## sipea2025139

## Source details

**Canonical URL:** [sipea2025139](https://www.imf.org/-/media/files/publications/selected-issues-papers/2025/english/sipea2025139.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/selected-issues-papers/2025/english/sipea2025139.pdf.md)
- [Structured JSON version](/-/media/files/publications/selected-issues-papers/2025/english/sipea2025139.pdf.json)

---

### The Evolving Growth Model of Lithuania — A. Introduction
- Lithuania has reached key milestones in European integration: ten years in the euro area and two decades in the EU.
- GDP per capita has risen by 116% percent since regaining independence.
- Growth and labor markets benefited from favorable migration dynamics, including returning Lithuanians and a temporary surge in migration from Ukraine and Belarus.
- The economy recovered strongly in 2024 with faster growth than Baltic peers and other EA countries.
- Recent strong growth was largely driven by labor accumulation while capital intensity remains low and productivity growth has been weak.
- New fiscal pressures: aging, increased defense spending needs, and uncertainty about migration flows.
- Policy implication: structural reforms to support capital accumulation and increase productivity are key to restoring the convergence path.

### Output Convergence and Transformation — B
- Rapid income convergence:
  - GDP per capita increased from about 30 percent of the euro area in the second half of the 1990s to about 85 percent in recent years.
  - Convergence was particularly fast in the decade preceding the GFC and during the 2010s, with growth largely driven by strong TFP growth and capital accumulation.
  - Over the past five years, convergence has stalled as positive contribution from labor accumulation—largely due to positive net migration flows—was offset by negative TFP growth.
- ICT sector expansion:
  - ICT grew on average by about 10 percent per year over the past ten years.
  - ICT share of GDP: 6 percent by the end of 2024, up from 3 percent in 2015.
  - Government policy support cited: 2021-27 Smart Specialization and 2023-28 Fintech Strategy.
  - Note: The increasing ICT share has not yet produced a systematic increase in labor productivity in the sector; potential future gains may arise after market selection of the most productive firms.
- Sectoral shifts:
  - Decreased shares in trade, travel, accommodation and food, and real estate.
  - Slight increases in manufacturing and construction.
  - Financial and insurance activities broadly stable.

### Structural Impediments to Growth — C
- Key long-run challenges: aging, structural change from technological progress, frictions in financial, labor and product markets, shallow capital markets, skill mismatches, inefficient firm insolvency regulations, green transition needs, and defense spending pressures.
- Capital deepening remains very low in Lithuania (Capital Deepening: EU=100 benchmark shown in figures).
- TFP growth:
  - Strong pre-pandemic dynamics turned negative in more recent years.
  - Comparison across EU shows Lithuania among countries with deteriorated TFP performance (TFP growth before and after the pandemic: percent).
- Firm-level constraints to investment:
  - Large share of non-financial corporations report lack of access to credit.
  - Gross expenditure on R&D (private and public) is relatively low (EU country comparisons; percent of GDP).
  - Few firms take advantage of available tax incentives for business R&D.
  - Digital skills constraint: only about half of Lithuanians have basic or above basic digital skills.
  - Digital uptake in firms is low: nearly 80% of firms have low or very low digital intensity despite adequate digital infrastructure.
- Firms investing too little: EIB and IMF indicators show high shares of firms underinvestment and financial constraints (percent measures).

### The Potential Benefits of AI — D
- Lithuania has invested significantly in digitalization, hosts one of Europe's main Fintech Hubs, and adopted a national AI strategy.
- Enterprise AI adoption remains limited relative to EU averages: enterprises using at least one AI technology (percentage of firms).
- AI preparedness and digital infrastructure indices place Lithuania around EU average in 2023 (AI Preparedness Index, Digital Infrastructure Index).
- Estimated potential TFP gains from AI adoption (baseline scenarios):
  - Between 0.11 percentage points per year (Misch et al., 2025) and 0.29 percentage points per year (Bergeaud, 2024) according to baseline scenarios.
  - Estimated TFP gains in the next 5 years from AI (percent) and in the next 10 years from AI (percent) shown in cross-country comparisons.
- Policy implications:
  - Facilitate technological diffusion, job transitions and AI adoption among firms.
  - Introduce measures to mitigate risks of job displacement and deepening inequality.
  - Increase R&D spending and innovation to support AI readiness and diffusion.

### Unlocking Productivity Growth via Structural Reforms — E
- Structural reforms are essential to support lasting recovery in labor productivity and expansion in high-value-added sectors.
- Priority reform areas and examples:
  - Labor market and education:
    - Reduce skill mismatches and improve labor quality.
    - Strengthen Active Labor Market Policies (ALMPs), including life-long learning and apprenticeships; reskill and retrain especially older workers; increase funding for ALMPs.
  - Financial sector and capital markets:
    - Reforms to allow deeper capital markets—ideally within a Capital Markets Union at the EU level—to support medium to large firms in accessing finance.
    - Reform property register procedures to allow more frequent updates of property values to improve collateral valuation for loans, especially benefiting SMEs.
  - Digitalization and AI preparedness:
    - Accelerate transition to a digitalized economy.
    - Increase R&D spending and innovation.
    - Policies to facilitate firm adoption of AI and diffusion of new technologies.
- Table 1 summarizes structural reforms to enhance productivity growth and priorities (labor market ALMPs example provided).

### Actionable Priorities and Implementation Notes — Section 2
- Strengthen collaboration with firms when designing activation programs, including training.
  - Priority: High
- Education reform to address skill mismatches:
  - Adapt PES-provided training and university curriculum to market needs and strengthen collaboration with firms.
  - Improve vocational training system, university courses targeting to address skills mismatch by producing those jobs the labor market needs.
  - Priority: High
- Financial Market — Deepening of capital markets and facilitating access to financing for SMEs:
  - Capital markets in Lithuania are not well developed, limiting investment and innovation, especially for SMEs.
  - Lithuanian firms tend to remain small and would benefit from facilitated access to financing for innovation (including boosting venture capital to ease financial conditions) targeting young growing firms.
  - Priority: Medium to high
- Innovation and R&D — Innovation, R&D, and digitalization:
  - Consolidating research institutions, simplifying access to public R&I support and incentivizing business R&I investment.
  - Priority: Medium to high

*Prepared by Saioa Armendariz and Alberto Musso; completed on July 29, 2025; SIP/2025/139.*

### Section 1

### The Evolving Growth Model of Lithuania

### A. Introduction
- Lithuania has reached key milestones in European integration: ten years in the euro area and two decades in the EU.
- GDP per capita has risen by 116% percent since regaining independence.
- Growth and labor markets benefited from favorable migration dynamics, including returning Lithuanians and a temporary surge in migration from Ukraine and Belarus.
- The economy recovered strongly in 2024 with faster growth than Baltic peers and other EA countries.
- Recent strong growth was largely driven by labor accumulation while capital intensity remains low and productivity growth has been weak.
- New fiscal pressures: aging, increased defense spending needs, and uncertainty about migration flows.
- Policy implication: structural reforms to support capital accumulation and increase productivity are key to restoring the convergence path.

### B. Output Convergence and Transformation
- Rapid income convergence: GDP per capita increased from about 30 percent of the euro area in the second half of the 1990s to about 85 percent in recent years.
- Convergence was particularly fast in the decade preceding the GFC and during the 2010s, with growth largely driven by strong TFP growth and capital accumulation.
- Over the past five years, convergence has stalled as positive contribution from labor accumulation—largely due to positive net migration flows—was offset by negative TFP growth.
- ICT sector expansion:
  - ICT grew on average by about 10 percent per year over the past ten years.
  - ICT share of GDP: 6 percent by the end of 2024, up from 3 percent in 2015.
  - Government policy support cited: 2021-27 Smart Specialization and 2023-28 Fintech Strategy.
- Sectoral shifts: decreased shares in trade, travel, accommodation and food, and real estate; slight increases in manufacturing and construction; financial and insurance activities broadly stable.
- Note: The increasing ICT share has not yet produced a systematic increase in labor productivity in the sector; potential future gains may arise after market selection of the most productive firms.

### C. Structural Impediments to Growth
- Key long-run challenges: aging, structural change from technological progress, frictions in financial, labor and product markets, shallow capital markets, skill mismatches, inefficient firm insolvency regulations, green transition needs, and defense spending pressures.
- Capital deepening remains very low in Lithuania (Capital Deepening: EU=100 benchmark shown in figures).
- TFP growth: strong pre-pandemic dynamics turned negative in more recent years; comparison across EU shows Lithuania among countries with deteriorated TFP performance (TFP growth before and after the pandemic: percent).
- Firm-level constraints to investment:
  - Large share of non-financial corporations report lack of access to credit.
  - Gross expenditure on R&D (private and public) is relatively low (EU country comparisons; percent of GDP).
  - Few firms take advantage of available tax incentives for business R&D.
  - Digital skills constraint: only about half of Lithuanians have basic or above basic digital skills.
  - Digital uptake in firms is low: nearly 80% of firms have low or very low digital intensity despite adequate digital infrastructure.
- Firms investing too little: EIB and IMF indicators show high shares of firms underinvestment and financial constraints (percent measures).

### D. The Potential Benefits of AI
- Lithuania has invested significantly in digitalization, hosts one of Europe's main Fintech Hubs, and adopted a national AI strategy.
- Enterprise AI adoption remains limited relative to EU averages: enterprises using at least one AI technology (percentage of firms).
- AI preparedness and digital infrastructure indices place Lithuania around EU average in 2023 (AI Preparedness Index, Digital Infrastructure Index).
- Estimated potential TFP gains from AI adoption (baseline scenarios):
  - Between 0.11 percentage points per year (Misch et al., 2025) and 0.29 percentage points per year (Bergeaud, 2024) according to baseline scenarios.
  - Estimated TFP gains in the next 5 years from AI (percent) and in the next 10 years from AI (percent) shown in cross-country comparisons.
- Policy implications:
  - Facilitate technological diffusion, job transitions and AI adoption among firms.
  - Introduce measures to mitigate risks of job displacement and deepening inequality.
  - Increase R&D spending and innovation to support AI readiness and diffusion.

### E. Unlocking Productivity Growth via Structural Reforms
- Structural reforms are essential to support lasting recovery in labor productivity and expansion in high-value-added sectors.
- Priority reform areas and examples:
  - Labor market and education:
    - Reduce skill mismatches and improve labor quality.
    - Strengthen Active Labor Market Policies (ALMPs), including life-long learning and apprenticeships; reskill and retrain especially older workers; increase funding for ALMPs.
  - Financial sector and capital markets:
    - Reforms to allow deeper capital markets—ideally within a Capital Markets Union at the EU level—to support medium to large firms in accessing finance.
    - Reform property register procedures to allow more frequent updates of property values to improve collateral valuation for loans, especially benefiting SMEs.
  - Digitalization and AI preparedness:
    - Accelerate transition to a digitalized economy.
    - Increase R&D spending and innovation.
    - Policies to facilitate firm adoption of AI and diffusion of new technologies.
- Table 1 summarizes structural reforms to enhance productivity growth and priorities (labor market ALMPs example provided).

*Prepared by Saioa Armendariz and Alberto Musso; completed on July 29, 2025; SIP/2025/139.*

### Section 2

### sipea2025139 - Section 2

### Strengthen collaboration with firms when designing activation programs, including training
- Strengthen collaboration with firms when designing activation programs, including training.
- High

### Education reform to address skill mismatches
- Adapt PES-provided training and university curriculum to market needs and strengthen collaboration with firms.
- Improve vocational training system, university courses targeting to address skills mismatch by producing those jobs the labor market needs.
- High

### Financial Market — Deepening of capital markets and facilitating access to financing for SMEs
- Capital markets in Lithuania are not well developed, limiting investment and innovation, especially for SMEs.
- Lithuanian firms tend to remain small and would benefit from facilitated access to financing for innovation (including boosting venture capital to ease financial conditions) targeting young growing firms.
- Medium to high

### Innovation and R&D — Innovation, R&D, and digitalization
- Consolidating research institutions, simplifying access to public R&I support and incentivizing business R&I investment.
- Medium to high

*Sources: IMF.*

---


_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2025/english/sipea2025139.pdf_