## 1. Panel Regression

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### Overview and main findings
- Lithuania’s unemployment rate has remained persistently high at around 7 percent since 2018 despite sustained growth and rising job vacancies.
- The (survey-based) unemployment rate fell from a peak of 18 percent in 2010 to around 6 percent in 2018, then broadly remained at this level except for the Covid-19 crisis in 2020.
- Registered unemployment rate fell to a low point of around 8 percent in 2016 and remained sticky around that level thereafter.
- Unemployment is concentrated among:
  - youth,
  - older workers,
  - the lower-educated,
  - residents outside major economic centers.
- Long-term unemployment remains sizable.
- Recent pattern summary:
  - Unemployment has averaged "8.5 percent since 2018" with a similar, albeit much larger spike in 2020.
  - Vacancies continued rising and peaked in "2025" while unemployment fluctuated around "7 percent".
  - Labor force survey interviews approximately "0.5 percent" of the population aged 15+ every quarter.

### Identified structural frictions (three main forces)
- Skills mismatch and sectoral shortages amid a structural shift toward higher value-added activities.
  - Skills mismatch remains above most European peers; skills mismatch indicator reported at "4.3" for Lithuania (2023) versus sample mean "2.6".
- Geographic mobility frictions linked to regional disparities, high homeownership rates, and a thin rental market.
  - Home ownership: "close to 90 percent".
- Social benefit design with high effective marginal tax rates for second earners that can weaken work incentives at the margin.
  - Net replacement rate: "37.5 percent" in 2005; "55.2 percent" in 2024.
  - Labor tax wedge: from "around 45 percent" in 2000 to "32 percent" in 2024.

### Quantitative assessment, panel regression, and counterfactuals
- Panel: "22 EU countries" over "2001-2023".
- Empirical model:
  - Unemployment_it = β X_it + γ Z_it + α_i + λ_t + ε_it with country and year fixed effects; heteroskedasticity-robust SE clustered at country level.
  - Robustness: 2SLS (lagged instruments), 2SLS with ALMP instrumented, Arellano-Bond, NAWRU replacement.
- Key regression associations (selected coefficients preserved verbatim from Table 1):
  - Lagged unemployment rate: 0.353** (0.122) in Column (1) OLS.
  - ALMP spending: -10.172** (4.220) in Column (1); other columns also show large negative point estimates (e.g., -10.122** (4.095) in Column (5)).
  - Skills mismatch: 0.263*** (0.058) in Column (1); robustly positive across specifications.
  - Minimum wage (minimum relative to median): -0.064* (0.032) in Column (1) and mixed across specifications.
  - Output gap: -0.440*** (0.143) in Column (1).
  - Real rate: 0.130* (0.069) in Column (1).
  - Capital accumulation: -1.217** (0.512) in Column (1).
  - Model diagnostics: R-squared values: (1) 0.832; (2) 0.815; (3) 0.830; (4) 0.836; (5) 0.576. Observations: (1) 459; (2) 459; (3) 381; (4) 433; (5) 459.
- Main empirical findings:
  - Higher skills mismatch and lower ALMP spending are positively associated with higher structural unemployment.
  - Higher labor tax wedge, higher replacement rate, and higher unionization are associated with higher unemployment in most specifications.
  - Minimum wage relative to median wage is not consistently associated with higher unemployment; some estimates show negative association.
  - Investment (capital accumulation) reduces unemployment; higher real interest rates increase unemployment.

### Counterfactual scenario and quantitative impact
- Counterfactual design:
  - Set skills mismatch, ALMP spending, average replacement rate, and average labor tax wedge to the 25th percentile of other European countries in the sample (ALMP spending set to 75th percentile).
- 2023 baseline and counterfactual selected values (preserved verbatim):
  - Lithuania:
    - Average labor tax wedge: 32.25
    - Average replacement rate: 55.0
    - ALMP spending / unemployment rate: 0.02
    - Skills mismatch: 4.3
  - Sample mean:
    - Average labor tax wedge: 35.85
    - Average replacement rate: 53.6
    - ALMP spending / unemployment rate: 0.10
    - Skills mismatch: 2.6
  - Counterfactual value (25th percentile; ALMP 75th percentile):
    - Average labor tax wedge: 32.54
    - Average replacement rate: 47.6
    - ALMP spending / unemployment rate: 0.13
    - Skills mismatch: 0.8
- Quantitative impact:
  - Under the counterfactual, structural unemployment would decline by more than 2 percentage points to around 4.6 percent.
  - Decline mainly driven by stronger ALMPs and reduced skills mismatch.

### Fiscal effects of reducing unemployment (counterfactual fiscal scenario)
- Counterfactual unemployment decline:
  - Labor force survey-based unemployment rate declines by 2.3 percentage points (counterfactual).
  - Assuming one-for-one translation to registered unemployment, registered unemployed would fall by around 34,000.
- Benefit and revenue assumptions (preserved verbatim):
  - Average net wage: EUR 1,475.
  - Average replacement rate: 55 percent (noted as a lower bound).
  - Implied unemployment benefits per beneficiary: about EUR 815 EUR per beneficiary.
  - Average gross wage: EUR 2,411.
  - Contributions (taxes and social contributions) per employed worker: about EUR 936 per month.
- Fiscal impact estimates:
  - Moving 34,000 unemployed persons into employment would improve the fiscal balance by around EUR 720 million per year.
  - Seasonal voluntary separations: registered unemployment exceeded its May-October trend by about 13,000 persons on average during June to September (2019 and 2022-2025).
    - Replacement rate during first three months of unemployment: 82 percent (baseline; will increase to 85 percent after reform in July 2026 for eligible employees).
    - Monthly benefits for a worker earning the average wage at 82 percent replacement: about EUR 1,204.
    - Monthly tax and social contribution revenues when employed: EUR 936.
    - Annual fiscal cost of seasonal voluntary separations could amount to roughly EUR 30 million.
  - Combined effect:
    - Reducing unemployment to the counterfactual value and tightening access to benefits for voluntary job separation could yield around EUR 750 million of fiscal savings or 0.9 percent of GDP annually.
- Caveats:
  - Initial higher spending on ALMPs would be required; medium-term ALMP spending would fall as unemployment declines. Social benefits spending would decrease given the lower replacement rate in the counterfactual.

### Policy priorities and recommendations (mutually reinforcing package)
- Scale up and better target ALMPs:
  - Increase ALMP spending and participation, especially for older workers and those outside the labor force.
  - Expand short, modular reskilling programs for mid-career and older jobseekers.
  - Improve access to labor-market-relevant training and education; modernize VET work-experience components and tertiary curricula toward in‑demand skills.
- Ease geographic mobility frictions:
  - Expand affordable and rental housing supply.
  - Improve transport connectivity and strengthen regional employment services and job‑matching.
- Recalibrate social benefits and work incentives while preserving adequacy:
  - Smooth benefit tapering to lower effective marginal tax rates.
  - Expand in‑work benefits and earned income tax credits.
  - Improve access to affordable childcare.
  - Tighten conditionality by linking benefits more closely to job search and participation in ALMPs.
  - Make unemployment benefits accessible only for involuntary labor contract termination (not in case of worker resignation).
  - Further decrease labor tax wedge by making employee social security contributions and/or personal income tax more progressive.
- Address digital and AI skill gaps:
  - Complement AI adoption with policies to upskill the workforce to mitigate potential exacerbation of skills mismatch and inequality.
- Other complementary reforms:
  - Deepen capital markets and ensure certainty in the corporate tax environment to foster private investment.

### Additional empirical and contextual findings (concise)
- ALMP spending in Lithuania: "around 0.2 percent of GDP" vs. OECD average "more than 0.4 percent".
- ALMP participation: "around 5-15 percent" of people outside the labor force and in unemployment.
- Minimum wage increase cited: 2012 increase "18 percent" (impact limited except for young workers in tradable sectors).
- Longer-run unemployment benchmarks:
  - Longer-run rate trended to "around 6 percent in 2025".
  - Stable-price and stable-wage rates have fluctuated "around 7-8 percent since 2018".
- Trade union density: below 10 percent since 2006.
- ALMP spending / unemployment rate for Lithuania (2023): 0.02; sample mean 0.10.
- Skills mismatch for Lithuania (2023): 4.3; counterfactual target 0.8.

*Source: STRUCTURAL UNEMPLOYMENT IN LITHUANIA — Republic of Lithuania / International Monetary Fund (July 9, 2026).*

### 1. Panel Regression _________________________________________________________________________ 14

### 1. Panel Regression

### Overview and main findings
- Lithuania’s unemployment rate has remained persistently high at around 7 percent since 2018 despite sustained growth and rising job vacancies.
- The (survey-based) unemployment rate fell from a peak of 18 percent in 2010 to around 6 percent in 2018, then broadly remained at this level except for the Covid-19 crisis in 2020.
- The registered unemployment rate fell to a low point of around 8 percent in 2016 and remained sticky around that level thereafter.
- Unemployment is concentrated among:
  - youth,
  - older workers,
  - the lower-educated,
  - residents outside major economic centers.
- Long-term unemployment remains sizable.

### Identified structural frictions (three main forces)
- Skills mismatch and sectoral shortages amid a structural shift toward higher value-added activities.
- Geographic mobility frictions linked to regional disparities, high homeownership rates, and a thin rental market.
- A social benefit design with high effective marginal tax rates for second earners that can weaken work incentives at the margin.

### Quantitative assessment and counterfactuals
- A cross-country panel exercise suggests these frictions materially raise equilibrium unemployment.
- In an implied counterfactual where Lithuania’s structural factors adjust closer to the most efficient levels across EU countries, unemployment could fall by more than 2 percentage points.

### Policy priorities and recommendations
- Scale up active labor market policies.
- Improve access to labor-market-relevant training and education.
- Ease housing and commuting constraints to reduce geographic mobility frictions.
- Better target social benefits to strengthen work incentives while preserving adequacy.

*Source: STRUCTURAL UNEMPLOYMENT IN LITHUANIA — Republic of Lithuania / International Monetary Fund (July 9, 2026).*

### 8.5 percent since 2018 with a similar, albeit much larger spike in 2020.

### 1ltuea2026002 - 8.5 percent since 2018 with a similar, albeit much larger spike in 2020.

### Overview and research objective
- Paper examines characteristics of unemployment in Lithuania and underlying factors that have prevented a further decline in unemployment despite economic expansion.
- Structure: (i) descriptive overview of the labor market and structure of unemployment; (ii) discussion of potential structural drivers: skills mismatch and shortages; insufficient active labor market policies (ALMPs); mobility frictions; benefits and work incentives; (iii) panel estimates for 22 EU countries (2001–2023) relating unemployment to structural and cyclical factors and a counterfactual analysis assessing potential unemployment reductions and fiscal savings if Lithuania converged to better-performing EU peers.

### Recent unemployment patterns and labor market dynamics
- Unemployment has averaged 8.5 percent since 2018 with a similar, albeit much larger spike in 2020.
- Survey-based unemployment rate: share of unemployed actively seeking work from household survey (~0.5 percent of population aged 15+ every quarter).
- Registered unemployment rate: share of persons registered with Lithuanian Employment Services (LES) in working age population.
- Since the 2010s:
  - Vacancies have been on an upward trend since 2010.
  - Growth remained strong in 2018/19 and after the Covid-19 crisis.
  - Unemployment fluctuated sideways, decoupling from vacancies and growth — indicating a large structural component with negative effects on potential growth.
- Labor force and employment:
  - In the 2010s, broadly flat labor force and continuous employment increases until 2018 led to falling unemployment.
  - In 2022–23, both employment and the labor force increased driven by strong positive net migration, mainly from Ukraine; migrants were quickly integrated and did not raise unemployed counts.
- Vacancies and matching:
  - Posted vacancies continued rising and peaked in 2025 while unemployment fluctuated around 7 percent without clear relationship to vacancies.
  - Continuous increases in vacancies concentrated across sectors: transport, public administration, health, professional, scientific and technical activities.
  - Some sectors (e.g., transport) face difficulties finding additional labor despite offering larger wage increases.
  - Regional variation: regions with high unemployment show lower vacancy rates.
- Wage–unemployment relationship:
  - Historically negative correlation between wage growth and unemployment; since 2018 this relationship has been muted.
  - High positive real wage growth in 2018–2021 and 2023–2025 did not further reduce unemployment.
  - Covid-19 (2020) and terms-of-trade shock related to Russia’s war in Ukraine (2022–23) drove down labor productivity, contributing to slight rises in unemployment.

### Structure of unemployment (demographics, duration, regional)
- Concentration by group:
  - Young workers face significantly higher unemployment risk — ongoing school-to-work transition challenges.
  - Older workers account for a relatively large share of unemployed compared with EU average — difficulties re-entering employment.
  - Lower educational attainment strongly associated with unemployment — persistent education and skill mismatches.
- Long-term unemployment remains substantial, indicating entrenched joblessness for vulnerable groups.
- Unemployment more prevalent outside main economic centers — regional disparities and limited geographic mobility evident.

### Structural drivers of unemployment
- Determinants identified in literature that can drive structural unemployment (cited references retained in source):
  - Unemployment benefit generosity and duration.
  - Labor taxes and labor tax wedge.
  - Trade union power.
  - Employment protection legislation.
  - Product market regulation.
  - Active labor market policies (ALMPs).
  - Minimum wages.
  - Housing policies.
  - Additional non-structural factors: real interest rate, investment, TFP growth.
- Lithuania-specific findings and evidence:
  - Prime-age men relatively less employed and more likely to fall into unemployment than women; older workers less likely to find employment relative to EU peers.
  - Wage growth outpacing productivity since 2013 related to increased wage bargaining power, shrinking labor force, several minimum wage increases, and an increase in unemployment benefits in 2017.
  - The 2012 minimum wage increase (18 percent) had no negative effects on workers’ employment retention prospects except for young workers in tradable sectors.
- Skills mismatch and shortages:
  - High skills mismatch has fallen but remains above most European peers.
  - Rapid structural change (higher value‑added manufacturing, ICT, business services) increased demand for technical, digital, managerial skills while parts of unemployed workforce need retraining and relocation.
- ALMPs:
  - Spending on ALMPs fluctuated around 0.2 percent of GDP, less than half the OECD average (>0.4 percent).
  - Participation in ALMPs around 5–15 percent of people outside the labor force and in unemployment — significantly below European peers.
  - Low participation linked to scheduling and cost barriers.
  - Lithuania introduced reforms: 2017 labor market reform creating Lithuanian Employment Service; 2021 digital jobseeker profiling tool; expansion of regional career centers; 2024 more restrictive eligibility for subsidized employment; national lifelong learning platform launched in 2024; ex-post ALMP evaluation launched in 2024.
  - Current ALMP provision insufficient in scale and targeting, especially for older workers and those outside the labor force.
- Mobility frictions:
  - Spatial mismatch: dynamic regions show high vacancy rates and low unemployment, lagging regions show the opposite.
  - Mobility constraints reinforced by high home ownership (~90 percent) and shallow rental market, reducing geographic mobility.
  - Low housing affordability in big cities and low-unemployment regions prevents jobseekers from responding to opportunities, especially low-income households.
- Benefits and work incentives:
  - Minimum income benefits increased over time and are now comparatively generous relative to median disposable income—improved social protection but may weaken work incentives marginally, especially for second earners.
  - High effective marginal tax rates from benefit withdrawal, childcare and transport costs reduce net gain from work.
  - Current rules allow unemployment benefits in case of worker resignation; paper recommends changing this (see policy suggestions).
- AI and digitalization risks:
  - Lithuania has made progress in digitalization and AI preparedness but population has relatively low digital skills.
  - AI could be complementary for many jobs but without policy action could exacerbate skills mismatches and inequality via job displacement in certain occupations.

### Benchmarks and natural rates
- Three unemployment benchmark measures estimated:
  1. Average longer-run unemployment rate.
  2. Longer-run potential minimum rate.
  3. Shorter-to-medium run non-inflationary/stable-price rate (akin to NAWRU).
- Findings:
  - Longer-run rate trended down to a low point of around 6 percent in 2025 and coincides with longer-run potential minimum rate.
  - Stable-price and stable-wage rates have fluctuated around 7–8 percent since 2018.
  - Current unemployment is close to current potential minimum and non-inflationary rate — benchmarks based solely on historical unemployment, inflation, and wage dynamics and not explicit structural modeling.

### Counterfactual analysis and panel estimation
- Empirical approach:
  - Panel of 22 EU countries, annual data 2001–2023.
  - Unemployment related to: ALMPs, labor tax wedge, unemployment benefits, minimum wage, trade union density, skills mismatch, and non-structural/cyclical factors (output gap, TFP and terms of trade growth, capital accumulation, real interest rate).
- Lithuania in cross-country perspective (structural factor levels and trends):
  - Trade union density: below 10 percent since 2006 — one of the lowest in Europe.
  - Minimum wage as a percentage of median wage: fluctuated between 40 and 55 percent — a few percentage points below average.
  - Labor tax wedge: fell from around 45 percent in 2000 (maximum across countries) to 32 percent in 2024 (average across countries).
  - Net replacement rate: 37.5 percent in 2005 (among lowest in EU) increased to 55.2 percent in 2024 (slightly above EU average).
  - ALMP spending: around half of EU peers and low over time with Covid period as exception.

### Policy recommendations (derived from analysis)
- Improve matching and worker adaptability:
  - Scale up active labor market policies (ALMPs) in both size and targeting, especially for older workers and those outside the labor force.
  - Modernize and enhance VET work-experience components, training and tertiary curricula toward in‑demand skills.
  - Expand short, modular reskilling programs for mid‑career workers and older jobseekers.
- Strengthen mobility and reduce spatial frictions:
  - Expand affordable and rental housing supply.
  - Improve transport connectivity.
  - Strengthen regional employment services and job‑matching.
- Reform benefits and work incentives while preserving adequacy:
  - Smooth benefit tapering to lower effective marginal tax rates.
  - Expand in‑work benefits and earned income tax credits.
  - Improve access to affordable childcare.
  - Tighten conditionality by linking benefits more closely to job search and participation in ALMPs.
  - Make unemployment benefits accessible only for involuntary labor contract termination (not in case of worker resignation).
  - Further decrease labor tax wedge by making employee social security contributions and/or personal income tax more progressive.
  - These measures would strengthen work incentives and support fiscal consolidation.
- Address digital and AI skill gaps:
  - Complement AI adoption with policies to upskill the workforce to mitigate potential exacerbation of skills mismatch and inequality.

### Key statistics and datapoints (preserved verbatim)
- Unemployment: "8.5 percent since 2018".
- Unemployment fluctuated around "7 percent" after 2022 while vacancies peaked in "2025".
- Labor force survey interviews approximately "0.5 percent" of the population aged 15+ every quarter.
- ALMP spending: "around 0.2 percent of GDP" in Lithuania vs. OECD average "more than 0.4 percent".
- ALMP participation: "around 5-15 percent" of people outside the labor force and in unemployment.
- Home ownership: "close to 90 percent".
- Minimum wage increases cited: 2012 increase "18 percent".
- Labor tax wedge: from "around 45 percent" in 2000 to "32 percent" in 2024.
- Net replacement rate: "37.5 percent" in 2005; "55.2 percent" in 2024.
- Longer-run unemployment rate trended to "around 6 percent in 2025".
- Stable-price and stable-wage rates fluctuated "around 7-8 percent since 2018".
- Panel: "22 EU countries" over "2001-2023".
- AI and digital skill indicators and indexes referenced as reported in source figures (no additional numeric summarization beyond the source text).

*Source: IMF staff chapter (1ltuea2026002) — analysis of Lithuania’s unemployment, structural drivers, and policy implications.*

### Box 1. Panel Regression

### Box 1. Panel Regression

### Methodology
- Estimated model:
  - Unemployment_it = β X_it + γ Z_it + α_i + λ_t + ε_it
  - Where unemployment rate (or NAWRU) for country i at time t is related to structural explanatory variables X_it, controls Z_it, country fixed effects α_i, and year fixed effects λ_t. ε_it are regression residuals.
- Baseline specification uses heteroskedasticity-robust standard errors clustered at the country level.
- Robustness exercises to mitigate reverse causality:
  - Two-stage least squares (2SLS) instrumenting structural variables with their one-year lagged values (following Orlandi (2012)).
  - 2SLS with ALMP instrumented by the lagged first difference of the residual from regressing ALMP on current and lagged values of the output gap (as in Bassanini and Duval (2006)).
  - Arellano-Bond (AB) estimator including instrumented lagged unemployment rates.
  - Replacement of unemployment rate with NAWRU in specification.
- Additional cyclical controls include growth rates of total factor productivity and terms of trade.

### Expected effects (theory)
- Higher labor tax wedge, higher minimum wage relative to the median wage, larger replacement rate, and larger unionization are usually expected to raise unemployment by increasing workers' bargaining position, raising wages, and increasing labor market frictions. However, some studies find opposite effects where rigidities stabilize expectations and promote productivity, technology growth, and demand.
- Higher ALMP spending is expected to lower unemployment via reskilling/upskilling and increasing matching efficiency.
- Higher skills mismatch should increase unemployment as vacancies and unemployed do not match.
- Larger output gap and stronger TFP growth are expected to lower unemployment.
- Stronger terms of trade (via lower import prices) should lower wage demand and thus lower unemployment.
- A higher real rate would lower investment and thus increase unemployment.
- Stronger capital accumulation would increase the marginal product of labor and thus lower unemployment.

### Key regression results (selected coefficients from Table 1)
- Lagged unemployment rate:
  - Column (1) OLS: 0.353** (0.122)
- Labor tax wedge:
  - (1) 0.043 (0.059)
  - (2) 0.008 (0.075)
  - (3) -0.014 (0.095)
  - (4) -0.041 (0.037)
  - (5) 0.085 (0.085)
- Minimum wage (minimum relative to median):
  - (1) -0.064* (0.032)
  - (2) -0.061* (0.037)
  - (3) -0.050* (0.030)
  - (4) 0.001 (0.008)
  - (5) -0.054** (0.022)
- Replacement rate:
  - (1) 0.035 (0.031)
  - (2) 0.041 (0.031)
  - (3) 0.029 (0.022)
  - (4) -0.016 (0.021)
  - (5) 0.033 (0.033)
- ALMP spending:
  - (1) -10.172** (4.220)
  - (2) -10.190 (7.713)
  - (3) -5.287 (3.628)
  - (4) -10.865 (7.567)
  - (5) -10.122** (4.095)
- Trade union density:
  - (1) 0.021 (0.058)
  - (2) 0.048 (0.067)
  - (3) 0.090 (0.093)
  - (4) 0.009 (0.011)
  - (5) 0.013 (0.062)
- Skills mismatch:
  - (1) 0.263*** (0.058)
  - (2) 0.274*** (0.065)
  - (3) 0.302*** (0.064)
  - (4) 0.215*** (0.045)
  - (5) 0.140** (0.061)
- Output gap:
  - (1) -0.440*** (0.143)
  - (2) -0.435*** (0.145)
  - (3) -0.396*** (0.146)
  - (4) -0.235*** (0.044)
  - (5) 0.104** (0.044)
- Real rate:
  - (1) 0.130* (0.069)
  - (2) 0.099 (0.064)
  - (3) 0.112* (0.059)
  - (4) 0.152*** (0.034)
  - (5) 0.088* (0.049)
- Capital accumulation:
  - (1) -1.217** (0.512)
  - (2) -1.152** (0.522)
  - (3) -0.753 (0.471)
  - (4) -0.339 (0.287)
  - (5) -0.942* (0.467)
- Model diagnostics:
  - Country fixed effects: Yes in all columns.
  - Time fixed effects: Yes in (1), (2), (3), (5); No in (4).
  - Additional cyclical controls: Yes in all columns.
  - R-squared:
    - (1) 0.832
    - (2) 0.815
    - (3) 0.830
    - (4) 0.836
    - (5) 0.576
  - Observations:
    - (1) 459
    - (2) 459
    - (3) 381
    - (4) 433
    - (5) 459
- Significance notation:
  - * p < 0.10, ** p < 0.05, *** p < 0.01.
- Notes:
  - 2SLS instruments structural variables with their lagged values; (3) additionally instruments ALMP spending with the residual from regressing ALMP spending on contemporaneous and three lags of the output gap. AB denotes Arellano-Bond.

### Main empirical findings
- Higher skills mismatch and lower ALMP spending are positively associated with higher structural unemployment.
- A higher labor tax wedge, a higher replacement rate, and higher unionization are associated with higher unemployment in most specifications.
- The minimum wage relative to median wage is not consistently associated with higher unemployment; some estimates show negative association (e.g., -0.064*).
- Higher ALMP spending is associated with lower unemployment (e.g., -10.172** in column (1)).
- Investment is an important determinant: higher capital accumulation is associated with lower unemployment; higher real interest rates are positively associated with unemployment.

### Counterfactual scenario: convergence to peer percentiles
- Counterfactual design:
  - Set skills mismatch, ALMP spending, average replacement rate, and average labor tax wedge to the 25th percentile of other European countries in the sample (ALMP spending set to 75th percentile).
- Table: 2023 values (selected)
  - Lithuania:
    - Average labor tax wedge: 32.25
    - Average replacement rate: 55.0
    - ALMP spending / unemployment rate: 0.02
    - Skills mismatch: 4.3
  - Sample mean:
    - Average labor tax wedge: 35.85
    - Average replacement rate: 53.6
    - ALMP spending / unemployment rate: 0.10
    - Skills mismatch: 2.6
  - Counterfactual value (25th percentile; ALMP 75th percentile):
    - Average labor tax wedge: 32.54
    - Average replacement rate: 47.6
    - ALMP spending / unemployment rate: 0.13
    - Skills mismatch: 0.8
- Quantitative impact:
  - Under this counterfactual, structural unemployment would decline by more than 2 percentage points to around 4.6 percent.
  - The decline is mainly driven by stronger ALMPs and reduced skills mismatch.

### Fiscal effects of reducing unemployment (Box 2 summary)
- Counterfactual unemployment decline:
  - Labor force survey-based unemployment rate declines by 2.3 percentage points (counterfactual).
  - Assuming one-for-one translation to registered unemployment, registered unemployed would fall by around 34,000.
- Benefit and revenue assumptions:
  - Average net wage: EUR 1,475.
  - Average replacement rate: 55 percent (noted as a lower bound).
  - Implied unemployment benefits per beneficiary: about EUR 815 EUR per beneficiary.
  - Average gross wage: EUR 2,411.
  - Contributions (taxes and social contributions) per employed worker: about EUR 936 per month.
- Fiscal impact:
  - Moving 34,000 unemployed persons into employment would improve the fiscal balance by around EUR 720 million per year.
  - Seasonal voluntary separations: registered unemployment exceeded its May-October trend by about 13,000 persons on average during June to September (2019 and 2022-2025).
    - Replacement rate during first three months of unemployment: 82 percent (baseline; will increase to 85 percent after reform in July 2026 for eligible employees).
    - Monthly benefits for a worker earning the average wage at 82 percent replacement: about EUR 1,204.
    - Monthly tax and social contribution revenues when employed: EUR 936.
    - Annual fiscal cost of seasonal voluntary separations could amount to roughly EUR 30 million.
  - Combined effect:
    - Reducing unemployment to the counterfactual value and tightening access to benefits for voluntary job separation could yield around EUR 750 million of fiscal savings or 0.9 percent of GDP annually.
- Caveats:
  - A decrease in unemployment to the counterfactual would imply higher spending on ALMPs initially (which would decrease in the medium term once unemployment falls) and a decrease in social benefits spending given the lower replacement rate in the counterfactual.

### Conclusion and policy implications
- Context:
  - Lithuania’s unemployment rate has remained sticky at around 7 percent since 2018 despite strong growth and rising vacancies, reflecting an upward shift in the Beveridge curve and growing structural frictions.
  - Unemployment is concentrated among youth, older workers, the lower-educated, and residents in rural areas; long-term unemployment remains sizable.
- Identified constraints:
  - Skills mismatch amid structural transformation.
  - Insufficient ALMP spending and targeting.
  - Limited geographic mobility of workers.
  - Social benefit designs that can weaken incentives at the margin.
- Policy priorities (mutually reinforcing package):
  - Scale up and better target ALMPs—especially training and job-matching for older and low-skilled workers.
  - Strengthen vocational and tertiary curricula and digital skills to ease mismatch and support AI-enabled productivity gains.
  - Lower mobility frictions through expanded affordable and rental housing and improved transport connectivity.
  - Recalibrate social benefits to preserve adequacy while reducing effective marginal tax rates and strengthening activation and conditionality.
  - Deepen capital markets and ensure certainty in the corporate tax environment to foster private investment.
- Expected outcome:
  - Implemented coherently, these measures would improve matching efficiency, broaden labor supply where shortages are most acute, support inclusive growth, and help contain fiscal pressures.

*International Monetary Fund — Box 1. Panel Regression (content unit 1ltuea2026002).*

### Appendix I.Table 1.Lithuania:Data Sources(Concluded)

### Appendix I.Table 1.Lithuania:Data Sources(Concluded)

### Data sources and coverage (selected variables)
- Home ownership rate: 2005-2024 for LTU — Eurostat.
- Real interest rate: Short-term real interest rate (GDP deflator based) — AMECO.
- Output gap: Output gap 1995-2025 — AMECO.
- Total factor productivity: Total factor productivity growth 1995-2025 — AMECO.
- Terms of trade: Export prices/import prices growth 1995-2025 — AMECO.
- Capital accumulation: Gross fixed capital accumulation/net capital stock 1995-2025 — AMECO.

### Role and importance of SMEs
- SMEs account for over 99 percent of firms in Lithuania.
- SMEs represent:
  - 74 percent of employment,
  - 62 percent of value added,
  - 63 percent of turnover,
  - 53 percent of employee wages and salaries (sources: 2024 OECD data for most series; 2023 data for value added at factor costs and wages and salaries).
- SMEs are defined as non-financial enterprises with less than 250 employees.

### Key SME challenges (survey evidence and patterns)
- Primary concerns reported by Lithuanian SMEs include: competition; availability of staff and managers; production costs; access to finance (EC and ECB, SAFE, 2025).
- Long-term barriers to investment for micro and small enterprises include: uncertainty about the future; availability of skilled workers; product demand; business regulation; availability of finance; energy costs (European Investment Bank, Investment Survey, 2025).
- Lithuanian SMEs report competition more frequently as a problem than SMEs across the EU on average.
- Skills shortages cited as a major barrier to adoption of digital and AI technologies (e.g., data scientists, AI engineers, ICT specialists).
- SMEs face greater challenges adopting digital and AI technologies than large enterprises; large firms have advantages in attracting experts, internal data, governance, and legal resources.

### Access to bank financing (structure and constraints)
- Lithuania's financial system is bank-dominated and highly concentrated, with lending dominated by foreign-owned banks.
- SME debt financing gap estimate: could be around EUR 1 bn in Lithuania (Raab, 2025) — estimate for 2023.
- Volume of corporate bank lending is subdued despite a sound and profitable banking sector.
- Outstanding SME loans were at less than 10 percent of GDP in 2024.
- Cost of borrowing and lending margins:
  - Cost of borrowing (In percent): Lithuania compared to Euro Area (figure-based).
  - MFIs lending margins on loans to NFCs (In percentage points): Lithuania among highest in the Baltic comparisons (figure-based).
- With a large share of corporate lending provided at variable interest rates, borrowing costs adjust rapidly to monetary policy changes, increasing interest‑rate sensitivity for firms.
- Specialized banks (introduced 2017) collect deposits and provide lending under narrower scope; several focus on SMEs via working‑capital, investment, and asset‑based lending.

### Collateral, rejection rates, and confidence
- About 12 percent of respondents in SAFE survey (2025) identified access to financing as their most significant issue in Lithuania (Greece at 14 percent; EU average 7 percent).
- Insufficient collateral affected 27 percent of those surveyed in Lithuania versus EU average 9 percent.
- More than 90 percent of SMEs required collateral in 2024 (OECD, 2026b).
- Lithuania has one of the highest shares of loans secured by real estate collateral in Europe.
- Confidence in ability to secure bank financing: 35 percent in Lithuania versus 56 percent EU average.
- EIB Investment Survey (2025) indicates elevated share of firms that are finance constrained (figure-based).
- SAFE survey (2025) reported a considerable percentage (31 percent) of respondents with pending applications; the share of applicants who received the full amount requested was below the EU average.
- Reported bank loan rejection rates: Lithuania above many other countries (figure-based).

### Use of alternative and non-bank financing
- Lithuanian firms rely more on trade credit and internal funds than on bank loans, relative to EU peers.
- Compared to the EU average, Lithuanian companies use less: bank loans, leasing agreements, hire-purchase agreements, grants, subsidized loans, and equity capital.
- NFC balance sheets show higher reliance on trade credits and advances compared to the Euro Area.
- NFC balance sheet composition (contributions to total liabilities and growth of outstanding liabilities, 2019–2025) shows:
  - Larger role of trade credits in Lithuania than in the Euro Area,
  - Substantial increase in trade credits in recent years (figure-based).

### Capital markets and regional initiatives
- Lithuanian capital markets are shallow:
  - Lithuanian stock exchange market capitalization is 6.7 percent of GDP with 32 companies listed (figure cited in text).
  - Comparison: France market capitalization 130 percent of GDP with 795 listed companies (figure cited in text).
- Baltic Capital Market Initiative has improved integration outcomes and supported EU-wide CMU, but:
  - Small scale of combined Baltic market limits liquidity and depth,
  - Continued policy efforts needed to secure gains in liquidity, investor depth, and risk capital availability.

### Policy implications and reform directions (high-level)
- Need for a holistic reform agenda to deepen SME financing by:
  - Mobilizing long-term savings,
  - Expanding non-bank financing channels,
  - Strengthening EU capital market integration.
- Complementary policies to ease labor and skills constraints, including:
  - Workforce upskilling,
  - Active labor market policies.

*International Monetary Fund — Republic of Lithuania, "Financing Constraints and Growth Challenges of Lithuanian SMEs" (Appendix I.Table 1.Lithuania: Data Sources (Concluded))*

### Box 1. The Baltic Regional Capital Market Initiative

### Box 1. The Baltic Regional Capital Market Initiative

### Overview and objectives
- Launched in 2017 when the Ministries of Finance of Estonia, Latvia, and Lithuania signed a Memorandum of Understanding to jointly develop a single pan‑Baltic capital market, with support from the European Commission (EC) and the European Bank for Reconstruction and Development (EBRD) (Mirbabaeva, 2023).
- Core objectives:
  - Achieve scale and visibility through regional integration.
  - Reduce reliance on bank lending.
  - Improve access to financing, particularly for SMEs and mid‑caps.
  - Align regional market development with the EU’s Capital Markets Union (CMU) agenda (Ministry of Finance of the Republic of Lithuania, 2025).

### Market integration progress and infrastructure
- Pre‑2017 integration elements included the Baltic List, harmonization of trading rules and market practices, a common trading system, and a single membership framework.
- Post‑2017 advances:
  - National exchanges unified under Nasdaq Baltic with harmonized listing standards and a single trading platform.
  - Establishment of a single regional central securities depository (Nasdaq CSD).
  - Full connectivity to TARGET2‑Securities via Clearstream in 2024, significantly reducing cross‑border settlement costs (Nasdaq, 2024).
  - Introduction of the MSCI Baltic Index in 2023 (Mirbabaeva, 2023).
- Result: increased issuance and improved market visibility; steps seen as aligning with CMU objectives.

### Disclosure harmonization for smaller public securities offerings
- December 2023: Bank of Lithuania, together with Latvian and Estonian authorities, agreed on a common information document for public securities offerings that do not require a prospectus.
- Since 2024: issuers can use a single English‑language document for offerings across one or more Baltic countries, replacing separate national submissions and reducing administrative burdens and issuance costs (Bank of Lithuania, 2023).
- Initial coverage: offerings of EUR 1–8 million over a 12‑month period aimed at facilitating capital market access for SMEs and mid‑sized firms.
- 2026 change: Lithuania increased the threshold for non‑prospectus public offerings from EUR 8 million to EUR 12 million while maintaining alignment with the common Baltic disclosure framework (Bank of Lithuania, 2026d).

### Remaining challenges and policy focus
- Persisting issues:
  - Thin secondary market liquidity.
  - Small equity markets relative to GDP.
  - Limited participation by domestic institutional investors.
  - Modest SME use of equity markets due to structural constraints (firm size, ownership structures, disclosure capacity).
- Policy priorities going forward:
  - Strengthen market and post‑trade infrastructure to attract more issuers to Baltic trading venues.
  - Broaden institutional investor participation.
  - Expand the range of available financial instruments to deepen markets and increase liquidity (Ministry of Finance of the Republic of Lithuania, 2025; Skrutkowski, 2025).
- Assessment: the initiative reduced regional fragmentation and facilitated CMU progress but has not yet produced deep, self‑sustaining capital markets; further policy effort is needed to translate integration into durable gains in liquidity, investor depth, and risk capital availability.

### Market‑based financing patterns and statistics
- Corporate bonds:
  - Non‑financial corporate bond issuance represented 2.6 percent of GDP at end‑2025.
  - Euro Area average for non‑financial corporate bond issuance: 11.8 percent (comparison in text chart).
  - Corporate bond issuances on First North increased in 2025, driven by supply‑ and demand‑enhancing factors (Box 2).
- Crowdfunding (predominantly loan‑based, concentrated in real estate):
  - In 2025, 16 crowdfunding service providers were operational in Lithuania.
  - Providers supplied about EUR 379 million (0.5 percent of GDP) in financing in 2025.
  - Lithuania ranked among the top five European countries in terms of capital raised and had the largest number of projects in 2024 (European Securities and Markets Authority, 2025).
  - Crowdfunding remains small relative to GDP and plays a complementary rather than systemic role in SME financing.
- Venture capital (VC):
  - VC has been expanding amid a growing start‑up ecosystem but remains modest in overall SME financing.
  - VC adoption is mostly focused on innovative tech firms; the insufficient scale and depth of Lithuania’s domestic VC market constrain SME scale‑up financing and broader capital deepening.
  - Studies find the EU’s underdeveloped VC market is an important factor holding back productivity and innovation (Brandao‑Marques et al., 2026).
- Public and EU support for SME finance:
  - National promotional bank ILTE provides loan guarantees, factoring, leasing, export credit guarantees, and preferential loans, including via alternative financing and venture‑capital‑linked schemes.
  - ILTE guarantees do not always materially reduce collateral requirements, as substantial additional collateral is often still required (Baliukonis and Buteikis, 2024).
  - Municipalities provide start‑up grants and interest subsidies; targeted direct lending was introduced for firms affected by Russia’s war against Ukraine (OECD, 2026).
  - InvestEU (implemented mainly by the European Investment Fund) provides EU budget guarantees supporting lending through local banks and alternative finance providers, enabling lower collateral requirements, longer maturities, and better pricing for SMEs, microenterprises, and small mid‑caps—particularly for projects related to innovation, digitalisation, sustainability, and microfinance (European Investment Fund, 2023).

*Source: Box 1. The Baltic Regional Capital Market Initiative, from the supplied IMF content unit.*

### Box 2. Lithuania’s First North Market

### Box 2. Lithuania’s First North Market

### Nasdaq Vilnius and First North: structure and purpose
- Nasdaq Vilnius is Lithuania’s primary stock exchange and the nation’s sole regulated securities market; together with Nasdaq Riga and Nasdaq Tallinn it forms the Nasdaq Baltic market infrastructure.
- Issuers can access:
  - the regulated market (licensed and overseen by the Bank of Lithuania), and
  - First North, an alternative market operating as a multilateral trading facility (MTF) under MiFID II and in place since 2009.
- First North offers a streamlined admission process relative to the regulated market and is governed by its own rulebook.
- Firms that join First North must engage a Certified Adviser for ongoing compliance, as set out in the Lithuanian First North rulebook.
- First North is positioned as a growth market for smaller fast‑growing companies, providing public listing benefits with simpler, lighter regulatory requirements and serving as a steppingstone to the regulated market.

### Market activity and bond issuance patterns
- Lithuania emerged as the most active Baltic First North bond market in terms of number of issuances in 2025.
- First North corporate bonds, on average, feature:
  - higher coupons,
  - shorter maturities, and
  - smaller issue sizes
  than corporate bonds issued on the regulated market.
- Sectoral composition of First North corporate issues: real estate leads, followed by energy.
- First North functions as a meaningful, though still niche, complement to bank finance for SMEs and mid‑caps; number of issuances and total amounts remain small by international standards.

### Drivers that activated Lithuania’s bond market on First North
- Tightening of bank credit after decades of heavy reliance on bank financing increased the need for alternative funding options such as bonds.
- Increased engagement of domestic banks as arrangers lowered transaction costs for issuers.
- Lighter disclosure requirements and reduced issuance expenses at First North versus the regulated market made the platform more attractive to SMEs and repeat issuers.
- Efforts in 2024 to expand the local Certified Adviser base strengthened market infrastructure.
- Rapidly growing retail investor base improved absorption capacity for smaller bond issues.
- Policy initiatives broadened investor access:
  - widening First North’s foreign securities list to include bonds, and
  - admitting 18 Lithuanian government Eurobonds listed in Luxembourg in 2025.

### Constraints limiting scale and broader SME access to market finance
- Fundamental constraint: scale. Lithuania’s small economy lacks the critical mass for liquid markets and a sufficiently diversified investor base for corporate securities.
- Supply‑side constraints:
  - Retail investor participation in capital markets remains low; household wealth is predominantly allocated to housing and deposits rather than financial instruments.
  - Lithuanian households hold a larger share of their wealth in housing than the EA average and both total and financial assets of households—relative to GDP—are significantly smaller than in the euro area.
  - A small domestic institutional investor base restricts available capital; the asset base of non‑bank financial institutions remains relatively small.
  - Pillar II pension withdrawals in the first quarter of 2026 reduced assets from about EUR 11 bn to EUR 7 bn (Bank of Lithuania, 2026c).
- Demand‑side constraints:
  - Weak pipeline of investable projects due to policy gaps, limited R&D and tertiary education investment, and cross‑border frictions.
  - SME sector dominated by micro enterprises: micro enterprises with fewer than 10 employees account for about 95.8 percent of all firms (data for 2024).
  - High business churn and limited firm longevity reduce demand for long‑term finance.
  - Fewer small firms operate in innovation‑intensive or high‑growth segments; small firms tend to be less innovative than the EU average.
  - Many SMEs are regionally focused and non‑scalable; a significant share of SME activity lies in non‑tradable sectors such as retail, construction and real estate, and local services (share by sector, 2024).
  - Limited financial reporting sophistication and financial literacy among SMEs can deter engagement with complex financing instruments and capital markets.

### Implications and potential role of First North
- First North could play a larger role as a complementary financing venue for Lithuanian SMEs and mid‑caps, particularly for bond issuance and repeated market access, but scale and liquidity remain limiting factors.
- Structural features interact to create a self‑reinforcing equilibrium: small issuer base → low secondary‑market liquidity → higher trading costs and risk premia → reduced institutional investor interest → limited issuance and market depth.

### Policy implications and recommendations (excerpted)
- Mobilize domestic institutional investors to deepen capital markets:
  - Pension system reform to ensure long‑term adequacy and sustainability, encourage voluntary participation, and reduce incentives for early withdrawals.
  - Improved financial education for retail investors and greater trust in investment advice to support a shift from deposits toward diversified capital‑market investments.
- Deepen Lithuania’s domestic non‑bank financing market:
  - Expand venture capital, private equity, securitization, and SME bond markets to reduce heavy reliance on bank lending.
- Harmonize EU Capital Markets:
  - Greater EU Single Market integration can provide scale, diversify the investor base, strengthen risk sharing, reduce transaction costs, and enhance liquidity.
  - The Savings and Investment Union and the Baltic regional capital market harmonization initiative can support market development.
- Leverage public instruments:
  - Use programs such as InvestEU and EIF guarantees to attract private investors, reduce risk, and develop local equity and debt markets accessible to smaller firms.
- Scale up ILTE lending while preserving financial sustainability:
  - ILTE should act as a catalytic public development institution, crowding in private finance via guarantees and co‑lending, avoiding direct competition with banks, and targeting well‑identified market failures.
  - Strengthen insolvency framework and improve use and calibration of ILTE guarantees to ease collateral constraints and better support constrained but viable firms.
- Strengthen financial reporting and financial literacy of SMEs:
  - More standardized and transparent financial reporting and better understanding of financing options and disclosure requirements would broaden the pool of SMEs able to engage with banks and capital markets.

_International Monetary Fund — Box 2. Lithuania’s First North Market_

### References

### References

### Bank of Lithuania and domestic publications
- Baliukonis, Lukas, and Andrius Buteikis. 2024. “State Guarantees for Small and Medium-Sized Business Loans.” 2024 m. Nr. 51: Bank of Lithuania.
- Bank of Lithuania. 2021. “Research on Financing Opportunities for Small and Medium-Sized Businesses.”
- Bank of Lithuania. 2023. “The Decisions Taken by the Bank of Lithuania Are an Important Step towards the Integration of the Capital Markets of the Baltic States,” December 15, 2023. https://www.lb.lt/lt/naujienos/lietuvos-banko-priimti-sprendimai-svarbus-zingsnis-baltijos-saliu-kapitalo-rinku-integracijos-link?__cf_chl_f_tk=PJ7vTeLwBN1kFFdA9slGCFy.PT9ClfJwbnydJpS7AuI-1782994904-1.0.1.1-VS5SqVuGt7F00r4EMDAclVT5.ULA0JItNKSBsqbpxP4.
- Bank of Lithuania. 2024. “Business Survey Overview.”
- Bank of Lithuania. 2025a. “Business Survey Overview.”
- Bank of Lithuania. 2025b. “Financial Stability Review 2025.”
- Bank of Lithuania. 2026a. “Capital Market Review 2025.”
- Bank of Lithuania. 2026b. “Review of the Activities of Crowdfunding Service Providers 2025.”
- Bank of Lithuania. 2026c. “Financial Stability Review.”
- Bank of Lithuania. 2026d. “Resolutions of the Board of the Bank of Lithuania,” June 9, 2026. https://www.lb.lt/lt/naujienos/lietuvos-banko-valdybos-nutarimai-249.
- Bielskis, Karolis. 2023. “Household Wealth and Finances in Lithuania.” No. 33/2023: Bank of Lithuania.

### IMF and regional economic analysis
- Brandao-Marques, Luis, Damien Capelle, Diego Cerdeiro, Adriano Fernandes, Alexandra Fotiou, Yueling Huang, Claire Yi Li, et al. 2026. “Deeper and More  Integrated Financial  Markets to Foster Growth  and Resilience in Europe.” IMF, Staff Discussion Note.
- Nina Budina, Oyun Erdene Adilbish, Diego A. Cerdeiro, Romain A Duval, Balázs Égert, Dmitriy Kovtun, Anh Thi Ngoc Nguyen, Augustus J Panton, and Michelle Tejada. "Europe’s National-Level Structural Reform Priorities", IMF Working Papers 2025, 104 (2025), accessed 6/15/2026, https://doi.org/10.5089/9798229011877.001
- IMF. 2025. “Regional Economic Outlook -- Europe. Note 2: Making European Reforms a Success on the Ground .” November 2025. https://www.imf.org/en/publications/reo/eu/issues/2025/10/17/regional-economic-outlook-europe-october-2025.

### European Commission, EU institutions, and EU-wide studies
- European Commission. 2025a. “2025 Country Report - Lithuania.”
- European Commission. 2025b. “2025 SME Country Fact Sheet: Lithuania.”
- European Commission. 2025c. “Annual Report on European SMEs 2024/2025.”
- European Commission. 2026. “Survey on the Access to Finance of Enterprises (SAFE): Analytical Report 2025.”
- European Investment Bank. 2025. “EIB Investment Survey 2024 Country Overview: Lithuania.”
- European Investment Fund. 2023. “InvestEU: EIF and Lithuania’s SME Finance to Deliver €40 Million in New Financing for Businesses in the Baltics and Beyond.” Eif.org. 2023. https://www.eif.org/press/all/investeu-eif-and-lithuanias-sme-finance-to-deliver-40-million-in-new-financing-for-businesses-in-the-baltics-and-beyond.
- European Securities and Markets Authority. 2025. “ESMA Market Report: Crowdfunding in the EU 2025.”

### Baltic and regional capital market initiatives and commentary
- Mirbabaeva, Nigina. 2023. “Pan-Baltic Capital Market to Benefit from Single Index Classification.” Ebrd.com. 2023. https://www.ebrd.com/home/news-and-events/news/2023/panbaltic-capital-market-to-benefit-from-single-index-classification.html#.
- Reiserer, Axel. 2017. “Baltic States to Create a Pan-Baltic Capital Market.” Ebrd.com. 2017. https://www.ebrd.com/home/news-and-events/news/2017/baltic-states-to-create-a-panbaltic-capital-market.html.
- Nasdaq. 2024. “Nasdaq CSD Partners with Clearstream to Improve European Market Access for Baltic Investors.” Nasdaq CSD. September 10, 2024. https://nasdaqcsd.com/news/nasdaq-csd-partners-with-clearstream-to-improve-european-market-access-for-baltic-investors/.
- Skrutkowski, Mathias. 2025. “Capital Markets in the Baltic Region: A Deep Dive.” ESM Briefs 20
- Kaskarelis, Loukas, Arndt-Gerrit Kund, Mathias Skrutkowski, and Juan Solé. 2025. “Capital Markets Union Redux: Towards a Deeper and More Accessible Savings and Investments Union.” ESM Discussion Paper Series 25. https://doi.org/10.2852/8417817.
- Ministry of Finance of the Republic of Lithuania. 2025. “Ministers of Finance of the Baltic States Addressed the EBRD on Strengthening the Regional Capital Market,” July. https://finmin.lrv.lt/en/news/ministers-of-finance-of-the-baltic-states-addressed-the-ebrd-on-strengthening-the-regional-capital-market-rKZJ/.
- Mirbabaeva, Nigina. 2023. “Pan-Baltic Capital Market to Benefit from Single Index Classification.” Ebrd.com. 2023. https://www.ebrd.com/home/news-and-events/news/2023/panbaltic-capital-market-to-benefit-from-single-index-classification.html#.
- Raab, Georg. 2025. “Debt Financing Gap for SMEs in Europe.” European Commission.: Working Paper 45.
- KPMG. 2024. “Baltic Private Equity and Venture Capital Market Overview.”

### OECD, ESMA, EIB, EIF and other multilateral references
- OECD. 2025a. “OECD Financing SMEs and Entrepreneurs Scoreboard. 2025 Highlights.” OECD.
- OECD. 2025b. “OECD Economic Surveys: Lithuania 2025.” OECD Publishing, Paris, 2025 (volume 2025/6). https://doi.org/10.1787/4abf1ea5-en.
- OECD. 2026a. “Financing SMEs and Entrepreneurs 2026: An OECD Scoreboard.” OECD Publishing, Paris, https://doi.org/10.1787/075d8058-en.
- OECD. 2026b. “OECD Financing SMEs and Entrepreneurs Scoreboard 2026. Policy Highlights.”
- European Securities and Markets Authority. 2025. “ESMA Market Report: Crowdfunding in the EU 2025.”
- European Investment Bank. 2025. “EIB Investment Survey 2024 Country Overview: Lithuania.”
- European Investment Fund. 2023. “InvestEU: EIF and Lithuania’s SME Finance to Deliver €40 Million in New Financing for Businesses in the Baltics and Beyond.” Eif.org. 2023. https://www.eif.org/press/all/investeu-eif-and-lithuanias-sme-finance-to-deliver-40-million-in-new-financing-for-businesses-in-the-baltics-and-beyond.

*1ltuea2026002 - References*

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_Source: https://www.imf.org/-/media/files/publications/cr/2026/english/1ltuea2026002.pdf_