Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Skill Premium Divergence: The Roles of Trade, Capital and Demographics

954 views

Published on

Sang-Wook (Stanley) Cho, Julian P. Diaz
Prepared for the Bank of Estonia
October 2017

Published in: Economy & Finance
  • Be the first to comment

  • Be the first to like this

Skill Premium Divergence: The Roles of Trade, Capital and Demographics

  1. 1. Skill Premium Divergence: The Roles of Trade, Capital and Demographics Sang-Wook (Stanley) Cho Juli´an P. D´ıaz School of Economics Department of Economics Australian School of Business Quinlan School of Business University of New South Wales Loyola University Chicago Prepared for the Bank of Estonia October 2017
  2. 2. What drives the patterns of the skill premium? – No unanimous consensus reached on which factors definitively drive the patterns of the skill premium (ws/wu), but a few hypotheses have emerged as prime candidates: – Technological change that favors skilled workers that are complementary to capital than unskilled workers.
  3. 3. What drives the patterns of the skill premium? – No unanimous consensus reached on which factors definitively drive the patterns of the skill premium (ws/wu), but a few hypotheses have emerged as prime candidates: – Technological change that favors skilled workers that are complementary to capital than unskilled workers. – The expansion of trade that encourages production in sectors that use a particular type of labor intensively.
  4. 4. What drives the patterns of the skill premium? – No unanimous consensus reached on which factors definitively drive the patterns of the skill premium (ws/wu), but a few hypotheses have emerged as prime candidates: – Technological change that favors skilled workers that are complementary to capital than unskilled workers. – The expansion of trade that encourages production in sectors that use a particular type of labor intensively. – Changes in the skill composition of the population that make skilled/unskilled labor more abundant/scarce.
  5. 5. What drives the patterns of the skill premium? – No unanimous consensus reached on which factors definitively drive the patterns of the skill premium (ws/wu), but a few hypotheses have emerged as prime candidates: – Technological change that favors skilled workers that are complementary to capital than unskilled workers. – The expansion of trade that encourages production in sectors that use a particular type of labor intensively. – Changes in the skill composition of the population that make skilled/unskilled labor more abundant/scarce. – Problem: most articles focus on one factor at a time, neglecting their potential interactions. – Aimed at accounting episodes of skill premium increases or decreases.
  6. 6. What Do We Do? – We construct a static general equilibrium model that incorporates all three factors simultaneously. – Our model allows us to assess the individual and joint effects of each factor on the skill premium. – Since our framework includes factors that affect the demand and supply for each type of labor, their overall effect could theoretically lead to skill premium increases or decreases. – To assess whether our model’s predictions are in line with the data, we apply it to account for the patterns of the skill premium in the three Baltic states: Estonia, Latvia and Lithuania.
  7. 7. Why the Baltics? – As they transitioned from centrally-planned to free-market systems: – Aggressively opened their economies to the rest of the world. – Experienced rapid expansions in their stocks of capital. – Went through significant changes in the skill composition of their labor forces. – Moreover, they displayed very similar levels of the skill premium in 1995 (around 1.8). – Since then, the Baltic skill premia took divergent patterns.
  8. 8. 1.2 1.5 1.8 2.1 2.4 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Skill Premium Estonia, Latvia and Lithuania Latvia Lithuania Estonia “Skill” definition is educational attainment: skilled workers are those with tertiary education, unskilled workers are those with non-tertiary education. Data from World Input Output Database (WIOD).
  9. 9. 70 80 90 100 110 120 130 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Skill Premium (1995 = 100) Latvia Lithuania Estonia By 2008: skill premium in Latvia had increased by approximately 16%, while in Estonia and Lithuania it had declined by around 20% and 13%, respectively.
  10. 10. Although the Baltics are usually referred to as a homogeneous bloc, there are some crucial differences along capital accumulation: 0 100 200 300 400 500 600 700 800 900 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Equipment Capital Stock (1995 = 100) Estonia, Latvia and Lithuania Latvia Lithuania Estonia 0 100 200 300 400 500 600 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Structure Capital Stock (1995 = 100) Estonia, Latvia and Lithuania Latvia Lithuania Estonia Changes in Capital Stock (1995-2008) Equipment Structures Estonia Latvia Lithuania Estonia Latvia Lithuania 431.0% 774.5% 620.5% 254.3% 387.7% 184.0% Source: PWT and author’s calculations.
  11. 11. Although the Baltics are usually referred to as a homogeneous bloc, there are some crucial differences in the relative skill supply: 0 50 100 150 200 Population with Tertiary Education (1995 = 100) Series1 Series2 Series3 Series4 Estonia Latvia Lithuania 1995 2000 2005 20081995 2000 2005 20081995 2000 2005 2008 60 80 100 120 Population without Tertiary Education (1995 = 100) Series1 Series2 Series3 Series4 1995 2000 2005 20081995 2000 2005 20081995 2000 2005 2008 Estonia Latvia Lithuania Changes in Skill Composition of Population Ages 15+ (1995-2008) Tertiary Education Non-Tertiary Education Estonia Latvia Lithuania Estonia Latvia Lithuania 53.0% 31.0% 91.1% -15.3% -5.8% -12.7% Source: Barro and Lee educational attainment database. Estonia’s tertiary educated population increased from 0.21 to 0.32 million during this period.
  12. 12. Although the Baltics are usually referred to as a homogeneous bloc, there are some crucial differences in the patterns of terms of trade: 60 80 100 120 140 160 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Terms of Trade (Goods) (1995 = 100) Lithuania Latvia Estonia 60 80 100 120 140 160 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Terms of Trade (Services) (1995 = 100) Lithuania Latvia Estonia Changes in Terms of Trade (1995-2008) Goods Services Estonia Latvia Lithuania Estonia Latvia Lithuania 14.5% -10.9% 29.9% 34.0% 8.3% 41.8% Source: AMECO database
  13. 13. – Summarizing: – Equipment capital grew at the fastest rate in Latvia and the slowest in Estonia. – Skilled population grew faster in Lithuania and Estonia and more slowly in Latvia. – Unskilled population shrank faster in Estonia and Lithuania and more slowly in Latvia. – Terms of trade in the goods sector improved in Estonia and Lithuania, but worsened in Latvia.
  14. 14. The Model – Main ingredients: – Two-country static general equilibrium model: Baltic state and ROW. – Impose small-economy assumption on Baltic state. – Agents: households, firms, government, rest of the world. – Two types of labor: skilled ( s) and unskilled ( u). Two types of capital: equipment (ke) and structures (kz). – Multi-sector model: goods and services with different skill intensities.
  15. 15. Flow chart aggregate economy Households Producers Government Rest of the World direct taxes capital (equipment and structures) labor (skilled and unskilled) goods and services indirect taxes tariffs goods and services inter/intra industry transactions goods and services imported componentssavings (trade balance)
  16. 16. Production plans domestic production firms final production firms intermediate inputs labor (skilled and unskilled) imported goods (m) capital (equipment and structure)
  17. 17. Final goods production – Final production in sector i — Armington aggregator: yi = φi δi y ρm,i d,i + (1 − δi )y ρm,i f ,i 1 ρm,i yf ,i is the imported component with foreign price ¯pf ,i , which the Baltic state takes as given.
  18. 18. Final goods production – Final production in sector i — Armington aggregator: yi = φi δi y ρm,i d,i + (1 − δi )y ρm,i f ,i 1 ρm,i yf ,i is the imported component with foreign price ¯pf ,i , which the Baltic state takes as given. – ρm,i is the parameter that governs the elasticity of substitution between domestic and imported components in sector i – Imports of commodity i are subject to ad-valorem tariff rate τf ,i (negligible for the Baltics)
  19. 19. Domestic goods production – Domestic component production in sector i — Nested CES: yd,i = min    x1,i a1,i , . . . , xn,i an,i , βi kαi z,i λi µi kρ e,i + (1 − µi ) ρ s,i σ ρ + (1 − λi ) σ u,i 1−αi σ    – αi , µi and λi are the share parameters of inputs in value added – ρ captures the elasticity of substitution between equipment and skilled labor; and σ governs the elasticity of substitution between unskilled labor and equipment or skilled labor – “Capital-skill complementarity” when σ > ρ (or 1 1−σ > 1 1−ρ)
  20. 20. Household – Utility function of household of type j: Uj =  ζj i∈I θj i cη i,j + θj inv (cinv,j + cb,j )η ψ η + (1 − ζj )(¯Lj − j )ψ   1 ψ subject to i∈I pi ci,j + pinv (cinv,j + cb,j ) = (1 − tj d )(wj j + re ¯ke,j + rz ¯kz,j ) – cinv,j and cb,j refer to domestic savings and government-issued bonds.
  21. 21. Skill Premium in the Model – Using the FOCs, we can derive the expression for the skill premium: ws wu = λi (1 − µi ) 1 − λi µi ke,i s,i ρ + (1 − µi ) σ−ρ ρ s,i u,i σ−1
  22. 22. Skill Premium in the Model – Using the FOCs, we can derive the expression for the skill premium: ws wu = λi (1 − µi ) 1 − λi µi ke,i s,i ρ + (1 − µi ) σ−ρ ρ s,i u,i σ−1 – Expressing in growth rates terms (γ): γSP µi (σ − ρ) ke,i s,i ρ (γke,i − γ s,i ) + (σ − 1)(γ s,i − γ u,i )
  23. 23. Skill Premium in the Model – Using the FOCs, we can derive the expression for the skill premium: ws wu = λi (1 − µi ) 1 − λi µi ke,i s,i ρ + (1 − µi ) σ−ρ ρ s,i u,i σ−1 – Expressing in growth rates terms (γ): γSP µi (σ − ρ) ke,i s,i ρ (γke,i − γ s,i ) + (σ − 1)(γ s,i − γ u,i ) – First term: if σ > ρ, increases in equipment capital lead to increases in the skill premium in presence of equipment-skill complementarity.
  24. 24. Skill Premium in the Model – Using the FOCs, we can derive the expression for the skill premium: ws wu = λi (1 − µi ) 1 − λi µi ke,i s,i ρ + (1 − µi ) σ−ρ ρ s,i u,i σ−1 – Expressing in growth rates terms (γ): γSP µi (σ − ρ) ke,i s,i ρ (γke,i − γ s,i ) + (σ − 1)(γ s,i − γ u,i ) – First term: if σ > ρ, increases in equipment capital lead to increases in the skill premium in presence of equipment-skill complementarity. – Second term: relative growth rates of skilled and unskilled labor affect the skill premium.
  25. 25. Skill Premium in the Model – Using the FOCs, we can derive the expression for the skill premium: ws wu = λi (1 − µi ) 1 − λi µi ke,i s,i ρ + (1 − µi ) σ−ρ ρ s,i u,i σ−1 – Expressing in growth rates terms (γ): γSP µi (σ − ρ) ke,i s,i ρ (γke,i − γ s,i ) + (σ − 1)(γ s,i − γ u,i ) – First term: if σ > ρ, increases in equipment capital lead to increases in the skill premium in presence of equipment-skill complementarity. – Second term: relative growth rates of skilled and unskilled labor affect the skill premium. – Cross-sector reallocation of factors—due to trade shocks, for example—will also affect skill premium.
  26. 26. Calibration and Data – Our model is calibrated so that it matches Estonian, Latvian and Lithuanian data in 1995. – Most of the parameters can be directly calibrated from Social Accounting Matrices we construct for each country using the optimality and market-clearing conditions. SAM – Elasticity parameters taken from the literature. We later run sensitivity checks on those values. Parameter Value Corresponding Elasticity Implied Elasticity ρm,i 0.827 Import elasticity of substitution 5.78 ρx 0.9 Export elast. of subst. 10 ρ -0.5 Equipment-skilled labor elast. 0.67 σ 0.4 Equipment-unskilled labor elast. 1.67 η -1 Consumption goods elast. of subst. 0.5 ψ -0.25 Consumption-leisure elast. of subst. 0.8
  27. 27. Numerical Experiments: Description – Comparative statics experiments: – ToT experiment: replicates changes in ToT experienced by Baltic states between 1995 and 2008 by varying foreign prices. – Capital-deepening experiment: replicates increases in both types of capital stocks by increasing their endowments. – Skill supply experiment: increase endowments of skilled and unskilled labor to match those observed in the Baltics. – Joint experiment: simulates all three shocks simultaneously. – In each instance, we compare original and new equilibria and assess the effects of each shock on the skill premium.
  28. 28. Results - SP changes Estonia Latvia Lithuania Data -20.2 16.3 -13.1 Experiment ToT -2.2 -1.9 -1.3 Capital deepening Skill composition – ToT experiment predicts skill premium declines due to H-O type reallocation of resources towards unskilled-intensive sectors where Baltics enjoy comparative advantages. ToT – Capital deepening predicts skill premium increases due to equipment-skill complementarity. Note that the effect is the strongest in Latvia, which recorded the largest increase in equipment capital. – Skill composition predicts skill premium decreases due to skilled labor becoming more abundant and unskilled labor scarcer. – Joint experiment: skill premium decreases in Estonia and Lithuania, and increase in Latvia. – Overall, our model is able to replicate Baltic divergence. Qualitative results are in line with data. Moreover, quantitative predictions fairly accurate.
  29. 29. Results - SP changes Estonia Latvia Lithuania Data -20.2 16.3 -13.1 Experiment ToT Capital deepening 27.0 49.3 28.9 Skill composition – ToT experiment predicts skill premium declines due to H-O type reallocation of resources towards unskilled-intensive sectors where Baltics enjoy comparative advantages. – Capital deepening predicts skill premium increases due to equipment-skill complementarity. Note that the effect is the strongest in Latvia, which recorded the largest increase in equipment capital. capital – Skill composition predicts skill premium decreases due to skilled labor becoming more abundant and unskilled labor scarcer. – Joint experiment: skill premium decreases in Estonia and Lithuania, and increase in Latvia. – Overall, our model is able to replicate Baltic divergence. Qualitative results are in line with data. Moreover, quantitative predictions fairly accurate.
  30. 30. Results - SP changes Estonia Latvia Lithuania Data -20.2 16.3 -13.1 Experiment ToT Capital deepening Skill composition -41.5 -28.2 -53.6 – ToT experiment predicts skill premium declines due to H-O type reallocation of resources towards unskilled-intensive sectors where Baltics enjoy comparative advantages. – Capital deepening predicts skill premium increases due to equipment-skill complementarity. Note that the effect is the strongest in Latvia, which recorded the largest increase in equipment capital. – Skill composition predicts skill premium decreases due to skilled labor becoming more abundant and unskilled labor scarcer. skill – Joint experiment: skill premium decreases in Estonia and Lithuania, and increase in Latvia. – Overall, our model is able to replicate Baltic divergence. Qualitative results are in line with data. Moreover, quantitative predictions fairly accurate.
  31. 31. Results - SP changes Estonia Latvia Lithuania Data -20.2 16.3 -13.1 Joint experiment -22.4 9.8 -35.3 ToT -2.2 -1.9 -1.3 Capital deepening 27.0 49.3 28.9 Skill composition -41.5 -28.2 -53.6 – ToT experiment predicts skill premium declines due to H-O type reallocation of resources towards unskilled-intensive sectors where Baltics enjoy comparative advantages. – Capital deepening predicts skill premium increases due to equipment-skill complementarity. Note that the effect is the strongest in Latvia, which recorded the largest increase in equipment capital. – Skill composition predicts skill premium decreases due to skilled labor becoming more abundant and unskilled labor scarcer. – Joint experiment: skill premium decreases in Estonia and Lithuania, and increase in Latvia.
  32. 32. Results - SP changes Estonia Latvia Lithuania Data -20.2 16.3 -13.1 Joint experiment -22.4 9.8 -35.3 ToT -2.2 -1.9 -1.3 Capital deepening 27.0 49.3 28.9 Skill composition -41.5 -28.2 -53.6 – ToT experiment predicts skill premium declines due to H-O type reallocation of resources towards unskilled-intensive sectors where Baltics enjoy comparative advantages. – Capital deepening predicts skill premium increases due to equipment-skill complementarity. Note that the effect is the strongest in Latvia, which recorded the largest increase in equipment capital. – Skill composition predicts skill premium decreases due to skilled labor becoming more abundant and unskilled labor scarcer. – Joint experiment: skill premium decreases in Estonia and Lithuania, and increase in Latvia. – Overall, our model is able to replicate Baltic divergence. Qualitative results are in line with data. Moreover, quantitative predictions fairly accurate.
  33. 33. Sensitivity Analysis 1 – Model Performance in Shorter Horizons 1995-2000 2000-2008 SP changes Estonia Latvia Lithuania Estonia Latvia Lithuania Data 4.3 20.7 5.3 -24.4 -4.4 -18.4 Joint -18.0 31.3 3.5 1.4 -8.6 -34.4 ToT -1.0 -2.6 -0.7 -0.5 0.5 0.5 Capital deepening 12.0 22.7 14.4 26.8 39.5 23.9 Skill supply -27.1 10.1 -9.5 -21.2 -37.0 -49.9 – We want to see if our model can also account for the skill premium patterns within the time span as their evolution were non-monotonic (initial rise and subsequent fall). – Our model reproduces an inverse U-shaped evolution of the skill premium in Latvia and Lithuania, but not in Estonia. – In fact, the labor supply factors seem to have a lagged effect on the skill premium in Estonia, which our model cannot capture. – Country-specific institutional factors in the labor market – Toomet (2011)
  34. 34. Sensitivity Analysis 1 – Model Performance in Shorter Horizons 1995-2000 2000-2008 Experiment Estonia Latvia Lithuania Estonia Latvia Lithuania Data 4.3 20.7 5.3 -24.4 -4.4 -18.4 Joint -18.0 31.3 3.5 1.4 -8.6 -34.4 ToT -1.0 -2.6 -0.7 -0.5 0.5 0.5 Capital deepening 12.0 22.7 14.4 26.8 39.5 23.9 Skill supply -27.1 10.1 -9.5 -21.2 -37.0 -49.9 – We want to see if our model can also account for the skill premium patterns within the time span as their evolution were non-monotonic (initial rise and subsequent fall). – Our model reproduces an inverse U-shaped evolution of the skill premium in Latvia and Lithuania, but not in Estonia. – In fact, the labor supply factors seem to have a lagged effect on the skill premium in Estonia, which our model cannot capture. – Country-specific institutional factors in the labor market – Toomet (2011)
  35. 35. Sensitivity Analysis 2 – Changes in Technology Parameters Change in Skill Premium (Cobb-Douglas) (Benchmark) (Polgreen-Silos) Country Experiment ρ = 0 ρ = −0.5 ρ = −0.357 σ = 0 σ = 0.4 σ = 0.659 Estonia Joint -49.6 -22.4 -4.8 ToT -3.3 -2.2 -1.3 Capital deepening -0.8 27.0 33.2 Skill supply -48.7 -41.5 -31.1 Latvia Joint -34.5 9.8 37.2 ToT -2.4 -1.9 -1.4 Capital deepening -1.7 49.3 71.7 Skill supply -31.3 -28.2 -22.5 Lithuania Joint -59.4 -35.3 -15.4 ToT -2.1 -1.3 -0.7 Capital deepening -0.4 28.9 39.5 Skill supply -59.4 -53.6 -43.6 – With the Cobb-Douglas specification, we abstract from capital-skill complementarity and the skill premium only depends on the relative skill supply and trade channels. – Our qualitative results crucially hinge on the capital-skill complementarity channel.
  36. 36. Sensitivity Analysis 3 – Changes in Preference Parameters Change in Skill Premium (Log utility) (Benchmark) (Cobb-Douglas) (Inel. labor) Country Experiment η = 0 η = −1 η = −1 η = −1 ψ = −0.25 ψ = −0.25 ψ = 0 ψ = −1.5 ζj = 1 Estonia Joint -22.4 -22.4 -21.0 -26.3 -17.9 ToT -2.2 -2.2 -2.2 -2.1 -2.5 Capital Deepening 27.0 27.0 24.1 35.8 25.0 Skill Supply -41.5 -41.5 -38.5 -49.0 -36.4 Latvia Joint 10.0 9.8 9.0 13.1 22.4 ToT -1.8 -1.9 -1.7 -2.6 -2.3 Capital Deepening 49.3 49.3 44.2 67.5 59.2 Skill Supply -28.2 -28.2 -25.7 -35.1 -25.3 Lithuania Joint -35.4 -35.3 -32.7 -42.8 -24.2 ToT -1.5 -1.3 -1.4 -0.6 -1.9 Capital Deepening 28.9 28.9 25.3 40.8 32.8 Skill Supply -53.6 -53.6 -49.5 -63.4 -47.4 – The lower the elasticity between consumption and leisure, the larger the changes in the skill premium. – Labor-leisure margin changes the relative magnitude of the skill premium changes. Our qualititavie results are still maintained under an inelastic labor supply assumption.
  37. 37. Conclusions – We construct a static general equilibrium model to account for the evolution of the skill premium. – Our model incorporates forces that have a biased effect on the demand for skilled and unskilled labor: international trade and capital-skill complementarity. – Unlike the large majority of recent articles, our model also incorporate factors that affect the supply of labor. – To assess the quantitative validity of the model’s predictions, we apply it to account for the divergent patterns of the skill premium in the Baltic states between 1995 and 2008.
  38. 38. Conclusions – Incorporating all forces together, we can qualitatively account for the Baltic skill premium divergence with a fair degree of quantitative success. – For shorter horizons, our model is able to replicate the initial rise and subsequent fall of the skill premium in Latvia and Lithuania, although not for Estonia. – Our results are robust to the choices of trade and preference elasticities, while the sensitivity analyses highlight the capital-skill complementarity channel.
  39. 39. SAM Estonia 1995  (Unit: Euro, Millions) (Unskilled) (Skilled) Goods 1544.9 948.5 0.0 0.0 0.0 1109.8 734.2 375.6 443.3 24.9 1199.0 5270.3 Service 981.8 4495.3 0.0 0.0 0.0 541.1 342.4 198.7 336.7 676.3 466.4 7497.5 Labor 483.0 1100.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1583.6 (Unskilled) 351.9 473.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 825.0 (Skilled) 131.1 627.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 758.6 Capital equipment 50.0 147.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 197.1 (Unskilled) 41.4 121.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 163.1 (Skilled) 8.6 25.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 34.0 Capital structure 166.4 489.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 656.2 (Unskilled) 137.7 405.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 543.0 (Skilled) 28.7 84.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 113.2 Households 0.0 0.0 1583.6 197.1 656.2 0.0 0.0 0.0 0.0 0.0 0.0 2437.0 Government 291.6 37.8 0.0 0.0 0.0 402.1 252.6 149.5 0.0 0.0 0.0 731.4 Direct Tax 0.0 0.0 0.0 0.0 0.0 402.1 252.6 149.5 0.0 0.0 0.0 402.1 Indirect Tax 291.2 37.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 329.0 Tariff 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 Leisure 0.0 0.0 0.0 0.0 0.0 2623.1 1423.1 1200.0 0.0 0.0 0.0 2623.1 Capital (Saving) 0.0 0.0 0.0 0.0 0.0 384.0 201.9 182.1 0.0 30.3 365.7 780.0 Import  1752.7 278.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2031.1 Total 5270.3 7497.5 1583.6 197.1 656.2 5060.1 2954.2 2105.8 780.0 731.4 2031.1 0.0 C I G X TotalCK  (structure) Goods Service L K  (equipment) back
  40. 40. ToT Experiment back Variable Type/Sector Estonia Latvia Lithuania Skill premium -2.2 -1.9 -1.3 Wage Skilled labor 10.8 -4.0 20.2 Unskilled labor 13.3 -2.1 21.7 Imports Goods 88.5 -39.1 274.3 Services 333.8 56.4 621.6 Exports Goods 116.6 -33.5 319.9 Services 63.5 0.7 16.5 Final output Goods 34.9 -6.4 89.4 Services 6.0 -1.1 4.6 Domestic output Goods 9.4 7.4 5.0 Services -4.8 -2.8 -3.8 Unskilled labor demand Goods 7.6 6.7 3.0 Services -7.3 -4.1 -6.1 Skilled labor demand Goods 11.5 9.2 5.7 Services -3.9 -1.5 -3.8 – ToT shocks lead to increases in trade in Estonia and Lithuania in both sectors. As a result, final output rises and wages go up. Sectoral reallocation occurs from skilled (service) sector towards unskilled (goods) sector. – In Latvia, overall trade falls, leading to lower final output and lower wages. In the goods sector, domestic production increases to substitute higher priced imports.
  41. 41. Capital Deepening Experiment Variable Type/Sector Estonia Latvia Lithuania Skill premium 27.0 49.3 28.9 Wage Skilled labor 82.9 175.8 114.2 Unskilled labor 44.0 84.8 66.2 Rental price Equipment capital -85.7 -88.1 -88.3 Final output Goods 42.3 83.2 60.7 Services 51.5 97.3 68.7 Domestic output Goods 49.9 96.9 74.7 Services 52.8 99.1 69.3 Equipment capital demand Goods 494.9 1064.5 799.4 Services 409.3 671.7 544.9 Unskilled labor demand Goods -3.1 -10.5 -10.8 Services -1.7 -6.4 -4.0 Skilled labor demand Goods 8.9 42.9 29.3 Services -6.7 -5.3 -7.3 – Increases in the stock of capital drive down the rental prices of capital which in turn raise the demand for capital in all sectors. – With larger stocks of capital, both domestic and final output increase in all sectors. Wages increase too. – Equipment capital deepening affects the demands of the two types of labor differently, favoring skilled over unskilled labor. back
  42. 42. Skill Composition Experiment Variable Type/Sector Estonia Latvia Lithuania Skill premium -41.5 -28.2 -53.6 Wage Skilled labor -30.1 -23.2 -45.1 Unskilled labor 19.5 7.1 18.3 Final output Goods 2.9 0.1 4.1 Services 9.0 4.3 11.7 Domestic output Goods 1.4 -1.1 2.6 Services 9.6 4.6 12.1 All sectors 6.9 2.9 8.9 Hours worked Unskilled labor -21.8 -4.7 -8.5 Skilled labor 58.7 29.4 84.4 Total labor 16.7 4.4 30.6 Unskilled labor demand Goods -16.8 -6.8 -12.6 Services -25.5 -11.2 -27.3 Skilled labor demand Goods 64.8 30.5 100.9 Services 57.4 34.1 105.5 – Despite mixed effects in the hours worked by different skill types, total hours worked rise for all countries. This leads to higher aggregate output – final and domestic. – As unskilled labor become scarcer, its wage rises. Vice versa for skilled labor. – Changes in the relative wages translate into changes in the relative demand for skilled and unskilled workers. back

×