality                    8.5                                                                                    ...
Trade Openness                                Panel A: Firm Export Participation                                          ...
Regional Robustnesswithin components                  overall           residual                                 inequalit...
Between-Firm Wage Inequality                                          Size and Exporter Premia                            ...
Likelihood Function                          L(Θ|Xj ) =       P{(hj , wj , ιj )|Θ}                                       j...
Unconstrained and Restricted Models                                  αh      αw            ζ       σu     σv     f        ...
Counterfactuals                                                                                          Out of sample    ...
Counterfactuals                                                                                          Out of sample    ...
Sensitivity of Counterfactual                                                                            Out of sample: va...
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Trade and Inequality: From Theory to Estimation

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NES 20th Anniversary Conference, Dec 13-16, 2012
Trade and Inequality: From Theory to Estimation (based on the article presented by Oleg Itskhoki at the NES 20th Anniversary Conference).
aUTHORS: Elhanan Helpman, Harvard; Oleg Itskhoki, Princeton; Marc Muendler, UC San Diego; Stephen Redding, Princeton

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Trade and Inequality: From Theory to Estimation

  1. 1. Trade and Inequality: From Theory to EstimationElhanan Helpman Oleg Itskhoki Marc Muendler Stephen Redding Harvard Princeton UC San Diego Princeton NES 20th Anniversary Conference December 2012 1 / 25
  2. 2. Motivation• Neoclassical trade theory (H-O, SF, R) — sector-level comparative advantage — focus on “between” effects• New trade theory — Krugman: intra-industry trade — Melitz: firm-level comparative advantage — focus on “within” effects• Trade and inequality — Heavily influenced by H-O framework — Empirically has limited explanatory power• “New view” of trade and inequality — link wages to firm performance — within-industry, between-firm 2 / 25
  3. 3. This Paper1 Uses matched employer-employee data from Brazil for 1986-1998 — increase in inequality show — trade liberalization show2 Documents, for both levels and changes, the importance of inequality: ◦ within sector-occupations ◦ for workers with similar observables (residual inequality) ◦ between firms3 Structurally estimates a heterogenous-firm model of firm employment, wages and export status — extension of HIR (2010) — a model of within-sector, between-firm residual inequality — reproduces empirical size and exporter wage premia: wij = α + βhj + δxj + xi γ + εij 3 / 25
  4. 4. Related Literature• Long and large tradition in labor literature• “New view” empirics: ◦ Bernard and Jensen (1995). . . ◦ Verhoogen (2008) ◦ Amity and Davis (2011) ◦ AKM (1999) estimation used in trade context• “New view” theory: ◦ Feenstra and Hanson (1999). . . ◦ Yeaple (2005). . . ◦ Egger and Kreickemeier (2009) ◦ HIR (2010). . . 4 / 25
  5. 5. Brazilian RAIS Data• Matched employer-employee data from 1986–1998 — All workers employed in the formal sector — Focus on the manufacturing sector — Observe firm, industry and occupation — Observe worker education (high school, college degree), demographics (age, sex) and experience (employment history)• Over the period 1986-1998 as a whole, our sample includes more than 7 million workers and 100,000 firms in every year• Trade transactions data from 1986-1998 — Merged with the matched employer-employee data — Observe firm exports and export products and destinations 5 / 25
  6. 6. Sectors • Twelve aggregate sectors (IBGE) 1986-1998 Industry Empl’t Rel. mean Fraction Exporter share (%) log wage Exporters Empl’t %2 Non-metallic mineral products 5.5 −0.12 2.3 32.33 Metallic products 9.8 0.27 6.1 49.94 Machinery, equipment & instr. 6.6 0.38 12.3 54.15 Electrical & telecomm. equip. 6.0 0.37 11.8 56.36 Transport equipment 6.3 0.61 11.2 70.67 Wood products & furniture 6.5 −0.48 3.2 23.58 Paper, publishing & printing 5.4 0.14 2.5 30.69 Rubber, tobacco, leather & fur 7.0 −0.04 8.6 50.810 Chemical & pharma. products 9.9 0.40 11.2 50.611 Apparel & textiles 15.7 −0.32 2.5 34.812 Footwear 4.4 −0.44 12.2 65.713 Food, beverages & alcohol 16.9 −0.30 3.9 38.0 • More than 250 disaggregated industries (CNAE) 1994-1998 6 / 25
  7. 7. Occupations• Five aggregate occupations 1986-1998 Occupation Employment Relative mean share (%) log wage 1 Professional and Managerial 7.8 1.08 2 Skilled White Collar 11.1 0.40 3 Unskilled White Collar 8.4 0.13 4 Skilled Blue Collar 57.4 −0.15 5 Unskilled Blue Collar 15.2 −0.35• More than 300 disaggregated occupations (CBO) 1986-1998 6 / 25
  8. 8. Non-structural Evidence Within and Between Inequality• Decompose overall wage inequality into within and between sector-occupation components Tt = Wt + Bt 1 Tt = i∈ (wit − wt )2 ¯ Nt 1 Wt = i∈ (wit − w t )2 ¯ Nt 1 Bt = N t (w t − wt )2 ¯ ¯ Nt◦ i – workers◦ – sectors, occupations or sector-occupation bins 7 / 25
  9. 9. Within and Between Inequality Sector-occupation bins Level Growth Panel A (1990) (1986-95) Within occupation 80% 92% Within sector 83% 73% Within sector–occupation 67% 67% Within detailed-occupation 58% 60% Within sector–detailed-occupation 52% 54% Panel B (1994) (1994-98) Within sector–occupation 68% 125% Within detailed-sector–detailed-occupation 47% 140%Fact 1Within sector-occupation component of wage inequality accountsfor over 2/3 of both level and growth of wage inequality 8 / 25
  10. 10. Within and Between Inequality Sector-occupation bins .12 .1 .04 .06 .08 Index (1986=0) .02 Log Wage Dispersion (Sector-Occupations) 1986 1990 1994 1998 Year Total Within sector-occupation Between sector-occupationJump to Between Firm 8 / 25
  11. 11. Observable and Residual Inequality• Mincer regression: wit = zit ϑt + νit ,• Observables: (zit ) ◦ Education (high-school, college degree) ◦ Age bins and male/female ◦ Experience quintiles• Observable-residual inequality decomposition: ˆ var (wit ) = var zit ϑt + var (ˆit ) ν• Decompose residual (within-group) inequality into within and between sector-occupation inequality 9 / 25
  12. 12. Observable and Residual Inequality .12 .1.04 .06 .08 Log Wage Dispersion Decomposition Index (1986=0) .02 1986 1990 1994 1998 Year Total Observables Within-Group 9 / 25
  13. 13. Observable and Residual Inequality Within-Group Wage Inequality Decomposition (Sector-Occupations) .08 .06 Index (1986=0).02 .04 1986 1990 1994 1998 Year Overall Within Sector-Occupation Between Sector-Occupation 9 / 25
  14. 14. Observable and Residual Inequality Level Growth Overall wage inequality (1990) (1986-95) Observables inequality 43% 52% Residual wage inequality 57% 48% — within sector–occupation 88% 91%Fact 2(i) Residual inequality is at least as important as workerobservables for both level and growth of wage inequality(ii) Almost all residual inequality is within sector-occupations 9 / 25
  15. 15. Between-firm Inequality• Mincer log-wage regression with firm fixed effect: wit = zit ϑ t + ψj t + νit — i worker — j firm — sector-occupation bin• ψj t firm fixed effect includes: — Returns to unobserved skill (workforce composition) — Worker rents (differences in wage for same workers) — Match effects• Decomposition of within inequality: ˆ ◦ Observables: var zit ϑ t ◦ Between-firm component: var ψj t ˆ ˆ t , ψj t ◦ Covariance: cov zit ϑ ˆ ◦ Within-firm component: var νit ˆ 10 / 25
  16. 16. Between-firm Inequality Within sector-occupation bins Level Growth A. Unconditional (1990) (1986-1995) Between-firm wage inequality 55% 115% Within-firm wage inequality 45% −15% B. Controlling for Worker Observables Between-firm wage inequality 38% 86% Within-firm wage inequality 34% −11% Worker observables 17% 2% Covariance observables and firm effects 11% 24%Fact 3Between-firm component account for about half of level and themajority of growth of within sector-occupation wage inequality 10 / 25
  17. 17. Between-firm Inequality Within sector-occupation bins .08 .06Index (1986==0) .02 0 .04 -.02 1986 1990 1994 1998 Year Total Worker Obs Covariance Between-Firm Within-Firm 10 / 25
  18. 18. Between-firm Inequality Size and exporter wage premia ωj t = λot + λst hj t + λxt ιjt + νj t ωj t ¯ ˆ ∈ {wj t , ψj t }◦ Size premium: λst = 0.12◦ Exporter premium: λxt = 0.20◦ Residual variation: R 2 = 0.12 more 11 / 25
  19. 19. Between-firm Inequality Size and exporter wage premia worker observables unconditional firm-occupation-year average wage, wj t ¯ ˆ fixed effect, ψj t Cross-Section Panel Cross-Section Panel 1990 1986-1998 1990 1986-1998Firm Size 0.126∗∗∗ −0.003∗ 0.118∗∗∗ 0.048∗∗∗ (0.008) (0.002) (0.006) (0.001)Firm Export Status 0.202∗∗∗ 0.216∗∗∗ 0.088∗∗∗ 0.012∗∗∗ (0.046) (0.004) (0.027) (0.002)Industry Fixed Effects yes no yes noFirm Fixed Effects no yes no yesWithin R-squared 0.164 0.007 0.146 0.013Observations 93, 392 1, 229, 133 93, 392 1, 229, 133Fact 4Larger firms on average pay higher wages; exporters on averagepay higher wages even after controlling for size. This holds both inthe cross-section and for a given firm over time. 12 / 25
  20. 20. Non-structural Evidence Summary1 Within sector-occupation component accounts for 2/3 of overall wage inequality2 Residual inequality is at least as important as worker observables3 Between-firm component accounts for majority of growth of within sector-occupation inequality Regional robustness4 Trade participation correlates with the between-firm wage component 13 / 25
  21. 21. Structural Exercise• Empirical model of (h, w , ι):   h = αh + µh ι + u, w = αw + µw ι + ζu + v , ι = I{z ≥ f },  where (u, v , z) is a firm-specific draw• Reproduce size and exporter wage relationship: w = λo + λs h + λx ι + ν• Explore the link between trade and (h, w ) distribution: — Selection effect: cov(u, z), cov(v , z) > 0 — Market access effect: µh , µw > 0• Model-based structural counterfactuals — reduction in trade costs: joint movement in (µh , µw , f ) Jump to estimation results 14 / 25
  22. 22. Structural Model Extension of HIR• Melitz (2003) product market: R = ΥAy β , Υ ∈ {1, Υx > 1}• Complementarity between productivity and worker ability y = e θ H γ ¯, a γ<1• Unobserved heterogeneity and costly screening k e −η Cac /δ δ ⇒ ¯= a ac k −1• DMP frictional labor market and wage bargaining βγ R k/δ W = = bac 1 + βγ H• Heterogeneity in fixed cost of exports e ε Fx 15 / 25
  23. 23. Model Predictions• A firm with idiosyncratic shock {θ, η, ε}: β(1−γk) 1−β β δΓ R(θ, η, ε) = κr Υ Γ eθ Γ eη β(1−γk)(1−k/δ) k (1−β)(1−k/δ) β(1−k/δ) δΓ −δ H(θ, η, ε) = κh Υ Γ eθ Γ eη k(1−β) βk k δ (1+ β(1−γk) ) δΓ W (θ, η, ε) = κw Υ δΓ eθ δΓ eη• Market access variable β Ax Υ = 1 + Ix Υx − 1 , Υx = 1 + τ − 1−β Ad• Selection into exporting β(1−γk) 1−β β δΓ Ix = κπ Υx Γ − 1 eθ Γ eη ≥ Fx e ε 15 / 25
  24. 24. Econometric Model• Empirical model of Xj = {hj , wj , ιj }j :   hj = αh + µh ιj + uj , wj = αw + µw ιj + ζuj + vj , ιj = I{zj ≥ f } • Distributional assumption 2   σu 0 ρu σu 2 (uj , vj , zj ) ∼ N (0, Σ), Σ= 0 σv ρv σv    ρu σu ρv σv 1• Parameter vector Θ = {αh , αw , ζ, σθ , ση , ρθ , ρη , µh , µw , f } 1 1−β f = αf + log Fx − log Υx Γ − 1 σ 1−β µh + µw = Υx Γ• Theoretical restrictions: µh , µw > 0. Likelihood function 16 / 25
  25. 25. Econometric Model Additional Restrictions1 Positive premia (market access) and selection: µh , µw , ρu , ρv > 02 Orthogonal structural shocks: ρu /ρv = (1 + ζ)σu /σv3 Orthogonal productivity shocks: µw ≥ ζµh4 No effects of trade on wage distribution µh = µw = ρu = ρv = 0 16 / 25
  26. 26. Shape of the Likelihood Function log L−0.415 −0.42−0.425 −0.43−0.435 −0.44 1 −0.5 0.5 0 0 0.5 −0.5 µw 1 −1 ρv ˜ 17 / 25
  27. 27. Shape of the Likelihood Function 0.75 − −33.96 .95 µw ≥ 0 −3 −3.938 .9 −3 4 0.5 .9 −3 −3 6 .9 −3.98 −3 4 .9 .9 7 5 µ w = ζµ h −3 − −. 9 −3 3.96 −3.944 Global Max .97 3 8 .9 5 0.25 −3.935 −3 −3 −3.98 .9 .97 6 −3 .9 − 5 −3 3.96 ρv 0 .95 −3 .9 7 −3 .9 8 −3 −3 .9 .9 −0.25 8 Orthog shocks −3 7 .9 6 −3.945 −3 −3 .9 −3. 5 .96 −3 95 −3.97 −0.5 .9 8 Local Max −3.9 . 95 −3.939 6 −3 −0.75 −0.5 −0.25 0 0.25 0.5 0.75 1 µwShow ML Estimates 17 / 25
  28. 28. Maximum Likelihood and GMM Fit Unconstrained and Restricted Models Specification log L LR Sqrt. GMM Obj. (i) Unconstrained −39,349.6 — 0.013(ii) µh , µ w > 0 −39,381.0 62.8 0.021(iii) µw = ζµh −39,441.3 183.4 0.034(iv) Orthogonal structural shocks −39,445.2 191.1 0.035(v) No trade effects −49,543.6 20,387.9 0.370 µh = µw = ρ u = ρ v = 0 • No premia (µh = µw = 0) strongly rejected • No selection (ρθ = ρη = 0) NOT strongly rejected • Pure noise in wages strongly rejected 18 / 25
  29. 29. Parameters Structural specification αh and αw σu σv 2.9 −0.25 1.2 0.45 2.8 −0.3 1.1 0.4 2.7 −0.35 2.6 −0.4 1 0.35 86 88 90 92 94 96 98 86 88 90 92 94 96 98 86 88 90 92 94 96 98 ζ ρu ρv0.12 0.25 0.04 0.2 0.1 0.15 0.020.08 0.10.06 0 0.05 86 88 90 92 94 96 98 86 88 90 92 94 96 98 86 88 90 92 94 96 98 f µh µw 2.6 1.6 2.4 1.5 2.2 0.2 1.4 2 1.3 1.8 0.1 86 88 90 92 94 96 98 86 88 90 92 94 96 98 86 88 90 92 94 96 98 19 / 25
  30. 30. Model Fit: Firm-level Moments Data Model Unconstrained StructuralA. Full Sample Mean Employment 2.97 2.97 2.97 Mean Wages −0.340 −0.341 −0.341 St. Dev. Employment 1.27 1.27 1.27 St. Dev. Wages 0.424 0.424 0.423 Covar Empl’t and Wages 0.352 0.354 0.352 Fraction of Exporters 5.5% 5.5% 5.5%B. Non-exporters Mean Employment 2.83 2.83 2.83 Mean Wages −0.359 −0.360 −0.360 St. Dev. Employment 1.12 1.14 1.14 St. Dev. Wages 0.419 0.419 0.416 Covar Empl’t and Wages 0.306 0.309 0.304C. Exporters Mean Employment 5.28 5.29 5.29 Mean Wages −0.010 −0.010 −0.006 St. Dev. Employment 1.52 1.14 1.14 St. Dev. Wages 0.366 0.373 0.417 Covar Empl’t and Wages 0.322 0.289 0.304 20 / 25
  31. 31. Model Fit: Worker-level Moments Data Model Unconstrained StructuralA. First and Second Moments Mean Wage 0.000 −0.085 −0.072 Non-exporters −0.122 −0.213 −0.215 Exporters 0.152 0.109 0.140 St. dev. Wages 0.404 0.429 0.450 Non-exporters 0.407 0.415 0.416 Exporters 0.345 0.374 0.414 Empl’t Share of Exporters 44.6% 40.0% 40.3%B. Wage Inequality Measures Gini 0.215 0.236 0.250 90/10 2.77 3.00 3.18 90/50 1.54 1.70 1.79 50/10 1.80 1.76 1.78C. Size and Exporter Wage Premia Size premium 0.111 0.112 0.111 Exporter premium 0.077 0.075 0.082 21 / 25
  32. 32. Employment and Wage Distributions Log Employment Log Firm Wages 0.5 1 Data Model0.25 0.5 0 0 −2 0 2 4 6 8 10 −2 −1 0 1 Exporters vs Non-Exporters Exporters vs Non-Exporters 0.5 10.25 0.5 0 0 −2 0 2 4 6 8 10 −2 −1 0 1 Data (non-exp) Data (exporters) Model (non-exp) Model (exporters) 22 / 25
  33. 33. Worker Wage Distribution Log Worker Wages Exporters vs Non-Exporters Data Model 1 10.5 0.5 0 0 −2 −1 0 1 2 −2 −1 0 1 2 22 / 25
  34. 34. Dynamic Fit Dispersion of worker Wages 0.07 Data 0.06 Structural ModelVariance log worker wages 0.05 0.04 0.03 0.02 0.01 0 86 88 90 92 94 96 98 23 / 25
  35. 35. Counterfactuals Out of sample: variation in τ Variance log worker wages 0.22 0.21 0.2 0.19 0.18 Estimate 1986 1990 0.17 0 1 2 3 4 5 6 7 8 (1−β)/Γ log ΥxShow sensitivity 24 / 25
  36. 36. Conclusions• Neoclassical trade theory emphasizes wage inequality between occupations and industries• In contrast, new theories of firm heterogeneity and trade point to wage dispersion within occupations and industries• Using matched employer-employee data for Brazil, we show that • Much of the increase in wage inequality since the mid-1980s has occurred within sector-occupations • Increased within-group wage inequality • Increased wage dispersion between firms • Between-firm wage dispersion related to trade participation• Develop a framework for the structural estimation of a model with firm heterogeneity and wage dispersion across firms• Use this framework to quantify the contribution of changes in trade openness to wage inequality and to undertake counterfactuals 25 / 25
  37. 37. 26 / 25
  38. 38. Wage Inequality 8.5 .75 Variance log wage (U.S. dollars) Mean log wage (U.S. dollars) .7 8 .65 7.5 1986 1990 1994 1998 Year Mean log wage (left scale) Variance log wage (right scale)Back to slides 27 / 25
  39. 39. Trade Openness Panel A: Firm Export Participation .54 .09 .52 Share of Employment .08 Share of Firms .5 .07 .48 .06 .46 .05 .44 1986 1990 1994 1998 Year Share Exporters Exp Employ ShareBack to slides 28 / 25
  40. 40. Regional Robustnesswithin components overall residual inequality inequality Level Change Level Change 1990 1986–95 1990 1986–95Sector-occupation 67 67 88 91Sector-occupation, S˜o Paolo a 66 49 91 71Sector-occupation-state 57 38 73 57Sector-occupation-meso 53 30 69 49 Back to slides 29 / 25
  41. 41. Between-Firm Wage Inequality Size and Exporter Premia Level Growth A. Unconditional (1990) (1986-1995) Firm Employment 7% 5% Firm Export Status 3% 6% Covar Employment-Export Status 3% 5% Residual 88% 84% B. Controlling for Worker Observables Firm Employment 7% 2% Firm Export Status 2% 4% Covar Employment-Export Status 2% 3% Residual 89% 91%Back to slides 30 / 25
  42. 42. Likelihood Function L(Θ|Xj ) = P{(hj , wj , ιj )|Θ} j P{(hj , wj , ιj )|Θ} = 1−ιj ιj1 1 f − ρu uj − ρv vj ˆ ˆ f − ρ u u j − ρ v vj ˆ ˆ φ uj ˆ φ vj ˆ Φ 1−Φσu σv 1 − ρ2 − ρ2 u v 1 − ρ2 − ρ2 u v hj − αh − µh ιj uj = ˆ , σu (wj − αw − µw ιj ) − ζ(hj − αh − µh ιj ) vj = ˆ σv back to slides 31 / 25
  43. 43. Unconstrained and Restricted Models αh αw ζ σu σv f 1990 2.84 −0.35 0.11 1.14 0.40 1.60 1994 2.75 −0.35 0.10 1.08 0.41 1.35 1990 1994 ρu µh ρv µw ρu µh ρv µw (i) Maximum, µw < 0 0.14 2.11 0.53 −0.16 0.07 1.91 0.48 −0.06 (ii) µh , µw ≥ 0 0.09 2.24 0.39 0.00 0.06 1.94 0.42 0.00(iii) µw = ζµh 0.02 2.40 0.10 0.27 0.02 2.02 0.20 0.20(iv) Orthogonal shocks 0.00 2.45 0.00 0.35 0.00 2.07 0.00 0.36 (v) Maximum, ρv < 0 −0.03 2.53 −0.48 0.78 0.03 2.01 −0.55 0.82 back to slides 32 / 25
  44. 44. Counterfactuals Out of sample 0.46 Counterfactual 0.455Standard Deviation of Log Wages 0.45 0.445 0.44 0.435 0.43 Data 0.425 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Exporter Employment Share 33 / 25
  45. 45. Counterfactuals Out of sample 0.46 Counterfactual 0.455Standard Deviation of Log Wages 0.45 0.445 0.44 0.435 0.43 Data 0.425 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Exporter Employment Share 33 / 25
  46. 46. Sensitivity of Counterfactual Out of sample: variation in τ 0.25 0.24 0.23 Variance log worker wages 0.22 0.21 0.2 0.19 0.18 0.17 0.16 0.15 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Exporter Employment Shareback to slides 34 / 25

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