## 1. Cross-Country Comparison of Productivity and R&D

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### Background: Role of the Automotive Sector in Czechia
- Czechia’s rapid convergence toward advanced European economies has been driven mostly by the auto industry; sustaining convergence requires moving up global value chains.
- Czechia lies below many advanced economies though above most emerging economies in terms of GDP-per-capita (reported in 2019).
- Czechia has relatively lower R&D spending per inhabitant compared with other advanced European economies.

### Size and Links of the Automotive Sector
- The automotive sector is one of the largest sectors in Czechia by output and employment.
- Motor vehicle sector’s value added and employment shares stood at 4.9 and 3.2 percent as of 2014.
- Sectors adjacent to motor vehicles (e.g., trade of motor vehicles and repair of equipment) generate positive spillovers.
- The auto industry is highly integrated into regional global value chains; Germany is the most important supplier and consumer of Czechia auto sector intermediate inputs. Major supplier countries after Czechia and Germany include Poland, Slovakia, and Italy; important intermediate-input consumers include Russia, France, and Slovakia.
- The relative use of intermediate inputs for Czechia in Figure 3 is around 50 percent (model calibration target).

### Value Added and Skill Intensity
- Czechia automotive sector has one of the lowest value-added shares compared to other countries in the region.
- The low value-added share reflects Czechia’s position in the global value chain—producing lower value-added components of vehicles.
- Czechia has one of the lowest shares of high-skilled workers in the automotive sector and a comparatively lower high-skill population share.
- By comparison, Germany has one of the highest value-added shares in the auto sector and a larger share of high-skilled workers available from the general population.

### Challenges in the Transition to Electric Vehicle (EV) Production
- Three observed trends creating risks for Czechia:
  - EV production involves shorter value chains than combustion-vehicle production; it is unclear whether Czechia’s current role will be preserved.
  - EV production tends to be higher value added and involves less labor; the shift to EVs risks lower labor demand and negative effects on employment.
  - EV production is more skill-intensive than combustion-vehicle production; Czechia’s relatively lower-skilled population may face a more challenging transition absent appropriate policies.

### Structural Model: Automotive Global Value Chain (overview)
- A stylized structural model of automotive global value chains is used to analyze policy impacts; it includes separate production of electric and combustion vehicles and embeds domestic capability features and specialization along the global value chain.
- Model economies: three countries i ∈ I = {CZE, DEU, ROW} representing Czechia, Germany, and the rest of the world.
- Key model components:
  - Value chains: two value chains (EV and CV) plus a numeraire good; production organized in stages (sectors h) using skilled and unskilled labor. Relative labor intensity denoted by α_h and relative skill intensity by β_h.
  - Trade: sector goods face variable trade cost τ_i,j^h and fixed trade cost f_i,j^h; fixed trade cost implies only most productive goods are exported in equilibrium.
  - Entry of new goods: firms pay a convex cost to create new varieties; the mass of goods captures sectoral capabilities and is used interchangeably with total factor productivity.
  - Capital is not explicitly modeled but its role is proxied by entry of new goods and intermediate inputs.

### Calibration and Key Parameters
- Calibration is based on recent automotive production data for combustion vehicles and parameterizes EV production to match observed differences.
- Transition to EV is modeled by increasing the taste parameter γ_t for electric vehicle consumption.
- Common Parameters (reported exactly as in source):
  - Discount Rate (%)4
  - EV Preference (%)10
  - Productivity Distribution5.0
  - Exit Probability0.1
- Country-Specific Parameters (productivity of unskilled and skilled workers reported as country vectors):
  - Productivity of Unskilled Workers[1.00 , 0.91 , 1.25]
  - Productivity of Skilled Workers[0.67 , 0.76 , 0.63]
- Sector Specific Parameters (reported as ordered vectors corresponding to model stages):
  - Elasticity of Substitution[3.0 , 3.0 , 3.0 , 2.5 , 3.0 , 2.5]
  - Labor Share[0.7 , 0.5 , 0.5 , 0.5 , 0.8 , 0.6]
  - Skill Intensity[0.2 , 0.2 , 0.2 , 0.4 , 0.3 , 0.5]
- Other Parameters (reported as country average):
  - Entry Cost[1.07 , 1.02 , 1.03]
  - Variable Trade Cost[1.07 , 1.06 , 1.06]
  - Fixed Trade Cost[0.107 , 0.106 , 0.106]

### Transition Scenario and Quantitative Illustration
- The model compares a baseline equilibrium with an EV equilibrium in which expenditures on electric vehicles increase to 90 percent of automotive expenditures (γ set to 90%).
- The EV equilibrium is interpreted as capturing the automotive sector around 20 years in the future (e.g., post-2035 EU combustive-engine ban scenario).
- Results are presented as steady-state comparisons (interpretable as the 2035 economy relative to current state); results are illustrative and scaled such that the absolute value of the change in Czechia is normalized to one.

### Model Results: Impacts of Transition to EV Production
- For Czechia, moving to the EV equilibrium produces:
  - A comparatively marginal increase in automotive sector output.
  - An increase in the share of high-skilled workers (skill share).
  - A reduction in the automotive sector’s value-added share relative to the baseline.
  - A decline in overall employment in the automotive sector relative to the baseline.
- Mechanisms:
  - The increase in output is driven by the narrowing of the value chain, allowing Czechia to displace ROW competitors in higher value-added stages where Czechia has a comparative advantage.
  - The decline in value-added share and employment follows from EV production’s lower labor intensity and parameterized higher skill intensity relative to combustion vehicles.

### Policy Experiments Simulated
- Three broad policy schemes are modeled as stand‑ins for groups of more specialized policies:
  - Boosting labor productivity:
    - Implemented by increasing Czechia labor productivity to match that of Germany.
    - Captures policies such as investing in education, upskilling, re‑skilling, lifelong learning, and digitalization.
  - Support for horizontal production capabilities:
    - Implemented by reducing the entry cost of Czechia for intermediate sectors in both the electric and combustion vehicle (CV stage 3 and EV stage 2) production by 20 percent.
    - Captures reinforcing current production capabilities (infrastructure investment, subsidies to current firms, lowering trade costs).
  - Support for vertical production capabilities:
    - Implemented by reducing the entry cost of the most downstream sectors for both electric and combustion vehicle production by around 20 percent.
    - Captures policies to move up the global value chain (R&D subsidies, lowering cost of new firm/product entry, labor market policies to allow access to foreign experts).
- Model experiment specifics and normalization:
  - The taste parameter for electric vehicles γ is set to 90 percent in policy experiment calculations.
  - Values are normalized such that the absolute value of the change in the transition (Figure 8) has value one.

### Main Results and Comparative Impacts of Policy Schemes
- General comparison:
  - Boosting labor productivity or supporting higher‑value (vertical) segments yield comparatively higher returns than horizontal policies.
  - Impacts are measured as changes in equilibrium outcomes under the policy scheme relative to the electric vehicle equilibrium.
- Boosting labor productivity:
  - Leads to increases in output, employment, the skill intensity of the economy, and the value‑added share.
  - Higher labor productivity enables movement toward higher value‑added sectors and rebalances intermediate EV and final EV sector production across Czechia and Germany (some intermediate EV production shifts to Germany while some final EV production shifts to Czechia).
- Horizontal production capability support (entry cost −20 percent for intermediate sectors):
  - Can have paradoxically negative effects on Czechia automotive sector across four dimensions.
  - Mechanisms:
    - Domestic competitiveness effect: cheaper Czechia intermediate inputs make final goods cheaper and more profitable domestically and abroad, initially benefiting Czechia automotive sector.
    - Global competition effects: increased profitability of final good production incentivizes investments in final good capabilities both domestically and abroad, raising global competition and, over time, negatively impacting Czechia final good producers.
  - Net effect: negative impact can dominate because horizontal policies reallocate activity from relatively favorable stages (final good) to relatively unfavorable stages (intermediate production).
- Vertical production capability support (entry cost −~20 percent for downstream/final sectors):
  - Yields increases in value‑added, employment, and share of skilled labor.
  - Lower cost of entry in the final EV sector allows more goods to be produced in Czechia, raising economic activity because the final EV sector is higher value‑added, more labor‑intensive, and more skill‑intensive.
  - Vertical policies and boosting labor productivity have relatively similar effects at the aggregate level: policies can either direct activity toward specific sectors or make conditions around those sectors more favorable to achieve similar goals.

### Policy Recommendations and Considerations
- Design priorities:
  - Clear and directed policy can help minimize headwinds in the transition to electric vehicle production.
  - Scale up investments in skills required to boost labor productivity to prepare the economy for new EV‑related production processes.
  - Invest in both horizontal and vertical capabilities to allow a more active role in the EV production chain, but avoid policies that cause specialization to shift toward lower value‑added and lower employment stages of production.
  - Large investments are required; policies should be well planned, sequenced, and targeted to maximize government capacity and labor resources.
- Further considerations outside model scope:
  - The model assumes final demand shifts uniformly across countries to reflect the transition to electric vehicles and the EU’s “Fit for 55” goal that all vehicles sold should be zero emissions by 2035; in reality, some countries (including Czechia) may choose not to fully phase out combustion vehicles, which could soften transition impacts or disincentivize EV investment.
  - The model does not directly consider the role of infrastructure or other forms of capital investment (e.g., charging stations, EV production factories). Subsidizing development of new EV production facilities would be qualitatively similar to policies that lower the cost of entry for new goods and the broad lessons should apply.

### Model Structure, Calibration, and Key Numerical Parameters (additional details)
- Countries and mapping:
  - Global economy modeled with countries i ∈ ℐ={1,2,3} corresponding to Czechia (CZE), Germany (DEU), and Rest of World (ROW).
- Value chains and sectors:
  - Combustion value chain ℋ_CV = {0,1,2,3,4} with final combustion good at sector 4.
  - Electric value chain ℋ_EV = {0,1,5,6} with final electric good at sector 6.
- Production and labor parameters:
  - Final automotive demand: Y_t = (Y_t^CV)^(1−γ_t) (Y_t^EV)^(γ_t), with γ_t the EV taste parameter.
  - Labor composite uses skill intensity β_h per sector.
  - Elasticity of substitution set to σ_h = 3 (with slightly higher σ in final stages).
  - Electric vehicle production assumed to have a lower labor share α_h and a higher skill intensity β_h at each stage versus combustion vehicle production.
- Trade and entry:
  - Trade costs estimated with the Head‑Reis Index; model distinguishes variable τ_{i,j}^h and fixed f_{i,j}^h components.
  - Entry costs ψ_i^h determine mass of goods; entry cost in the common sector normalized to one for Czechia.
  - Horizontal policy experiment: reduce entry cost for intermediate sectors by 20 percent.
  - Vertical policy experiment: reduce entry cost for most downstream sectors by around 20 percent.
- Data and calibration:
  - Data sources: World Input‑Output Database for 2014 and Socio‑Economic Accounts for 2011.
  - Calibration targets include Czechia’s relative use of intermediate inputs around 50 percent and sectoral characteristics from the input‑output structure.
  - Country‑specific productivity: productivity of unskilled labor in Czechia normalized to unity; Germany assumed to have relatively expensive unskilled labor but relatively cheap skilled labor; Rest of World has cheapest unskilled labor but most expensive skilled labor.
- Equilibrium insights:
  - A country’s sectoral capabilities are determined by investments to produce new varieties, influenced by trade costs (τ, f), productivity (A_U, A_S), relative cost of skilled labor, and entry costs ψ.
  - Sector capabilities determine trade patterns; goods trade internationally if productivity offsets trade costs and fixed entry costs.

*Source: CZECH REPUBLIC — CZECHIA: STRUCTURAL TRANSITIONS TO ELECTRIC VEHICLE PRODUCTION (December 12, 2022), International Monetary Fund*

### 1. Cross-Country Comparison of Productivity and R&D  ________________________________ 3

### 1. Cross-Country Comparison of Productivity and R&D

### Background: Role of the Automotive Sector in Czechia
- Czechia’s rapid convergence toward advanced European economies has been driven mostly by the auto industry; sustaining convergence requires moving up global value chains.
- Czechia lies below many advanced economies though above most emerging economies in terms of GDP-per-capita (reported in 2019).
- Czechia has relatively lower R&D spending per inhabitant compared with other advanced European economies.

### Size and Links of the Automotive Sector
- The automotive sector is one of the largest sectors in Czechia by output and employment.
- Motor vehicle sector’s value added and employment shares stood at 4.9 and 3.2 percent as of 2014.
- Sectors adjacent to motor vehicles (e.g., trade of motor vehicles and repair of equipment) generate positive spillovers.
- The auto industry is highly integrated into regional global value chains; Germany is the most important supplier and consumer of Czechia auto sector intermediate inputs. Major supplier countries after Czechia and Germany include Poland, Slovakia, and Italy; important intermediate-input consumers include Russia, France, and Slovakia.
- The relative use of intermediate inputs for Czechia in Figure 3 is around 50 percent (model calibration target).

### Value Added and Skill Intensity
- Czechia automotive sector has one of the lowest value-added shares compared to other countries in the region.
- The low value-added share reflects Czechia’s position in the global value chain—producing lower value-added components of vehicles.
- Czechia has one of the lowest shares of high-skilled workers in the automotive sector and a comparatively lower high-skill population share.
- By comparison, Germany has one of the highest value-added shares in the auto sector and a larger share of high-skilled workers available from the general population.

### Challenges in the Transition to Electric Vehicle (EV) Production
- Three observed trends creating risks for Czechia:
  - EV production involves shorter value chains than combustion-vehicle production; it is unclear whether Czechia’s current role will be preserved.
  - EV production tends to be higher value added and involves less labor; the shift to EVs risks lower labor demand and negative effects on employment.
  - EV production is more skill-intensive than combustion-vehicle production; Czechia’s relatively lower-skilled population may face a more challenging transition absent appropriate policies.

### Structural Model: Automotive Global Value Chain (overview)
- A stylized structural model of automotive global value chains is used to analyze policy impacts; it includes separate production of electric and combustion vehicles and embeds domestic capability features and specialization along the global value chain.
- Model economies: three countries i ∈ I = {CZE, DEU, ROW} representing Czechia, Germany, and the rest of the world.
- Key model components:
  - Value chains: two value chains (EV and CV) plus a numeraire good; production organized in stages (sectors h) using skilled and unskilled labor. Relative labor intensity denoted by α_h and relative skill intensity by β_h.
  - Trade: sector goods face variable trade cost τ_i,j^h and fixed trade cost f_i,j^h; fixed trade cost implies only most productive goods are exported in equilibrium.
  - Entry of new goods: firms pay a convex cost to create new varieties; the mass of goods captures sectoral capabilities and is used interchangeably with total factor productivity.
  - Capital is not explicitly modeled but its role is proxied by entry of new goods and intermediate inputs.

### Calibration and Key Parameters (as presented)
- Calibration is based on recent automotive production data for combustion vehicles and parameterizes EV production to match observed differences.
- Transition to EV is modeled by increasing the taste parameter γ_t for electric vehicle consumption.
- Common Parameters (reported exactly as in source):
  - Discount Rate (%)4
  - EV Preference (%)10
  - Productivity Distribution5.0
  - Exit Probability0.1
- Country-Specific Parameters (productivity of unskilled and skilled workers reported as country vectors):
  - Productivity of Unskilled Workers[1.00 , 0.91 , 1.25]
  - Productivity of Skilled Workers[0.67 , 0.76 , 0.63]
- Sector Specific Parameters (reported as ordered vectors corresponding to model stages):
  - Elasticity of Substitution[3.0 , 3.0 , 3.0 , 2.5 , 3.0 , 2.5]
  - Labor Share[0.7 , 0.5 , 0.5 , 0.5 , 0.8 , 0.6]
  - Skill Intensity[0.2 , 0.2 , 0.2 , 0.4 , 0.3 , 0.5]
- Other Parameters (reported as country average):
  - Entry Cost[1.07 , 1.02 , 1.03]
  - Variable Trade Cost[1.07 , 1.06 , 1.06]
  - Fixed Trade Cost[0.107 , 0.106 , 0.106]

### Transition Scenario and Quantitative Illustration
- The model compares a baseline equilibrium with an EV equilibrium in which expenditures on electric vehicles increase to 90 percent of automotive expenditures (γ set to 90%).
- The EV equilibrium is interpreted as capturing the automotive sector around 20 years in the future (e.g., post-2035 EU combustive-engine ban scenario).
- Results are presented as steady-state comparisons (interpretable as the 2035 economy relative to current state); results are illustrative and scaled such that the absolute value of the change in Czechia is normalized to one.

### Model Results: Impacts of Transition to EV Production
- For Czechia, moving to the EV equilibrium produces:
  - A comparatively marginal increase in automotive sector output.
  - An increase in the share of high-skilled workers (skill share).
  - A reduction in the automotive sector’s value-added share relative to the baseline.
  - A decline in overall employment in the automotive sector relative to the baseline.
- Mechanisms:
  - The increase in output is driven by the narrowing of the value chain, allowing Czechia to displace ROW competitors in higher value-added stages where Czechia has a comparative advantage.
  - The decline in value-added share and employment follows from EV production’s lower labor intensity and parameterized higher skill intensity relative to combustion vehicles.

### Policy Analysis Overview (as foreshadowed)
- The model accommodates several policy channels to support the automotive sector during the EV transition, including:
  - Increasing labor productivity.
  - Boosting production capabilities within current specialties.
  - Moving up the global value chain toward higher value-added activities.
- The following sections of the original analysis explore the potential implications of these broad policy categories to offset negative transition effects.

*Source: CZECH REPUBLIC — CZECHIA: STRUCTURAL TRANSITIONS TO ELECTRIC VEHICLE PRODUCTION (December 12, 2022), International Monetary Fund*

### 11.      The model is used to simulate three broad policy schemes that could potentially aid the

### 1czeea2023002 - 11.      The model is used to simulate three broad policy schemes that could potentially aid the

### Policy experiments simulated
- Three broad policy schemes are modeled as stand‑ins for groups of more specialized policies:
  - Boosting labor productivity:
    - Implemented by increasing Czechia labor productivity to match that of Germany.
    - Captures policies such as investing in education, upskilling, re‑skilling, lifelong learning, and digitalization.
  - Support for horizontal production capabilities:
    - Implemented by reducing the entry cost of Czechia for intermediate sectors in both the electric and combustion vehicle (CV stage 3 and EV stage 2) production by 20 percent.
    - Captures reinforcing current production capabilities (infrastructure investment, subsidies to current firms, lowering trade costs).
  - Support for vertical production capabilities:
    - Implemented by reducing the entry cost of the most downstream sectors for both electric and combustion vehicle production by around 20 percent.
    - Captures policies to move up the global value chain (R&D subsidies, lowering cost of new firm/product entry, labor market policies to allow access to foreign experts).

- Model experiment specifics and normalization:
  - The taste parameter for electric vehicles γ is set to 90 percent in policy experiment calculations.
  - Values are normalized such that the absolute value of the change in the transition (Figure 8) has value one.

### Main results and comparative impacts
- General comparison:
  - Boosting labor productivity or supporting higher‑value (vertical) segments yield comparatively higher returns than horizontal policies.
  - Impacts are measured as changes in equilibrium outcomes under the policy scheme relative to the electric vehicle equilibrium.

- Boosting labor productivity:
  - Leads to increases in output, employment, the skill intensity of the economy, and the value‑added share.
  - Higher labor productivity enables movement toward higher value‑added sectors and rebalances intermediate EV and final EV sector production across Czechia and Germany (some intermediate EV production shifts to Germany while some final EV production shifts to Czechia).

- Horizontal production capability support (entry cost −20 percent for intermediate sectors):
  - Can have paradoxically negative effects on Czechia automotive sector across four dimensions.
  - Mechanisms:
    - Domestic competitiveness effect: cheaper Czechia intermediate inputs make final goods cheaper and more profitable domestically and abroad, initially benefiting Czechia automotive sector.
    - Global competition effects: increased profitability of final good production incentivizes investments in final good capabilities both domestically and abroad, raising global competition and, over time, negatively impacting Czechia final good producers.
  - Net effect: negative impact can dominate because horizontal policies reallocate activity from relatively favorable stages (final good) to relatively unfavorable stages (intermediate production).

- Vertical production capability support (entry cost −~20 percent for downstream/final sectors):
  - Yields increases in value‑added, employment, and share of skilled labor.
  - Lower cost of entry in the final EV sector allows more goods to be produced in Czechia, raising economic activity because the final EV sector is higher value‑added, more labor‑intensive, and more skill‑intensive.
  - Vertical policies and boosting labor productivity have relatively similar effects at the aggregate level: policies can either direct activity toward specific sectors or make conditions around those sectors more favorable to achieve similar goals.

### Policy recommendations and considerations
- Design priorities:
  - Clear and directed policy can help minimize headwinds in the transition to electric vehicle production.
  - Scale up investments in skills required to boost labor productivity to prepare the economy for new EV‑related production processes.
  - Invest in both horizontal and vertical capabilities to allow a more active role in the EV production chain, but avoid policies that cause specialization to shift toward lower value‑added and lower employment stages of production.
  - Large investments are required; policies should be well planned, sequenced, and targeted to maximize government capacity and labor resources.

- Further considerations outside model scope:
  - The model assumes final demand shifts uniformly across countries to reflect the transition to electric vehicles and the EU’s “Fit for 55” goal that all vehicles sold should be zero emissions by 2035; in reality, some countries (including Czechia) may choose not to fully phase out combustion vehicles, which could soften transition impacts or disincentivize EV investment.
  - The model does not directly consider the role of infrastructure or other forms of capital investment (e.g., charging stations, EV production factories). Subsidizing development of new EV production facilities would be qualitatively similar to policies that lower the cost of entry for new goods and the broad lessons should apply.

### Model structure, calibration, and key numerical parameters
- Countries and mapping:
  - Global economy modeled with countries i ∈ ℐ={1,2,3} corresponding to Czechia (CZE), Germany (DEU), and Rest of World (ROW).
- Value chains and sectors:
  - Combustion value chain ℋ_CV = {0,1,2,3,4} with final combustion good at sector 4.
  - Electric value chain ℋ_EV = {0,1,5,6} with final electric good at sector 6.
- Production and labor parameters:
  - Final automotive demand: Y_t = (Y_t^CV)^(1−γ_t) (Y_t^EV)^(γ_t), with γ_t the EV taste parameter.
  - Labor composite uses skill intensity β_h per sector.
  - Elasticity of substitution set to σ_h = 3 (with slightly higher σ in final stages).
  - Electric vehicle production assumed to have a lower labor share α_h and a higher skill intensity β_h at each stage versus combustion vehicle production.
- Trade and entry:
  - Trade costs estimated with the Head‑Reis Index; model distinguishes variable τ_{i,j}^h and fixed f_{i,j}^h components.
  - Entry costs ψ_i^h determine mass of goods; entry cost in the common sector normalized to one for Czechia.
  - Horizontal policy experiment: reduce entry cost for intermediate sectors by 20 percent.
  - Vertical policy experiment: reduce entry cost for most downstream sectors by around 20 percent.
- Data and calibration:
  - Data sources: World Input‑Output Database for 2014 and Socio‑Economic Accounts for 2011.
  - Calibration targets include Czechia’s relative use of intermediate inputs around 50 percent and sectoral characteristics from the input‑output structure.
  - Country‑specific productivity: productivity of unskilled labor in Czechia normalized to unity; Germany assumed to have relatively expensive unskilled labor but relatively cheap skilled labor; Rest of World has cheapest unskilled labor but most expensive skilled labor.
- Equilibrium insights:
  - A country’s sectoral capabilities are determined by investments to produce new varieties, influenced by trade costs (τ, f), productivity (A_U, A_S), relative cost of skilled labor, and entry costs ψ.
  - Sector capabilities determine trade patterns; goods trade internationally if productivity offsets trade costs and fixed entry costs.

*Source: 1czeea2023002 - 11.      The model is used to simulate three broad policy schemes that could potentially aid the*

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_Source: https://www.imf.org/-/media/files/publications/cr/2023/english/1czeea2023002.pdf_