## 1. Exports and Contribution to Export Share Increase ## Source details **Canonical URL:** [1. Exports and Contribution to Export Share Increase](https://www.imf.org/-/media/files/publications/cr/2025/english/1czeea2025002-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2025/english/1czeea2025002-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2025/english/1czeea2025002-print-pdf.pdf.json) --- ### Overview - The Czech economy has underperformed European peers in the post-pandemic period and economic convergence has come to a halt. - Common attributions (close ties to Germany and an energy-intensive economic structure) only partly explain underperformance; IMF analysis finds declining productivity and structural weaknesses play a major role. ### Exports and Economic Structure - Exports have performed broadly in line with exports in the rest of the EU since the pandemic. - Constant share decomposition: Czech exporters offset weak German demand by expanding in other fast-growing markets, including Poland. - Counterfactual benchmarking (Caceres et al. (2021) methodology): - Only one third of the output loss relative to a pre-pandemic trend can be attributed to an unfavorable economic structure (concentration in sectors affected by supply-side disruptions and energy- or interest-rate-sensitive sectors). - The remaining two thirds is explained by underperformance within sectors. ### The Productivity Slowdown: Three Key Stylized Facts - System-wide productivity growth has been on a declining trend: - After the GFC productivity growth remained higher than that of Germany and other EU countries but the gap reduced considerably. - Since end-2019, labor productivity has fallen in the Czech Republic while it advanced, albeit modestly, in the EU. - Both slower total factor productivity (TFP) growth and capital deepening contributed to the decline (decomposition with EU KLEMS). - TFP decelerated significantly and did not recover to pre-GFC growth rates. - Sector-specific developments: - Construction: - Significant labor productivity underperformance vs. most EU countries and CEE peers. - Vacancies peaked at 16 percent in 2019 and declined only gradually afterwards. - Sector has shrunk by nearly 25 percent over the last ten years. - Mining and energy: sharper correction due to falling demand, phasing out of coal production, and limited investment in renewables. - Non-auto manufacturing: stagnant for almost a decade. - Automotive industry (especially car parts): generally resilient after initial supply-chain disruptions; competitiveness gains more than demand-driven, though inventory movements may distort measured progress. - Distance to EU TFP frontier shows sectoral variation: - TFP gap for energy-related sectors, particularly electricity, increased in 2019 compared to 2011. - Mining shows a consistently larger TFP gap from the EU frontier. - ICT and financial and insurance services saw a decrease in the TFP gap as TFP grew broadly in line with EU frontier countries. ### Labor Reallocation and Resource Misallocation - Inefficient labor reallocation contributed to the productivity slowdown: - Shift-share analysis (2019Q4-2024Q1) indicates allocative inefficiency explains the post-pandemic productivity decline. - High-productivity sectors (manufacturing, financial services) have reduced workforce; low-productivity public administration expanded considerably. - ICT sector employment increased but not enough to meet labor demand; persistent labor shortages and skills mismatches remain, with a high share of tertiary graduates working in occupations not requiring their degree. - Overall, labor is increasingly concentrated in low-productivity sectors, in contrast to the EU where reallocation has positively affected productivity. - Financial flows misallocation: - Strong growth in bank credit to low-productivity sectors, including public administration. - Declining credit to high-productivity sectors such as manufacturing and ICT. - Decline in FDI inflows into manufacturing over the last decade; FDI shifted toward financial and insurance services. - Capital markets are underdeveloped and venture capital investment is low relative to other OECD countries. ### Econometric Analysis of Industry-Level TFP Growth (1995–2020) - Baseline specification includes: - TFP growth frontier ∆yL (highest TFP growth in industry j at time t). - Technological gap (yi,j,t-1 − yL i,j,t-1). - Industry- and country-level controls: ICT capital spending, non-ICT capital spending, R&D spending, consumer price inflation, domestic credit to the private sector. - Key empirical findings: - TFP growth at the frontier is positive and statistically significant across all tradable industries but insignificant in the non-tradable sector. - The technological gap is a significant determinant of TFP growth: adopting existing technologies is a powerful engine for convergence. - R&D spending as a share of gross fixed investment has a positive effect on TFP growth across sectors, but is statistically significant only for the non-tradable sector; the magnitude is greater for non-tradables. - ICT capital spending as a share of gross fixed investment is positively associated with higher TFP growth, but not statistically significant. - Non-ICT capital spending (proxied by transportation equipment) as a share of gross fixed investment does not appear to matter for TFP growth across specifications. - Inflation has a negative but significant effect on TFP growth across all industries and in the tradable sector; it is insignificant in the non-tradable sector. - Financial development (domestic credit to the private sector) has a positive but insignificant effect on TFP growth across specifications. ### Sectoral productivity convergence: empirical findings - Main finding: Sectoral productivity is likely to converge toward the frontier if bottlenecks in underperforming sectors are effectively addressed. - Convergence mechanisms: upgrading of processes and products, adoption of existing technologies, leveraging of knowledge spillovers. - Policy imperative: Expediting processes, especially at the local administration level, is critical to address bottlenecks in the construction sector and scale up renewable investments in the energy sector. - Key regression results (dependent variable: TFP growth; columns: All, Tradables, Non-tradables): - TFP growth at frontier: - All: 0.133** (t = 2.697) - Tradables: 0.203*** (t = 4.188) - Non-tradables: -0.104 (t = -0.972) - Technological gap: - All: -13.40*** (t = -3.148) - Tradables: -15.62** (t = -2.664) - Non-tradables: -10.15** (t = -2.525) - ICT capital: - All: 1.944 (t = 0.288) - Tradables: 5.508 (t = 0.827) - Non-tradables: 3.105 (t = 0.248) - Non-ICT capital: - All: 9.875 (t = 1.274) - Tradables: 7.931 (t = 0.670) - Non-tradables: 12.75 (t = 1.621) - R&D spending: - All: 19.84 (t = 1.594) - Tradables: 14.505 (t = 0.933) - Non-tradables: 1.11* (t = 1.931) - Inflation: - All: -0.746** (t = -2.630) - Tradables: -0.826* (t = -1.845) - Non-tradables: -0.521 (t = -1.315) - Financial development: - All: 0.131 (t = 1.639) - Tradables: 0.0763 (t = 0.731) - Non-tradables: 0.137 (t = 1.494) - Number of observations: - All: 628 - Tradables: 353 - Non-tradables: 275 - Adjusted R2: - All: 0.144 - Tradables: 0.179 - Non-tradables: 0.239 - Statistical notes: Robust t-statistics in parentheses; a constant is included but not shown. Significance levels: *** p<0.01, ** p<0.05, * p<0.1. ### Structural transformation, manufacturing, and services - Manufacturing: - Manufacturing still represents 21 percent of the Czech economy. - Manufacturing share of GDP has declined since 2017; employment share decline is typically even more pronounced. - International evidence: a hump-shaped relationship between industrialization and income convergence. - Services: - A higher share of services does not necessarily correlate with lower productivity growth; effects depend on which subsectors expand. - Global context: growing global services exports, exceptional growth in digitally delivered services, increased tradability and international outsourcing; AI may further accelerate this process. - Czech performance: - Productivity in selected services subsectors has grown fast, but constrained resources prevented output expansion. - Services exports have fallen behind peers despite Czech cost advantages. - High value-added professional services have lagged Europe over the past two years. ### Policy levers to support productivity-enhancing reallocation - Labor market and public sector: - Contain the sustained expansion of the public administration, including at the municipality level. - Implement more targeted active labor market policies to facilitate reallocation towards high-productivity sectors. - Use migration policies to better integrate workers into these sectors. - Lift restrictions for some professional activities where appropriate. - Digital adoption and infrastructure: - Enhance adoption of digital technologies and expand digital infrastructures, including broadband. - Business environment and finance: - Simplify the process of starting businesses to address relatively high barriers and low company birth rates. - Expand availability of venture capital and equity financing to close the financing gap for early-stage innovative companies. - Address lack of collateral or credit track record limiting access to bank funding for young firms. - Sector-specific bottlenecks: - Address bureaucratic hurdles around construction permits. - Remove impediments to renewables investment. ### The role and economic footprint of the automotive industry (2023) - Macro importance: - Automotive sector accounted for approximately 10 percent of the country’s GDP, the highest share of total value added in Europe. - Direct employment: over 200 thousand people (around 3.7 percent of total employment). - Direct + indirect employment: 434 thousand (8 percent of total employment). - Automotive exports: nearly 20 percent of the country’s total exports in 2023. - Domestic linkages: - Around 50 percent of the sector’s value added is generated using domestic intermediate inputs. - Auto parts and accessories export destinations by value added share: - Germany: 20 percent - France: 9 percent - Poland: 4.5 percent ### Auto industry challenges, global dynamics, and projections - Recent shocks and recovery: - Pandemic, supply disruptions (including semiconductor shortages), rising energy costs, and shortage of skilled labor affected the sector. - Production returned to its pre-pandemic level in 2023. - Global production and EV transition: - Projected global new car sales by 2028: around 109 million vehicles, surpassing the 2017 peak of 95 million (EIU 2024). - Share of new EVs in total sales projected to increase from 18 percent in 2023 to 35 percent by 2028, with China accounting for over half of the sales (EIU 2024). - Trade tensions and policy responses: - EU imposed additional tariffs ranging between 17.4 and 38.1 percent, on top of existing 10 percent EU MFN tariffs on car imports. - EU regulatory compliance requires achieving a 55 percent reduction in CO2 emissions for new cars and a complete transition to zero emissions by 2035. ### Simulated shocks and their impacts on the Czech Republic (Wingender et. al, 2024) - Scenarios: - EV Shock: increasing China's share of EU automotive spending by 15 percentage points over five years. - Estimated short- to medium-term loss for the Czech Republic: around 1.5 percent of GDP. - Mechanisms: cheaper Chinese EVs reduce demand for Czech-produced ICE vehicles; high dependence on the automotive sector and capital intensity amplify macroeconomic effects. - Protectionist Response: additional tariffs on Chinese EVs. - Outcomes: reduces car imports from China but leads to more adverse short- and long-run effects for the Czech Republic due to lost consumer benefits, higher costs of intermediate goods, exchange rate appreciation hurting exports, and negative labor market effects relative to a non-tariff scenario. - FDI Delivery: Chinese firms meet higher EV demand by establishing production in the EU via FDI. - Outcomes: reduced costs and mitigated GDP losses relative to the EV Shock scenario, contingent on proportional FDI distribution across EU countries and the Czech Republic's ability to attract FDI. ### Policy implications and strategic priorities for the Czech auto sector - Diversification and resilience: - Diversify geographic markets and industrial bases to reduce vulnerability from regional concentration of exports (notably heavy reliance on Germany). - Investment and upgrading: - Align manufacturing practices with EU CO2 emission reduction targets and invest in technologies and production facilities for EVs. - Retrain and upskill the workforce to address shortages of specialized skills required by EV production. - Secure supply chains for critical EV inputs (e.g., batteries, semiconductors, raw materials) and consider implications of tariffs and local-content requirements. - Trade strategy: - Carefully weigh protectionist measures; tariffs may produce adverse macroeconomic and labor market effects. - Pursue policies that improve the Czech Republic’s attractiveness for FDI to capture potential benefits of localized EV production. ### Policy measures to support the automotive industry (summary) - Objectives: Alleviate the green transition challenge and increased competition from Chinese manufacturers via innovation, workforce development, infrastructure, and calibrated consumer incentives. - Investment in R&D: - Provide financial incentives for domestic producers to invest in R&D for EV technology and battery development (improve battery storage capacity and accelerate charging time). - Labor transformation: - Promote reskilling and upskilling programs; invest in job placement services for transitioning workers. - Infrastructure: - Expedite development of EV charging stations; promote renewable energy sources for EV charging. - Trade and investment policies: - Improve business environment to attract foreign EV producers; seek trade arrangements favoring Czech exports in EV-related technologies and components. - Consumer incentives: - Introduce tax breaks and subsidies for EV purchases. - Marketing and branding: - Develop a strong brand identity highlighting Czech quality craftsmanship and expertise. - Expected outcomes: - Enhance resource allocation toward auto production; leverage skilled labor and supply chains to gain global competitiveness. ### The Czech Republic’s natural rate of interest (r*) - Definition: r* is the level of the real short-term interest rate that would keep output at potential and inflation at target. - Approaches used: - HLW: Semi-structural model following Holston, Laubach and Williams (2017). - BV: Bulir and Vlcek (2024) open-economy extension. - DN: Del Negro et al. (2017) factor model. - Range of estimates for 2024: - "The three approaches estimate the natural rate in a range of 0 to 1.5 percent in 2024." - Specific point estimates: - HLW: r* for Q2 2024 slightly below 1.5 percent. - DN: r* slightly above 1 percent. - BV: r* slightly below 0 percent. - Divergence and uncertainty: - On average, the three estimates diverge by more than 1 percent and at times by up to 3 percent. - Example: 90 percent confidence bands of around +/- 0.75 percent size around the DN approach. - Link to policy stance and inflation: - Policy stance is accommodative if r < r* and restrictive if r > r*. - Correlation with future inflation (up to 18 months): - DN measure correlation: 0.9. - HLW-implied stance correlation: 0.8. - BV stance correlation: 0.72. - Implication: "The current estimate of r* around 1 percent implies a terminal rate for monetary policy at around 3 percent given inflation expectations and actual inflation close to 2 percent." - Given a current rate of 4 percent, "monetary policy is still tight, leaving further room to lower policy rates." - If r* were to drop close to 0, monetary policy would be limited to stabilizing the business cycle and there would be increased need for unconventional policies as during the 2013-17 period. - Fiscal implication: real rates around 1 percent would imply considerably larger debt service payments than observed during 2016-23 when real ex-post yields on government bonds have been negative. - Structural drivers of r*: - Factors decreasing r*: demographics, lower growth, higher global uncertainty, vanishing safe asset status of government bonds, retrenchment of global savings glut. - Factors increasing r*: higher public investment (energy transition, defense), AI-led technological growth, higher energy efficiency and integrated value chains over time. - Demographic specifics: - Ratio of middle-aged prime savers (40-49) to young individuals (20-29) increased from below 0.9 in 2006 to 1.64 in 2023. - Ratio of retired to the working age population increased from 19 to 32 percent from 1990 to 2023. ### How markets perceive macroeconomic news: methodology and caveats - Methodology: - Event-study approach estimating effects of macroeconomic news (CPI and GDP) and domestic and foreign monetary policy surprises on the Czech koruna exchange rate, its volatility, and domestic market interest rates. - Surprise measures: Bloomberg economic calendar for GDP and CPI; CNB monetary policy surprises from Bloomberg; ECB surprises from intra-daily changes in 1-month OIS (Euro Area Monetary Policy Event-Study Database); Fed surprises from 30-minute changes in expectations after FOMC meetings. - Comparative periods: 2014-21 and 2022-24 to detect changes under a more data-dependent monetary policy. - Caveats: - An intra-daily window (few minutes around the event) could yield different results. - Regressions do not control for the macroeconomic environment, leaving ambiguity about whether differential responses pre- and post-2022 are due to the inflation environment or changes in monetary policy making. ### Exchange rate volatility has increased in recent years - Trend: - Volatility spiked in response to Covid (early-2020) and Russia’s invasion of Ukraine (early-2022). - On average, exchange rate volatility has been more than twice as high since 2020 compared to the previous five years. - Domestic monetary policy effects: - Policy rate decisions have led to higher exchange rate volatility after 2021. - CNB rate decisions deviating from expectations have raised EUR/CZK volatility post-2021 more than three times as much as before. - Surprise tightening: a 25-basis point surprise tightening is associated with an appreciation of the Czech koruna vis-à-vis the euro of 0.44 percent compared to 0.19 percent pre-2022. - CPI and GDP news different from expectations have not led to higher exchange rate volatility post-2021. - Domestic macro news effects on market interest rates: - Domestic GDP and CPI news have had larger effects on domestic market interest rates post-2021. - 1x4 Forward Rate Agreements show significantly stronger reactions post-2021; 2-year government bond yields do not show much differential response. - Government bond spreads: positive CPI news produced larger increases in spreads post-2021; positive GDP news produced larger decreases in spreads post-2021. - Foreign monetary policy spillovers: - Foreign monetary policy surprises associated with significantly larger volatility in domestic market interest rates since 2022. - Fed surprise rate hikes led to stronger interest rate increases post-2021 for the 3-month Pribor and the 2y government bond yields. - ECB surprise tightenings post-2022 have led to stronger decreases in the 3-month interbank market rate (possible mechanism: euroization of the Czech economy). - Central bank policy trade-off: - Option i) More data-dependent approach: respond to data outturns but face greater financial market volatility. - Option ii) Return to a more forecast-based rule: potentially reduce volatility but risk greater deviation from target. - As inflation aligns to target, the cost of higher volatility may outweigh benefits of heavy weighting on recent data. - Key quantitative findings (selected coefficients and sample sizes): - CNB Policy Rate Surprises and CZK Volatility: β = 99.7 (Pre-22), β = 593.5 (Post-21). - CNB Policy Rate Surprises and CZK Depreciation: β = -0.76 (Pre-22), β = -1.75 (Post-21). - GDP Surprises and CZK Volatility: β = 18.32 (Pre-22), β = 26.94 (Post-21). - CPI Surprises and CZK Volatility: β = 82.38 (Pre-22), β = -12.18 (Post-21). - 1x4 FRA Rate responses: - GDP: 0.0396 (Pre-22), 5.489** (Post-21). - CPI: 3.432** (Pre-22), 9.685*** (Post-21). - CNB MP: 0.616*** (Pre-22), 0.941*** (Post-21). - 3m Pribor responses: - FED MP: -0.0262 (Pre-22), 0.228 (Post-21). - ECB MP: -0.00079 (Pre-22), 0.00033 (Post-21). - 2y government bond yield responses: - FED MP: -0.0520 (Pre-22), 0.399** (Post-21). - CNB MP: 0.415*** (Pre-22), 0.582*** (Post-21). - 5-year government bond spread responses: - FED MP: -0.453 (Pre-22), 2.536** (Post-21). - CPI: 0.0446* (Pre-22), 0.147*** (Post-21). - Table sample sizes (N) for events in 2014-21 (2022-24): - GDP: 78 (33) - CPI: 96 (32) - CNB MP: 64 (21) - ECB: 68 (15) - FED: 63 (19) *Source: 1. Exports and Contribution to Export Share Increase (1czeea2025002-print-pdf)* ### 1. Exports and Contribution to Export Share Increase ________________________________ 4 ### 1. Exports and Contribution to Export Share Increase ### Overview - The Czech economy has underperformed European peers in the post-pandemic period and economic convergence has come to a halt. - Common attributions: close ties to Germany and an energy-intensive economic structure. - IMF analysis finds these factors only partly explain the underperformance; declining productivity and structural weaknesses play a major role. ### Exports and Economic Structure - Exports have performed broadly in line with exports in the rest of the EU since the pandemic. - A constant share decomposition shows Czech exporters offset weak German demand by expanding in other fast-growing markets, including Poland. - Using the Caceres et al. (2021) methodology to benchmark actual output loss against a counterfactual economy with European-average sector weights: - Only one third of the output loss relative to a pre-pandemic trend can be attributed to an unfavorable economic structure (concentration in sectors affected by supply-side disruptions and energy- or interest-rate-sensitive sectors). - The remaining two thirds is explained by underperformance within sectors. ### The Productivity Slowdown: Three Key Stylized Facts - System-wide productivity growth has been on a declining trend: - After the GFC productivity growth remained higher than that of Germany and other EU countries but the gap reduced considerably. - Since end-2019, labor productivity has fallen in the Czech Republic while it advanced, albeit modestly, in the EU. - Both slower total factor productivity (TFP) growth and capital deepening contributed to the decline. - Decomposition with EU KLEMS indicates both factors lowered labor productivity; TFP decelerated significantly and did not recover to pre-GFC growth rates. - Sector-specific developments explain much of the slowdown: - Construction: significant labor productivity underperformance vs. most EU countries and CEE peers; vacancies peaked at 16 percent in 2019 and declined only gradually afterwards; sector has shrunk by nearly 25 percent over the last ten years. - Mining and energy: sharper correction due to falling demand, phasing out of coal production, and limited investment in renewables. - Non-auto manufacturing: stagnant for almost a decade, consistent with slower industrialization during convergence. - Automotive industry (especially car parts): generally resilient after initial supply-chain disruptions; competitiveness gains more than demand-driven, though inventory movements may distort measured progress. - Distance to the EU TFP frontier shows sectoral variation: - TFP gap for energy-related sectors, particularly electricity, increased in 2019 compared to 2011. - Mining shows a consistently larger TFP gap from the EU frontier. - ICT and financial and insurance services saw a decrease in the TFP gap as TFP grew broadly in line with EU frontier countries. ### Labor Reallocation and Resource Misallocation - Inefficient reallocation of labor across sectors contributed to the productivity slowdown: - Shift-share analysis (2019Q4-2024Q1) indicates allocative inefficiency explains the post-pandemic productivity decline. - High-productivity sectors (manufacturing, financial services) have reduced workforce; low-productivity public administration expanded considerably. - ICT sector employment increased but not enough to meet labor demand; persistent labor shortages and skills mismatches remain, with a high share of tertiary graduates working in occupations not requiring their degree. - Overall, labor is increasingly concentrated in low-productivity sectors, in contrast to the EU where reallocation has positively affected productivity. - Financial flows misallocation: - Strong growth in bank credit to low-productivity sectors, including public administration. - Declining credit to high-productivity sectors such as manufacturing and ICT. - Decline in FDI inflows into manufacturing over the last decade; FDI shifted toward financial and insurance services. - Capital markets are underdeveloped and venture capital investment is low relative to other OECD countries. ### Econometric Analysis of Industry-Level TFP Growth - Industry-level TFP growth modeled for period 1995-2020 using EU-KLEMS and other macro datasets; baseline specification includes: - TFP growth frontier ∆yL (highest TFP growth in industry j at time t) to capture innovation spillovers. - Technological gap (yi,j,t-1 − yL i,j,t-1) captures potential gains from adopting frontier technologies. - Industry- and country-level controls: ICT capital spending, non-ICT capital spending, R&D spending, consumer price inflation, domestic credit to the private sector. - Key empirical findings: - TFP growth at the frontier is positive and statistically significant across all tradable industries but insignificant in the non-tradable sector. Tradable sectors thus benefit from innovation spillovers. - The technological gap is a significant determinant of TFP growth: adopting existing technologies at the sectoral level is a powerful engine for convergence. - R&D spending as a share of gross fixed investment has a positive effect on TFP growth across sectors, but is statistically significant only for the non-tradable sector; the magnitude is greater for non-tradables. - ICT capital spending as a share of gross fixed investment is positively associated with higher TFP growth, but not statistically significant. - Non-ICT capital spending (proxied by transportation equipment) as a share of gross fixed investment does not appear to matter for TFP growth across specifications. - Inflation has a negative but significant effect on TFP growth across all industries and in the tradable sector; it is insignificant in the non-tradable sector. - Financial development (domestic credit to the private sector) has a positive but insignificant effect on TFP growth across specifications. ### Policy Implications and Recommendations - A multi-pronged policy approach is needed to address: - Economy-wide frictions (system-level). - Sector-level bottlenecks (construction, mining and energy, non-auto manufacturing). - Inefficient allocation of labor and capital across sectors. - Specific policy pointers from the analysis: - Encourage greater use of existing tax credits for R&D to support innovation and broader diffusion of technologies across industries. - Upgrade capacity in new technologies to narrow the technological gap vis-à-vis the frontier and to boost productivity growth. - Address sectoral constraints (skills shortages, administrative burdens) and improve allocation of credit and FDI toward high-productivity sectors. *Source: 1. Exports and Contribution to Export Share Increase (1czeea2025002-print-pdf)* ### 16. Findings also indicate that sectoral productivity is likely to converge toward the ### 1czeea2025002-print-pdf - 16. Findings also indicate that sectoral productivity is likely to converge toward the ### Sectoral productivity convergence: empirical findings - Main finding: Sectoral productivity is likely to converge toward the frontier if bottlenecks in underperforming sectors are effectively addressed. - Convergence mechanisms highlighted: upgrading of processes and products, adoption of existing technologies, and leveraging of knowledge spillovers. - Policy imperative: Expediting processes, especially at the local administration level, is critical to address bottlenecks in the construction sector and scale up renewable investments in the energy sector. - Key regression results (dependent variable: TFP growth; columns: All, Tradables, Non-tradables): - TFP growth at frontier: - All: 0.133** (t = 2.697) - Tradables: 0.203*** (t = 4.188) - Non-tradables: -0.104 (t = -0.972) - Technological gap: - All: -13.40*** (t = -3.148) - Tradables: -15.62** (t = -2.664) - Non-tradables: -10.15** (t = -2.525) - ICT capital: - All: 1.944 (t = 0.288) - Tradables: 5.508 (t = 0.827) - Non-tradables: 3.105 (t = 0.248) - Non-ICT capital: - All: 9.875 (t = 1.274) - Tradables: 7.931 (t = 0.670) - Non-tradables: 12.75 (t = 1.621) - R&D spending: - All: 19.84 (t = 1.594) - Tradables: 14.505 (t = 0.933) - Non-tradables: 1.11* (t = 1.931) - Inflation: - All: -0.746** (t = -2.630) - Tradables: -0.826* (t = -1.845) - Non-tradables: -0.521 (t = -1.315) - Financial development: - All: 0.131 (t = 1.639) - Tradables: 0.0763 (t = 0.731) - Non-tradables: 0.137 (t = 1.494) - Number of observations: - All: 628 - Tradables: 353 - Non-tradables: 275 - Adjusted R2: - All: 0.144 - Tradables: 0.179 - Non-tradables: 0.239 - Statistical notes: Robust t-statistics in parentheses; a constant is included in all specifications but not shown. Significance levels: *** p<0.01, ** p<0.05, * p<0.1. ### Structural transformation, manufacturing, and services - Manufacturing share and trends: - Manufacturing still represents 21 percent of the Czech economy. - Manufacturing share of GDP has declined since 2017; employment share decline is typically even more pronounced. - International evidence: an observed hump-shaped relationship between industrialization and income convergence. - Services transition: - A higher share of services does not necessarily correlate with lower productivity growth. - The services sector comprises subsectors with different productivity levels and growth rates; aggregate productivity effects depend on which subsectors expand. - Global context: growing global services exports, exceptional growth in digitally delivered services, increased tradability and international outsourcing; artificial intelligence may further accelerate this process. - Czech Republic performance: - Productivity in selected services subsectors has grown fast, but constrained resources have prevented output expansion. - Services exports have fallen behind peers despite Czech cost advantages. - High value-added professional services have lagged Europe over the past two years. ### Policy levers to support productivity-enhancing reallocation - Labor market and public sector: - Contain the sustained expansion of the public administration, including at the municipality level. - Implement more targeted active labor market policies to facilitate reallocation towards high-productivity sectors. - Use migration policies to better integrate workers into these sectors. - Lift restrictions for some professional activities where appropriate. - Digital adoption and infrastructure: - Enhance adoption of digital technologies and expand digital infrastructures, including broadband. - Business environment and finance: - Simplify the process of starting businesses to address relatively high barriers and low company birth rates. - Expand availability of venture capital and equity financing to close the financing gap for early-stage innovative companies. - Address lack of collateral or credit track record limiting access to bank funding for young firms. - Sector-specific bottlenecks: - Address bureaucratic hurdles around construction permits. - Remove impediments to renewables investment. ### The role and economic footprint of the automotive industry - Macro importance (2023): - Automotive sector accounted for approximately 10 percent of the country’s GDP, the highest share of total value added in Europe. - Direct employment: over 200 thousand people (around 3.7 percent of total employment). - Direct + indirect employment: 434 thousand (8 percent of total employment) when accounting for downstream industries. - Automotive exports: nearly 20 percent of the country’s total exports in 2023. - Domestic linkages: - Around 50 percent of the sector’s value added is generated using domestic intermediate inputs. - Auto parts and accessories export destinations by value added share: - Germany: 20 percent - France: 9 percent - Poland: 4.5 percent ### Auto industry challenges, global dynamics, and projections - Recent shocks and recovery: - Pandemic, supply disruptions (including semiconductor shortages), rising energy costs, and shortage of skilled labor affected the sector. - Production returned to its pre-pandemic level in 2023. - Global production and EV transition: - Projected global new car sales by 2028: around 109 million vehicles, surpassing the 2017 peak of 95 million (EIU 2024). - Share of new EVs in total sales projected to increase from 18 percent in 2023 to 35 percent by 2028, with China accounting for over half of the sales (EIU 2024). - Trade tensions and policy responses: - China’s EV advancement has triggered trade tensions; the EU imposed additional tariffs ranging between 17.4 and 38.1 percent, on top of existing 10 percent EU MFN tariffs on car imports. - Regulatory compliance in the EU requires achieving a 55 percent reduction in CO2 emissions for new cars and a complete transition to zero emissions by 2035. ### Simulated shocks and their impacts on the Czech Republic - Scenarios modeled (Wingender et. al, 2024): - EV Shock: increasing China's share of EU automotive spending by 15 percentage points over five years. - Estimated short- to medium-term loss for the Czech Republic: around 1.5 percent of GDP. - Mechanisms: cheaper Chinese EVs reduce demand for Czech-produced ICE vehicles; high dependence on the automotive sector and capital intensity amplify macroeconomic effects. - Protectionist Response: additional tariffs on Chinese EVs. - Outcomes: reduces car imports from China but leads to more adverse short- and long-run effects for the Czech Republic due to lost consumer benefits, higher costs of intermediate goods, exchange rate appreciation hurting exports, and negative labor market effects (lower wages and employment) relative to a non-tariff scenario. - FDI Delivery: Chinese firms meet higher EV demand by establishing production in the EU via FDI. - Outcomes: reduced costs and mitigated GDP losses relative to the EV Shock scenario, contingent on proportional FDI distribution across EU countries and the Czech Republic's ability to attract FDI. ### Policy implications and strategic priorities for the Czech auto sector - Diversification and resilience: - Diversify geographic markets and industrial bases to reduce vulnerability from regional concentration of exports (notably heavy reliance on Germany). - Investment and upgrading: - Align manufacturing practices with EU CO2 emission reduction targets and invest in technologies and production facilities for EVs. - Retrain and upskill the workforce to address shortages of specialized skills required by rapid technological advancements and EV production. - Secure supply chains for critical EV inputs (e.g., batteries, semiconductors, raw materials) and consider implications of tariffs and local-content requirements. - Trade strategy: - Carefully weigh protectionist measures; tariffs may produce adverse macroeconomic and labor market effects. - Pursue policies that improve the Czech Republic’s attractiveness for FDI to capture potential benefits of localized EV production. *Italicized source: Content from 1czeea2025002-print-pdf - 16. Findings also indicate that sectoral productivity is likely to converge toward the (PDF chapter/section).* ### 11. Several policy measures can be implemented to alleviate the potential economic ### 11. Several policy measures can be implemented to alleviate the potential economic impact and enhance resilience of the Czech Republic's automotive industry ### Policy measures and recommendations for the automotive industry - Overall objective: - Alleviate the dual challenge of the green transition towards electric vehicles (EVs) and increased competition from Chinese manufacturers. - Emphasize innovation, workforce development, infrastructure, and carefully calibrated consumer incentives to support sustainable growth. - Investment in Research and Development (R&D): - Provide financial incentives for domestic producers to invest in R&D for EV technology and battery development, including by improving the battery storage capacity to increase range and accelerate charging time. - Rationale: Foster innovation and create competitive advantages. - Support for labor transformation: - Promote reskilling and upskilling programs to equip the labor force with the skills required for the EV sector, such as battery production and software development. - Invest in job placement services to assist workers transitioning from traditional auto manufacturing jobs to new roles within the EV industry. - Infrastructure development: - Expedite development of EV charging stations across the country to facilitate the transition to EVs and encourage consumer adoption. - Continue to promote renewable energy sources for EV charging. - Trade and investment policies: - Continue to improve the business environment to attract foreign companies that produce EVs or components, including through streamlined regulations and procedures. - Seek trade arrangements to favor Czech exports in EV-related technologies and components. - Consumer incentives: - Introduce tax breaks and subsidies for EV purchases to stimulate demand for locally produced vehicles. - Marketing and branding: - Promote a comprehensive marketing and branding strategy, including developing a strong brand identity highlighting quality craftsmanship and expertise behind Czech automotive manufacturing. - Expected outcomes if implemented: - Enhance the Czech Republic’s ability to efficiently allocate resources towards auto production. - Leverage existing skilled labor force and established supply chains to further global competitiveness and effectively gain market share. *Italicized source attribution: Content from the IMF chapter on policy measures for the Czech Republic automotive industry.* --- ### The Czech Republic’s natural rate of interest (r*) ### Definition, approaches, and estimation methods - Definition: - The real natural or neutral interest rate r* is the level of the real short-term interest rate that would keep output at potential and inflation at target. - Approaches used in the note: - HLW: Semi-structural model following Holston, Laubach and Williams (2017). Produces a natural rate that includes some persistence (medium to long-term estimate). - BV: Bulir and Vlcek (2024) open-economy extension (includes exchange rate effects and foreign growth via foreign interest rates). - DN: Del Negro et al. (2017) factor model decomposing observable variables into cyclical and trend components using cointegrating relationships. ### Key quantitative estimates and comparisons - Range of estimates for 2024: - "The three approaches estimate the natural rate in a range of 0 to 1.5 percent in 2024." - Specific point estimates: - HLW: estimates r* for Q2 2024 slightly below 1.5 percent. - DN: estimates r* at slightly above 1 percent. - BV: estimates r* at slightly below 0 percent. - Divergence and uncertainty: - On average, the three estimates diverge by more than 1 percent and at times by up to 3 percent. - Estimation uncertainty example: 90 percent confidence bands of around +/- 0.75 percent size around the DN approach. - Sensitivities: results depend on interest rate series (type and maturity), inflation expectations (survey- vs. model-based), parameter and prior specification, and sample start. ### Link to policy stance and inflation - Policy stance interpretation: - Policy stance is accommodative if r < r* and restrictive if r > r*. - Correlation with future inflation (monetary policy-relevant horizon up to 18 months): - DN measure produces the largest correlation with future inflation: 0.9. - HLW-implied stance correlation: 0.8. - BV stance correlation: 0.72. - Implications for current policy: - "The current estimate of r* around 1 percent implies a terminal rate for monetary policy at around 3 percent given inflation expectations and actual inflation close to 2 percent." - "This implies that at the current rate of 4 percent monetary policy is still tight, leaving further room to lower policy rates." - If r* were to drop close to 0, monetary policy would be limited to stabilizing the business cycle and there would be an increased need for unconventional policies as during the 2013-17 period. - Fiscal policy implication: real rates around 1 percent would imply considerably larger debt service payments than observed during 2016-23 when real ex-post yields on government bonds have been negative. ### Structural and evolving drivers of r* - Factors exerting decreasing pressure on r*: - Demographics (e.g., changing age composition). - Lower growth (e.g., from supply-chain reshoring and the energy transition). - Higher global uncertainty and higher demand for safe assets. - Vanishing safe asset status of government bonds' (increasing government debt). - Retrenchment of global savings glut. - Factors exerting increasing pressure on r*: - Higher public investment (e.g., from the energy transition and defense). - AI-led technological growth. - Over time, better energy efficiency and integrated value chains may boost growth and unwind downward pressure from transition costs; higher investment spending related to reshoring would buffer downward pressures. - Demographic specifics cited: - Ratio of middle-aged prime savers (40-49) to young individuals (20-29) increased from below 0.9 in 2006 to 1.64 in 2023. - Ratio of retired to the working age population increased from 19 to 32 percent from 1990 to 2023. - Other contextual drivers: - Energy transition costs could exert downward pressure on growth and thus on r*, while higher investment needs would increase r*. - Geoeconomic fragmentation and reshoring may increase costs, lowering potential and putting downward pressure on r*. - Increased energy efficiency and AI-led productivity growth could boost growth and r*. - Higher global uncertainty and demand for safe assets would increase savings and exert downward pressure on r*; however, higher imported inflation from a weaker koruna could require local policy rates to rise. *Italicized source attribution: Content from the IMF chapter on the Czech Republic’s natural rate of interest.* --- ### How markets perceive macroeconomic news: methodology and caveats ### Methodology - Event-study approach: - Simple regression approach estimating effects of macroeconomic news (CPI and GDP) and domestic and foreign monetary policy surprises on the Czech koruna exchange rate, its volatility, and domestic market interest rates. - Data and surprise measures: - GDP and CPI news surprises, and CNB monetary policy surprises: taken from the Bloomberg economic calendar as the difference between actual announcement and the last survey estimate. - ECB monetary policy surprises: intra-daily changes in the 1-month overnight index swap rates from the Euro Area Monetary Policy Event-Study Database (Altavilla et al., 2019). - Fed monetary policy surprises: 30-minute changes in expectations of the Federal Funds rate immediately after each FOMC meeting. - Comparative periods: - Market reactions estimated separately for the periods 2014-21 and 2022-24 to explore potential changes under a more data-dependent monetary policy. ### Key considerations and caveats - Two main caveats noted: - An intra-daily window (few minutes around the event) could give different results because it is cleaner of other same-day news. - Regressions do not control for the macroeconomic environment, leaving it ambiguous how much differential responses pre- and post-2022 are due to the inflation environment versus changes in monetary policy making. - Motivation: - To detect whether changes in monetary policy frameworks (from forecast-based inflation targeting to more data-dependent meeting-by-meeting approaches) have altered asset price responses to macro news, potentially increasing absolute size of responses or volatility with implications for financial stability and international trade. *Italicized source attribution: Content from the IMF chapter on how markets perceive macroeconomic news.* ### 5. Exchange rate volatility has increased in recent years. After relatively low levels of ### 5. Exchange rate volatility has increased in recent years. ### Exchange rate volatility trend - After relatively low levels of exchange rate volatility during the second half of the 2010s, volatility has spiked, most evidently in response to the Covid outbreak in early-2020 and to Russia’s invasion of Ukraine in early-2022. - On average, exchange rate volatility has been more than twice as high since 2020 compared to the previous five years. ### Domestic monetary policy effects on exchange rate - Policy rate decisions have led to higher exchange rate volatility after 2021. - CNB rate decisions deviating from expectations have raised EUR/CZK volatility post-2021 more than three times as much as before. - Surprise tightening of domestic monetary policy have led to larger Czech koruna appreciations than pre-2022: - A 25-basis point surprise tightening is associated with an appreciation of the Czech koruna vis-à-vis the euro of 0.44 percent compared to 0.19 percent pre-2022. - CPI and GDP news different from expectations have not led to higher exchange rate volatility post-2021. ### Domestic macro news effects on market interest rates - Domestic GDP and CPI news have had larger effects on domestic market interest rates post-2021. - Post-2021, both positive CPI and GDP news have produced relatively larger increases in short-term interest rates: - 1x4 Forward Rate Agreements show significantly stronger reactions post-2021. - 2-year government bond yields do not show much of a differential response across the two sample horizons. - Government bond spreads responses changed significantly post-2021: - Positive CPI news produced larger increases in spreads post-2021. - Positive GDP news produced larger decreases in spreads post-2021. - Interpretation: Stronger inflation raises domestic rates while stronger growth seems to lower the risk premium for domestic bond yields. ### Foreign monetary policy spillovers - Foreign monetary policy surprises were associated with significantly larger volatility in domestic market interest rates since 2022. - Fed surprise rate hikes led to stronger interest rate increases post-2021: - Stronger increases for the 3-month Pribor and the 2y government bond yields post-2021, indicating stronger co-movement with US monetary policy and inflation cycles. - ECB surprise decisions produced different effects: - Post-2022, ECB surprise tightenings have led to stronger decreases in the 3-month interbank market rate. - Possible mechanism: significant euroization of the Czech economy—tighter euro area monetary policy also restricts domestic financial conditions. - Volatility of interest rate reactions to foreign monetary policy surprises has been much larger than pre-2022. ### Central bank policy trade-off - A trade-off between the cost of market volatility and the benefit of data dependence may emerge: - Option i) Pursue a more data-dependent approach: respond to data outturns that may provide stronger informative signals about future inflation but face greater financial market volatility. - Option ii) Return to a more forecast-based policy rule: potentially reduce volatility but risk greater deviation from target. - As inflation becomes more closely aligned to target, the cost of higher volatility is more likely to outweigh the benefit of attaching a large weight on recent data when setting policy rates. ### Key quantitative findings (selected figures and regression coefficients) - CNB Policy Rate Surprises and CZK Volatility: β = 99.7 (Pre-22), β = 593.5 (Post-21). - CNB Policy Rate Surprises and CZK Depreciation: β = -0.76 (Pre-22), β = -1.75 (Post-21). - GDP Surprises and CZK Volatility: β = 18.32 (Pre-22), β = 26.94 (Post-21). - CPI Surprises and CZK Volatility: β = 82.38 (Pre-22), β = -12.18 (Post-21). - 1x4 FRA Rate responses: - GDP: 0.0396 (Pre-22), 5.489** (Post-21). - CPI: 3.432** (Pre-22), 9.685*** (Post-21). - CNB MP: 0.616*** (Pre-22), 0.941*** (Post-21). - 3m Pribor responses: - FED MP: -0.0262 (Pre-22), 0.228 (Post-21). - ECB MP: -0.00079 (Pre-22), 0.00033 (Post-21). - 2y government bond yield responses: - FED MP: -0.0520 (Pre-22), 0.399** (Post-21). - CNB MP: 0.415*** (Pre-22), 0.582*** (Post-21). - 5-year government bond spread responses: - FED MP: -0.453 (Pre-22), 2.536** (Post-21). - CPI: 0.0446* (Pre-22), 0.147*** (Post-21). - Table sample sizes (N) for events in 2014-21 (2022-24): - GDP: 78 (33) - CPI: 96 (32) - CNB MP: 64 (21) - ECB: 68 (15) - FED: 63 (19) *Source: IMF staff analysis in the Czech Republic chapter.* --- _Source: https://www.imf.org/-/media/files/publications/cr/2025/english/1czeea2025002-print-pdf.pdf_