## CREDIT GROWTH AND ECONOMIC RECOVERY IN EUROPE: THE CASE OF SLOVENIA

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### Introduction
- Key observations:
  - Economic activity in Europe has yet to fully recover nearly eight years after the global financial crisis (GFC).
  - Bank credit expansion remains tepid despite historically low lending interest rates.
- Study highlights:
  - Emerging Europe’s post-GFC recovery is slower than what one can expect after a crisis.
  - Slovenia’s post-GFC recovery path is generally explained by the global nature of the 2008 financial crisis and Slovenia’s 2012–13 banking crisis.
  - Loan quality, customer deposits, and bank capital, as well as the macroeconomic environment, are key factors influencing bank credit developments in Europe and Slovenia.
  - Bank credit to the private sector has a positive, but modest impact on economic activity in European countries, working mainly through the investment channel.
- Slovenia-specific historical points:
  - Output contracted nearly 8 percent in 2009.
  - A two-year recession in 2012–13 with a cumulative output loss of nearly 4 percent.
  - A transfer of EUR 4.9 billion in commercial bank loans to a government-sponsored bad bank (BAMC) in 2013–14.

### Post GFC‑Recovery Assessment (method and comparative evidence)
- Methodology:
  - Local projection (LP) method drawing on Jordà, Schularick, and Taylor (2013).
  - Dependent variables: cumulative change in real GDP per capita, real private-sector consumption per capita, real investment (GFCF) per capita, and real bank credit to the private sector per capita from the beginning of each recession and recovery period.
  - Dummy variables N (non-financial) and F (financial) indicate whether the episode was preceded by a banking crisis.
  - Controls include excess credit accumulated during expansion and standardized percentage changes in dependent variables two years and one year before recession start.
- Estimation design:
  - 79 recession and recovery episodes across 35 advanced and large emerging-market countries covering 1971–2006.
  - Projection horizon of 7 years (consistent with 2009–2015 post-GFC period).
  - 28 separate regressions (7 regressions for each dependent variable).
  - Episodes: 64 non-financial and 15 financial recessions; 20 European recession/recovery periods included.
- Counterfactual for weak global demand:
  - External demand regressor rescaled to reflect post-GFC external demand faced by European countries to produce adjusted projection paths.

### Slovenia’s Performance Relative to Projection Paths
- Starting in 2009:
  - Slovenia’s cumulative change in real GDP per capita from 2008 to 2015 is significantly below the projection path for typical recessions and recoveries following a financial crisis.
  - Slovenia’s real private consumption per capita, real gross fixed capital formation, and real bank credit to the private sector outperform comparator groups until at least horizon year three.
  - Real investment and real bank credit are within the 95 percent confidence interval of the projected path.
- Starting in 2012 and controlling for weak external demand:
  - Slovenia’s performance is more typical.
  - Real investment since the start of the 2012 recession outperforms the projected path for real investment following a financial crisis.
  - Real GDP outperforms in the outer projection horizon years.
  - Real consumption remains broadly within the confidence interval for typical financial-crisis recoveries.
  - Main outlier: real bank credit in the early projection years, partially explained by the EUR 4.9 billion transfer to BAMC in 2013–14.
  - By end-2015 bank credit had not begun to pick up as the projection path suggests it should.

### Determinants of Credit Growth (cross-country bank-level evidence)
- Data and estimation:
  - Bank-level panel covering 37 European countries (including Slovenia) and nearly 8,000 banks.
  - Annual data 1999–2015 from Fitch-Connect for bank-specific data and WEO for macro data.
  - Credit growth variable: growth of gross loans extended by individual banks to borrowers of a specific country.
  - Estimation: system generalized method of moments (GMM); J-test/Sargan validity tests applied.
- Key bank-specific determinants:
  - Loan quality (NPL ratio) — largest relative impact on credit growth in both pre- and post-GFC periods.
  - Customer deposits, bank capital, and bank equity prices — significant determinants.
- Key macro/demand determinants:
  - GDP growth and inflation influence bank credit extension.

### Credit-growth pattern and episodic drivers in Slovenia
- Co-movement:
  - Average annual growth in bank credit in Slovenia broadly co-moves with fitted values from the panel regression, but is more volatile.
- Pre-GFC expansion:
  - Rapid expansion likely reflects nominal convergence before joining the euro area and mergers-and-acquisition activity.
- 2013 contraction:
  - Sharp drop primarily due to transfers of bad bank assets to the BAMC.
- Post-GFC regression interpretation:
  - Bank-specific factors and macro conditions offer partial explanations for Slovenia’s credit developments.

### Bank capital, customer deposits, and NPLs — Slovenia-specific diagnostics
- Bank capital:
  - Cross-country analysis: regulatory capital as percent of total assets is significant with a negative sign, especially during recessions—raising regulatory capital associated with less credit expansion.
  - Slovenia: average regulatory capital trended consistently upward since 2012 with a big jump at end-2013 attributable to recapitalization of three state-owned banks; continued rise likely dampened credit growth.
- Customer deposits:
  - Cross-country coefficient: positive overall, but turns negative during recessions.
  - Slovenia: deposit growth rates slid before the 2012 recession and 2013 banking crisis (turning negative before the bailout), then accelerated sharply after the crisis.
  - Before the crisis, domestic bank credit growth correlated positively with deposit growth; after the crisis, credit growth contracted while customer deposits grew (consistent with negative coefficient in recessions).
- Non-Performing Loans (NPLs):
  - Cross-country: NPL ratio coefficient significant and negative—higher NPLs trigger more conservative credit expansion.
  - Slovenia: NPL ratio rose from 2008 until late-2013 banking crisis.
    - Bank credit growth decelerated in early 2010, turned negative in mid-2011, and continued to contract thereafter.
    - Post-crisis divergence: falling NPLs but continued weak credit extension.
      - Explanations:
        - Aggregate NPL ratio remains relatively elevated at 5.1 percent despite significant reduction over the last two years.
        - Slovenian corporates deleveraging since the crisis, holding back demand for domestic bank loans despite improving bank balance sheets.

### Credit–growth link and macro effects (panel GMM evidence)
- Relationship estimates:
  - In an “average” European country, a 10 percent increase in domestic bank credit to the private sector would raise real GDP by 0.6–0.7 percent.
  - Main transmission: gross fixed capital investment — a credit growth of 10 percentage points raises private GFCF formation by some 1½–2 percent.
  - The credit impulse (change in credit growth) influences GDP growth strongly and significantly during post-GFC recessions and recoveries; during a recession (and for CESEE during recovery) the credit impulse coefficient is significant and larger than the coefficient on credit growth.
- Selected regression coefficients (panel GMM; full sample):
  - Table 1 (GDP growth and private sector credit growth):
    - GDP growth rate (t-1): 0.219**
    - Private sector credit growth: 0.0711***
    - Private sector credit-to-GDP ratio: -0.0129*
    - External demand: 0.253**
    - Log (VIX): -0.00388
  - Table 2 (GFCF growth and private sector credit growth):
    - GFCF growth rate (t-1): 0.168*
    - Private sector credit growth: 0.200**
    - Private sector credit-to-GDP ratio: -0.126**
    - Interest rate (policy rate): -1.783***
    - External demand: 0.725**
  - Table 3 (GDP growth and credit impulse):
    - GDP growth rate (t-1): 0.300***
    - Change in private sector credit growth: -0.0218
    - Change in private sector credit growth * Dummy recession: 0.0888**
    - Change in private sector credit growth * Dummy recovery: 0.0739**

### Findings and Conclusions (credit and growth)
- Recovery characterization:
  - From 2009 assessment: Slovenia’s post-GFC recovery significantly lags typical post-recession recoveries for both normal and financial-crisis-driven recessions.
  - Accounting for the 2012–13 “double dip” recession and global growth slowdown shows Slovenia’s recovery is not atypical.
- Credit dynamics drivers:
  - Bank-specific factors (NPLs, customer deposits, capital, equity prices) and macro conditions (GDP growth, inflation) are key determinants of bank lending.
- Credit–growth linkage:
  - Bank credit to the private sector influences economic activity modestly, mainly through the investment channel.
- Slovenia-specific weak credit driver:
  - The need to strengthen bank and corporate balance sheets after the GFC likely contributed significantly to weaker investment and GDP growth during Slovenia’s post-GFC recovery.

### Policy recommendations (credit and banking)
- Asset quality:
  - NPLs can be a major drag on credit growth; authorities and banks have reduced NPLs, particularly for large non-financial corporations.
  - SMEs account for a significant portion of the remaining NPL stock; a concerted effort applying recently published guidelines on reducing SME NPLs and aggressive supervision of implementation is appropriate.
- Diagnosis and patience:
  - Proper diagnosis of economic and financial developments is crucial. Slovenia’s performance, benchmarked to the 2012–13 recession, aligns with other financial-crisis countries, reflecting the time needed to work off unsustainable debt burdens.
  - Important to be patient and allow balance sheet repair to take its course before encouraging banks to increase lending.
- Structural adjustment:
  - The sale of state-owned enterprises could add equity financing to the economy, easing the need for the corporate sector to rely on deleveraging to restore financial health.

---

### The performance of Slovenia’s exports after the crisis

### Export performance overview
- Slovenia’s strong exports offset a decline in domestic demand, leaving overall GDP growth relatively flat (2008–16).
- From 2007 to 2016, Slovenia’s current account improved dramatically; improvement mainly due to higher exports of goods and, to a lesser extent, services like tourism.
- Cross-country perspective: Slovenia’s exports performed better than most other countries in the region over 2007–16.

### Structure and concentration of goods exports
- Top export categories (HS 2-digit, 2015): vehicles, electrical machinery, general machinery, pharmaceuticals, and fuel.
- Stability:
  - Top 5 export categories made up 50 percent of total exports in both 2007 and 2015.
  - Top 10 export categories made up 80 percent of total exports in both 2007 and 2015.
- Market concentration:
  - Slovenia’s exports are relatively concentrated compared with other European countries and have become more concentrated over time.
- By trading partner:
  - Top 5 markets: Germany, Italy, Austria, Croatia and France; these accounted for just over 50% of total exports in 2007 and 2015.
  - Top 10 export markets accounted for about 70% of total exports in both years.

### Changes and drivers of export growth (2007–2015)
- Product-level shifts:
  - The 5 HS 2-digit categories with the greatest increases (fuel, medicines, electrical machinery, plastic products, and one more) accounted for 74 percent of the total increase.
  - Furniture and machinery saw significant decreases.
  - Even largest categories comprised only 6 percent of total exports in 2007, suggesting production bottlenecks were not a constraint.
- Market-level shifts:
  - The 5 countries with greatest increases from 2007 to 2015: Germany, Austria, Switzerland, Croatia, and Poland, accounting for 43 percent of the total increase.
  - France, Ukraine, and Italy saw the largest decreases.
- Decomposition (2007–2015):
  - Increases in existing goods to existing markets represented 95% of the increase.
    - Of that 95%: top 10 export categories contributed 40% of the increase; all other goods contributed 55%.
  - Only 5% of the increase was due to exports to new markets or of new products to existing markets.
  - Slovenia already exported in 1,140 of 1,241 (or 92%) of the HS 4-digit product categories.

### Structure effects and comparative advantage
- Export structure as a drag:
  - Between 2007 and 2015, the top 10 exports grew more slowly than overall exports, falling from 70 to 67 percent of total exports.
  - Growth-orientation analysis shows Slovenia’s exports were concentrated in partners and product categories that grew slower than average during 2007–15.
- Revealed Comparative Advantage (RCA):
  - Pharmaceuticals show comparative advantage; most of the top 10 exports are not in the RCA top list.
  - Breakdown by primary labor type among the top RCA products: 4 low-skilled, 4 medium-skilled white-collar, one medium-skilled blue-collar (wood products), and one high-skilled (pharmaceuticals).
- Skill and factor composition:
  - By value shares (2002–15): low-skilled, blue-collar, and white-collar labor categories each about 30%; high-skilled exports rose from 14 to 20 percent of total exports from 2002 to 2015.
  - Compared with France and Germany, Slovenia’s exports are relatively intensive in natural resources and labor and less intensive in medium- and high-technology goods; pattern similar to other eastern European countries.

### Drivers of export increase and prospects
- Main drivers (2007–15):
  - Strong increase in partners’ imports relative to GDP and increases in Slovenia’s market share in Europe.
  - Eight of the top 10 export categories (all except furniture and vehicles) saw increases in their market shares in the EU.
  - Slovenia’s market share of its top 10 exports in the EU increased by 7 percent between 2007 and 2015.
- Partner import growth:
  - From 2007 to 2015, average growth rate of Slovenia’s trading partners’ nominal imports exceeded Slovenia’s nominal GDP growth: 2.5 vs. 1.0 percent.
  - Slovenia’s export growth averaged 3.0 percent, exceeding partner import growth due to increased market share.
- Prospects (2016–22):
  - IMF projections for Slovenia’s trading partners suggest robust export growth is expected to continue for 2016–2022.
  - Market orientation of exports appears better in 2016 than in 2007; IMF projects imports of Slovenia’s trading partners will grow even faster from 2016 to 2022.
  - Goods exports are expected to continue supporting economic activity in Slovenia.

### Conclusions on exports
- Export improvement after the GFC mainly due to:
  - Strong growth of partner imports relative to GDP.
  - Increase in market shares of many Slovenian exports in the EU—stemming from improved competitiveness and deeper integration in regional supply chains.
- Despite strong export performance, export structure was a drag on export growth 2007–2016 because exports were concentrated in slower-growing product categories and partners.
- Exports of high-skilled products rose quickly but plateaued after 2009.
- Overall prospects are positive with projected partner import growth for 2016–2022 supporting continued export-driven contribution.

---

### Labor market outcomes and the 2013 reform

### Crisis context and labor market duality
- Crisis effects:
  - Labor market outcomes deteriorated sharply during 2009–13; rigidities impeded adjustment and reallocation.
  - Minimum wage major increase in 2010 raised the minimum wage from 41 to 51½ of the average wage, the highest ratio in the EU; increase phased in gradually.
  - Youth temporary contracts comprised almost ¾ of youth employment by early 2013.
  - Student work comprises 15–20 percent of Slovenia’s employment in the 15–29 age group.
- Duality:
  - Segmentation between core open-ended employees (heavy protection) and short-term temporary contracts intensified.
  - Pervasive duality carries welfare costs: excessive employment fluctuations and potential long-term human capital losses, particularly for the young.

### 2013 Labor Market Reform — design
- Objective: limit duality by increasing employers’ flexibility for open-ended contracts and strengthening protections for temporary employees.
- Open-ended contract changes:
  - Simplified dismissal procedures: allowing temporary layoffs and cash compensation instead of reintegration; shorter dismissal notice periods.
  - Reduction of firing costs: shorter notice periods (from 120 to 60–80 days) and lower severance payments.
  - Social contribution exemptions (mostly temporary): 2-year exemption from unemployment insurance contributions for new open-ended hires; exemptions on parts of employer pension and health contributions for hiring younger or older workers; 2-year exemption for hiring young, previously unemployed persons.
- Temporary contract changes:
  - Limits on chain contracts: renewals allowed up to two years for same work, after which open-ended contract must be offered or employee dismissed.
  - Increase in some social contributions and severance pay for temporary hires.
  - Student work made subject to social contributions effective early 2015.
- Assessment of scope:
  - Reduction in employment protection for individual dismissals was substantial: relevant index fell slightly below OECD average though still exceeded some transition economies.
  - Regulations for temporary contracts tightened and remain somewhat more restrictive than OECD average.
  - Collective dismissal regime unchanged and remains relatively restrictive.

### Reform impact — aggregate and contract-level evidence
- Aggregate outcomes:
  - Employment growth and unemployment reduction post-2013 fell short of EU average despite Slovenia’s stronger post-2014 output recovery relative to the EU.
- Contract composition:
  - Immediately after reform, new open-ended contracts dominated new temporary contracts; especially pronounced for youth in first 12 post-reform months.
  - Near-zero post-reform total new youth contracts suggest unintended side effects.
  - The initial shift was largely transitory; shares of new open-ended vs temporary contracts reverted toward pre-reform patterns.
  - Share of temporary employment in total employment edged down in 2014 but resumed upward trend and reached an all-time high by 2016.
- Employment-growth elasticities:
  - Calculated average employment-output elasticities do not support that growth became more “employment-friendly” post-reform; latest sub-period elasticity slightly lower than pre-crisis expansion.
  - Crisis-era layoffs gave way to more intensive labor utilization rather than significant new hires during recovery.

### Matching and Beveridge curve evidence
- Beveridge curve:
  - Crisis associated with major outward shift from early 2009 through mid-2013.
  - By end-2016 the Beveridge curve was at its end-2011 position almost 4 years after the reform and 3 years into recovery—matching has not meaningfully improved.
  - Slovenia’s post-reform Beveridge curve is relatively steep: sizeable vacancy increases yield moderate unemployment declines.
- Robustness (Labor Shortage Indicator, LSI):
  - LSI–unemployment relation noisier but shows some shift back since trough; nonetheless by end-2016 unemployment for given LSI was still some 2½ percent higher relative to pre-crisis period.

### Transition analysis (SILC-based) and reform effects
- Data: Eurostat SILC longitudinal sample covering 24 EU countries, 2003–16.
- Entrants drive cyclical unemployment:
  - Transition frequencies from employment to unemployment virtually identical between crisis and 2014–16 recovery; increases in cyclical unemployment are driven by entrants failing to find jobs.
- Reform impact on transitions:
  - Temporary → open-ended transition frequency jumps by almost 7 percentage points between pre- and post-reform; concentrated in the year after reform with a 13 percentage point jump, returning to pre-reform levels in 2015–16.
  - Patterns imply fiscal incentives induced transitory reallocation to open-ended contracts; inadequate employment protection loosening led employers to revert once incentives faded.
- Unintended substitution:
  - Shift from temporary employment to unemployment increased—Slovenia moved from middle to near top of sample distribution post-reform.
  - Transition from self-employment to open-ended employment falls by some 7 percentage points post-reform.
  - Transition from open-ended employment to self-employment almost triples—self-employment increasingly a fallback.
- Inactivity → Employment:
  - Persistently very low transition frequency from inactivity to employment—average under 5 percent across 2006–2016; apparently not impacted by the reform.
- Unemployment duration:
  - Transition frequencies from unemployment to unemployment have risen, translating into longer average unemployment duration.

### Model extensions, implications, and policy recommendations
- Model extensions:
  - Endogenize open-ended contracts by treating layoff costs as large but finite; incorporate payroll taxes on regular and temporary work to capture 2013 reform features.
- Main model implications:
  - B-L baseline: lower layoff costs for open-ended contracts increase hiring and dismissals with ambiguous net employment effect.
  - Extended model: lower layoff costs increase conversions of temporary jobs to regular jobs and narrow regular-temporary wage gaps—unambiguous reduction in duality.
  - Payroll tax reductions on regular contracts unambiguously reduce unemployment and segmentation; payroll tax increases on temporary contracts reduce segmentation but may increase unemployment.
  - Interaction: adverse unemployment impact of higher temporary-contract taxes is smaller and favorable duality impact larger when layoff costs on permanent contracts are lower.
- Policy recommendations:
  - Prioritize faster and deeper loosening of employment protection regulations (including collective dismissal rules) to sustain gains in reducing dualism and improve matching.
  - Rethink fiscal instruments: fiscal incentives favoring open-ended contracts are poor substitutes for employment protection liberalization and can produce distortions.
  - Improve matching and skills: intensify retraining programs and establish enterprise-based apprenticeship systems; support via derogations from minimum wage and targeted fiscal incentives during setup.
  - Improve transitions from inactivity to employment by lowering the marginal tax wedge at entry; consider general reductions in labor taxes concentrated at the low end of the wage distribution.
  - Address long-term unemployment with liberalization complemented by marginal tax wedge measures, derogations for certain groups, and targeted training.

*cr17126 - IMF staff report excerpts compiled in the content unit*

### References __________________________________________________________________________ 17

### CREDIT GROWTH AND ECONOMIC RECOVERY IN EUROPE: THE CASE OF SLOVENIA

### Introduction
- Economic activity in Europe has yet to fully recover nearly eight years after the global financial crisis (GFC).
- Bank credit expansion remains tepid despite historically low lending interest rates.
- The study uncovers:
  - Emerging Europe’s post-GFC recovery is slower than what one can expect after a crisis.
  - Slovenia’s post-GFC recovery path is generally explained by the global nature of the 2008 financial crisis and Slovenia’s 2012–13 banking crisis.
  - Loan quality, customer deposits, and bank capital, as well as the macroeconomic environment, are key factors influencing bank credit developments in Europe and Slovenia.
  - Bank credit to the private sector has a positive, but modest impact on economic activity in European countries, working mainly through the investment channel.
- Key historical points for Slovenia:
  - Output contracted nearly 8 percent in 2009.
  - A two-year recession in 2012–13 with a cumulative output loss of nearly 4 percent.
  - A transfer of EUR 4.9 billion in commercial bank loans to a government-sponsored bad bank (BAMC) in 2013–14.

### Post GFC‑Recovery Assessment (method and comparative evidence)
- Methodology:
  - Local projection (LP) method drawing on Jordà, Schularick, and Taylor (2013) to develop projections of ‘typical’ recession and recovery paths.
  - Dependent variables: cumulative change in real GDP per capita, real private-sector consumption per capita, real investment (GFCF) per capita, and real bank credit to the private sector per capita from the beginning of each recession and recovery period.
  - Dummy variables N (non-financial) and F (financial) indicate whether the episode was preceded by a banking crisis.
  - Control variables include measures of excess credit accumulated during the expansion and standardized percentage changes in the dependent variables two years and one year before the recession start.
- Estimation sample and design:
  - 79 recession and recovery episodes across 35 advanced and large emerging-market countries covering 1971–2006.
  - Projection horizon of 7 years (consistent with 2009–2015 post-GFC period).
  - 28 separate regressions run (7 regressions for each dependent variable).
  - Episodes: 64 classified as non-financial and 15 as financial recessions.
  - 20 recession and recovery periods in European countries included.
- Counterfactual adjustment for weak global demand after the GFC:
  - Included an external demand regressor, rescaled to reflect post-GFC external demand faced by European countries, and generated counterfactual dependent variables to produce adjusted projection paths.

### Slovenia’s Performance Relative to Projection Paths
- When starting assessment in 2009:
  - Slovenia’s cumulative change in real GDP per capita from 2008 to 2015 is significantly below the projection path for typical recessions and recoveries following a financial crisis.
  - Slovenia’s real private consumption per capita, real gross fixed capital formation, and real bank credit to the private sector outperform comparator groups until at least horizon year three.
  - Slovenia’s performance for real investment and real bank credit is within the 95 percent confidence interval of the projected path.
- When starting assessment in 2012 and controlling for weak external demand:
  - Slovenia’s performance is more typical.
  - Real investment since the start of the 2012 recession outperforms the projected path for real investment following a financial crisis.
  - Real GDP outperforms in the outer projection horizon years.
  - Real consumption remains broadly within the confidence interval for typical financial-crisis recoveries.
  - The main outlier remains real bank credit in the early projection years; partially explained by the EUR 4.9 billion transfer to BAMC in 2013–14.
  - By end-2015 bank credit had not begun to pick up as the projection path suggests it should.

### Determinants of Credit Growth (cross-country bank-level evidence)
- Data and estimation:
  - Bank-level panel analysis covering 37 European countries (including Slovenia) and nearly 8,000 banks.
  - Annual data over 1999–2015 from Fitch-Connect for bank-specific data and WEO for macro data.
  - Credit growth variable: growth of gross loans extended by individual banks to borrowers of a specific country.
  - Estimation method: system generalized method of moments (GMM) to address endogeneity and instrumentation; J-test/Sargan validity tests applied.
- Key bank-specific determinants identified:
  - Loan quality, proxied by the NPL ratio, has the largest relative impact on credit growth in both the pre- and post-GFC periods.
  - Other significant bank-specific factors: customer deposits, bank capital, and bank equity prices.
- Key macro/demand determinants identified:
  - GDP growth and inflation influence bank credit extension.

### Findings and Conclusions
- Recovery characterization:
  - The post-GFC recovery in Slovenia significantly lags typical post-recession recoveries for both normal and financial-crisis-driven recessions when assessed from 2009.
  - Controlling for Slovenia’s 2012–13 “double dip” recession and the slowdown in global growth after the GFC shows Slovenia’s recovery is not atypical.
- Credit dynamics drivers:
  - Bank-specific factors (loan quality/NPLs, customer deposits, capital, equity prices) and macro conditions (GDP growth, inflation) are key determinants of bank lending.
- Credit–growth link:
  - Bank credit to the private sector has a positive, but modest impact on economic activity in European countries, working mainly through the investment channel.
- Slovenia-specific driver of weak credit:
  - The need to strengthen bank and corporate balance sheets after the GFC likely contributed significantly to weaker investment and GDP growth during Slovenia’s post-GFC recovery.

*Prepared by John Ralyea with assistance from Luisa Calixto and Dustin Smith; paper draws on cross-country analysis performed by an EUR team including Sergei Antoshin, Marco Arena, Tonny Lybek, John Ralyea, and Etienne Yehoue under the supervision of Nikolay Gueorguiev.*

### 12.      Slovenia’s bank credit dynamics are broadly track the model but are more volatile.

### 12.      Slovenia’s bank credit dynamics are broadly track the model but are more volatile.

### Credit-growth pattern and main episodic drivers
- The average annual growth in bank credit in Slovenia broadly co-moves with the fitted values from the panel regression.
- Rapid expansion in the pre-GFC period likely reflects nominal convergence before joining the euro area and mergers-and-acquisition activity.
- The sharp drop in 2013 is primarily due to transfers of bad bank assets to the BAMC.
- Post-GFC, a closer look at regression results and bank-specific factors offers a partial explanation for Slovenia’s credit developments.

### Bank capital
- Bank regulatory capital as percent of total assets is significant in the cross-country analysis with a negative sign, especially during recessions, indicating that raising regulatory capital is associated with less credit expansion.
- Raising regulatory capital, owing to changes in regulatory standards or market requirements, either leads to less loanable funds or triggers deleveraging.
- Slovenian banks’ average regulatory capital has trended consistently upward since 2012, with a big jump at the end of 2013 attributable to the recapitalization of three state-owned banks during the bank crisis.
- After the capital injection, regulatory bank capital continued to increase; the steady rise likely had a dampening effect on bank credit growth in Slovenia throughout the post-GFC period.

### Customer deposits
- The coefficient on customer deposits is significant with an overall positive sign in the bank-level cross-country analysis, though it turns negative during recessionary periods.
- Interpretation:
  - Positive sign: strong saving mobilization facilitates credit expansion.
  - Negative sign in recessions: deposit growth may reflect increased saving over consumption and investment, constraining credit extension.
- In Slovenia:
  - Deposit growth rates slid in advance of the 2012 recession and 2013 banking crisis, turning negative on the eve of the government’s bank bailout, then accelerated sharply after the crisis.
  - Domestic bank credit growth was positively correlated with deposit growth rates prior to the recession/banking crisis (growth slowed as deposits slowed).
  - After the crisis, credit growth continued to contract as customer deposits grew (consistent with negative coefficient during recessions).

### Non-Performing Loans (NPLs)
- The coefficient on NPL ratio is significant with a negative sign in the cross-country analysis: increasing NPL ratio triggers more conservative credit expansion.
- Even if NPLs are fully provisioned, provisioning still has costs for credit expansion as resources are diverted from new lending.
- In Slovenia:
  - The NPL ratio rose from 2008 until the banking crisis in late-2013.
  - Bank credit growth began decelerating in early 2010, turned negative in mid-2011, and continued to contract thereafter — broadly consistent with negative correlation between NPL ratio and credit growth.
  - Post-crisis, there is a divergence: a positive correlation between falling NPLs and continued weak credit extension.
    - Explanations:
      - The aggregate NPL ratio of Slovenia remains relatively elevated at 5.1 percent despite significant reduction over the last two years.
      - Slovenian corporates have been deleveraging since the crisis, holding back demand for domestic bank loans despite improving bank balance sheets.

### GDP growth and credit growth relationship
- Some countries show no clear relationship between GDP growth and domestic bank credit growth after the GFC, echoing “creditless” recoveries; Slovenia exhibits positive GDP and private investment growth while bank credit continues to contract.
- Dynamic system Generalized-Method-of-Moments (Blundell and Bond, 1998) panel estimates for 39 European countries (1999–2015) indicate:
  - The relationship between bank credit growth and GDP growth has remained essentially unchanged during and after the GFC.
  - In an “average” European country, a 10 percent increase in domestic bank credit to the private sector would raise real GDP by 0.6–0.7 percent.
  - The main channel is gross fixed capital investment: a credit growth of 10 percentage points raises private GFCF formation by some 1½–2 percent.
  - The credit impulse (change in credit growth) influences GDP growth strongly and significantly during post-GFC recessions and recoveries; during a recession, and for the sample of CESEE countries during the recovery, the credit impulse coefficient is significant and larger than the coefficient on credit growth.

### Key regression coefficients (selected results from panel GMM)
- Table 1 (GDP growth and private sector credit growth; full sample):
  - GDP growth rate (t-1): 0.219**
  - Private sector credit growth: 0.0711***
  - Private sector credit-to-GDP ratio: -0.0129*
  - External demand: 0.253**
  - Log (VIX): -0.00388
- Table 2 (GFCF growth and private sector credit growth; full sample):
  - GFCF growth rate (t-1): 0.168*
  - Private sector credit growth: 0.200**
  - Private sector credit-to-GDP ratio: -0.126**
  - Interest rate (policy rate): -1.783***
  - External demand: 0.725**
- Table 3 (GDP growth and credit impulse; full sample):
  - GDP growth rate (t-1): 0.300***
  - Change in private sector credit growth: -0.0218
  - Change in private sector credit growth * Dummy recession: 0.0888**
  - Change in private sector credit growth * Dummy recovery: 0.0739**

### Concluding diagnostics
- Slovenia’s post-GFC experience is broadly in line with other countries after accounting for the severity of its post-GFC downturn and the 2012–13 banking crisis.
- Bank-specific factors — loan quality (NPLs), customer deposits, and capital — likely played a role in weak credit creation after the crisis.
- Bank credit to the private sector influences economic activity modestly, mainly through the investment channel.

### Policy recommendations
- Asset quality:
  - NPLs can be a major drag on credit growth; authorities and banks have reduced NPLs, particularly for large non-financial corporations.
  - SMEs account for a significant portion of the remaining NPL stock; a concerted effort applying recently published guidelines on reducing SME NPLs and aggressive supervision of implementation is appropriate.
- Diagnosis and patience:
  - Proper diagnosis of economic and financial developments is crucial. Slovenia’s performance, benchmarked to the 2012–13 recession, aligns with other financial-crisis countries, reflecting the time needed to work off unsustainable debt burdens.
  - It is important to be patient and allow balance sheet repair to take its course before encouraging banks to increase lending.
- Structural adjustment:
  - The sale of state-owned enterprises could add equity financing to the economy, easing the need for the corporate sector to rely on deleveraging to restore financial health.

*Source: cr17126 - 12.      Slovenia’s bank credit dynamics are broadly track the model but are more volatile.*

### 2.      The performance of Slovenia’s exports after the crisis was also strong relative to that

### 2.      The performance of Slovenia’s exports after the crisis was also strong relative to that

### Export performance and cross-country comparison
- Slovenia’s strong exports offset a decline in domestic demand, leaving overall GDP growth relatively flat (2008–16 period).
- A few countries (e.g., Croatia and Hungary) followed a similar pattern; in others (e.g., Austria and Poland) domestic demand was the main contributor to growth. The Slovak Republic is the only country in the sample in which both exports and domestic demand played a significant role.
- From 2007 to 2016, Slovenia’s current account improved dramatically; improvement mainly due to higher exports of goods and, to a lesser extent, services like tourism.
- Cross-country perspective: Slovenia’s exports performed better than most other countries in the region over 2007–16.

### Structure of goods exports (Section B)
- Top export categories (HS 2-digit, 2015): vehicles, electrical machinery, general machinery, pharmaceuticals, and fuel.
- Stability over time:
  - Top 10 export categories were relatively stable between 2007 and 2015, with only one new product (fuel) appearing.
  - Top 5 export categories made up 50 percent of total exports in both 2007 and 2015.
  - Top 10 export categories made up 80 percent of total exports in both 2007 and 2015.
- Market concentration:
  - Slovenia’s exports are relatively concentrated compared with other European countries and have become more concentrated over time (Figure 5).
- By trading partner:
  - Top 5 markets: Germany, Italy, Austria, Croatia and France; these accounted for just over 50% of total exports in 2007 and 2015.
  - Top 10 export markets accounted for about 70% of total exports in both years.
- Top 20 HS 4-digit categories (2015 vs 2007): list of prominent items includes Electric appliances, Aluminum bars & rods, Electromagnets, Wood boards, Other furniture, Construction equip parts, Electric motors, Pumps & compressors, Electric motor parts, Coated paper, Chairs & seats, Flat-rolled steel, Electric heaters, Rubber tires, Electrical energy, Electric parts for vehicles, Refined petroleum, Vehicle parts, Vehicles, Medicines.

### Largest changes in exports (Section 4)
- By product (HS 2-digit), the 5 categories with the greatest increases (fuel, medicines, electrical machinery, plastic products, and one more) accounted for 74 percent of the total increase.
- Simultaneously, furniture and machinery saw significant decreases.
- Even the largest categories comprised only 6 percent of total exports in 2007, suggesting production bottlenecks were not a constraint on export growth.
- By trading partner, the 5 countries with the greatest increases from 2007 to 2015 were Germany, Austria, Switzerland, Croatia, and Poland, accounting for 43 percent of the total increase. France, Ukraine, and Italy saw the largest decreases.

### Industry-level and partner-level divergences (Section 5)
- Significant within-industry changes:
  - Overall vehicle exports were largest share in 2007 and 2015, but not among top categories for changes; vehicle exports to Germany showed large increase while vehicle exports to France and Italy showed significant decreases.
  - Similar divergence occurred for aluminum exports.

### Decomposition of export growth (Section 6)
- Increases in existing goods to existing markets represented 95% of the increase in exports from 2007 to 2015.
  - Of that 95%: the top 10 export categories contributed 40% of the increase, while all other goods contributed 55%.
- Only 5% of the increase was due to exports to new markets or of new products to existing markets.
- Slovenia already exported in 1,140 of 1,241 (or 92%) of the HS 4-digit product categories, consistent with a mature, well-integrated economy.

### Effects of structure on performance (Section C)
- Despite good overall performance, Slovenia’s export structure was a drag on export growth:
  - Between 2007 and 2015, the top 10 exports grew more slowly than overall exports, falling from 70 to 67 percent of total exports.
  - Growth-orientation analysis shows Slovenia’s exports were concentrated in partners and product categories that grew slower than average during 2007–15 (negative slope in Figure 9).
- Implication: Slovenia’s exports would likely have done even better if the largest export categories had performed at the average level.

### Slovenia’s revealed comparative advantage and skill/factor composition (Section D)
- Revealed Comparative Advantage (RCA, Balassa 1965):
  - Pharmaceuticals show comparative advantage; however, most of the top 10 exports are not in the RCA top list.
  - Only three products in Figure 10 (wood articles, aluminum, pharmaceuticals) are in Slovenia’s top 10 export categories; only two (pharmaceuticals and wood articles) are among the largest contributors to export growth from 2007 to 2015.
  - Breakdown by primary labor type among the top RCA products: 4 of the top 10 are primarily produced with low-skilled labor, 4 with medium-skilled white-collar labor, one with medium-skilled blue-collar labor (wood products), and one (pharmaceuticals) with high-skilled labor.
- Peneder/Banerjee skill-level taxonomy:
  - Skill-level classification examples:
    - Low-skilled: metal manufactures, textiles, rubber.
    - Medium-skilled blue-collar: vehicles, furniture, miscellaneous manufactures.
    - Medium-skilled white-collar: electrical equipment, paper products, power generating equipment.
    - High-skilled: pharmaceuticals, specialized industrial equipment, metal working machinery.
  - By value shares (2002–15):
    - Exports classified as using low-skilled, blue-collar, or white-collar labor are each about 30% of Slovenia’s exports; these shares declined only modestly over time.
    - High-skilled exports rose from 14 to 20 percent of total exports from 2002 to 2015, with growth concentrated in specialized industrial equipment and pharmaceuticals.
    - Most of the increase in high-skilled exports occurred between 2002 and 2009; the share has been relatively constant since then.
  - GFC pattern:
    - Prior to the crisis, trend away from white-collar towards high-skilled and blue-collar.
    - Immediately after GFC, greatest decline concentrated in goods produced primarily by blue-collar workers; white-collar goods bounced back.
    - Changes since 2011 have been relatively modest.
  - International comparison:
    - Compared with Austria, France, Germany, and Italy, Slovenia exports greater shares of goods that embody white-collar and high-skilled labor and smaller shares that embody low-skilled and blue-collar labor.
    - Slovenia’s pattern is similar to other eastern European countries (Croatia, Hungary, Poland, Serbia).
- Factor intensity classification:
  - Natural resources intensive: wood, metals, rubber.
  - Labor intensive: textiles, apparel & footwear, food & beverages.
  - Medium- to high-technology: vehicles, electrical equipment, pharmaceuticals.
  - Shares by factor intensity were relatively constant over time.
  - Compared with France and Germany, Slovenia’s exports are relatively intensive in natural resources and labor and less intensive in medium- and high-technology goods; pattern similar to other eastern European countries.

### Explanations for positive export contribution and prospects (Section E)
- Drivers of export increase (2007–15):
  - Not due to favorable orientation of products or trading partners.
  - Largely due to strong increase in partners’ imports relative to GDP and increases in Slovenia’s market share in Europe.
  - Eight of the top 10 export categories (all except furniture and vehicles) saw increases in their market shares in the EU.
  - Market shares of a few categories (wood and plastic products, iron and steel, electrical machinery) rose by more than 20 percent in 2015 relative to 2007.
  - Slovenia’s exports declined to some markets (including France, Italy, Poland, & Serbia) but increases in market share in large markets (Austria, Germany, Croatia, and Russia) more than offset declines.
  - Overall, Slovenia’s market share of its top 10 exports in the EU increased by 7 percent between 2007 and 2015.
- Partner import growth:
  - From 2007 to 2015, the average growth rate of Slovenia’s trading partners’ nominal imports exceeded that of Slovenia’s nominal GDP: 2.5 vs. 1.0 percent.
  - Slovenia’s export growth averaged 3.0 percent, exceeding partner import growth due to increased market share.
- Prospects (2016–22):
  - IMF projections for Slovenia’s trading partners suggest robust export growth is expected to continue for 2016–2022.
  - As Europe’s growth recovers, Slovenia’s largest trading partners are expected to have improved growth prospects relative to past performance.
  - A continued rise in Slovenia’s market shares in partner imports would be favorable but is hard to predict.
  - The market orientation of exports appears better in 2016 than in 2007; IMF projects imports of Slovenia’s trading partners will grow even faster from 2016 to 2022.
  - As a result, goods exports are expected to continue supporting economic activity in Slovenia.

### Conclusions (Section F)
- The improvement in Slovenia’s goods exports after the GFC was mainly due to:
  - Strong growth of partner imports relative to GDP.
  - An increase in the market shares of many of Slovenia’s exports in the European Union—stemming from improved competitiveness and deeper integration in regional supply chains.
- Despite strong export performance, Slovenia’s export structure was a drag on export growth from 2007 to 2016 because exports were concentrated in product categories and trading partners that grew more slowly than average.
- Measures of revealed comparative advantage suggest competitiveness in medium-skilled white-collar products.
- Exports of products that use high-skilled labor (pharmaceuticals and specialized industrial equipment) rose quickly but plateaued after 2009.
- Overall prospects are positive: market orientation improved by 2016 and projected partner import growth for 2016–2022 supports continued export-driven contribution to Slovenia’s activity.

*Prepared by Lawrence Dwight. International Monetary Fund.*

### 1.      Amid deteriorating labor market outcomes as the crisis deepened, Slovenia embarked

### 1.      Amid deteriorating labor market outcomes as the crisis deepened, Slovenia embarked

### Context and Background
- Slovenia’s labor market outcomes deteriorated sharply with the onset of the 2009–13 crisis; underlying rigidities impeded smooth labor market adjustment and substantial labor reallocation.
- The major increase in the minimum wage introduced in 2010 brought the minimum wage from 41 to 51½ of the average wage, the highest ratio in the EU; the increase was phased in gradually.
- Relative to other EU countries, Slovenia experienced a sharper increase in unemployment and deeper reductions in employment during the crisis; the relative employment deterioration was particularly pronounced.
- Labor market segmentation intensified over the past decade between:
  - a “core” of employees on open-ended contracts with heavy employment protection;
  - a segment dominated by short-term, temporary contracts used as a margin of flexibility.
- Youth (15–24) temporary contracts had been steadily increasing pre-crisis and, by the trough in early 2013, temporary contracts made up almost ¾ of youth employment, making Slovenia an outlier within the EU.
- Student work comprises 15–20 percent of Slovenia’s employment in the 15–29 age group.
- The youth and the low-skilled experienced particularly significant deterioration in employment and unemployment rates relative to the overall Slovenian labor force and EU averages; the relative employment deterioration appears more substantial.
- Pervasive duality (high incidence of temporary employment) carries welfare costs: excessive employment fluctuations at business cycle frequencies and potential long-term human capital losses, particularly for the young.
- Early crisis policy measures included two intervention laws to preserve jobs (shortening working hours and allowing temporary layoffs) and expanded active labor market (ALM) policies; these measures covered almost 5 percent of the total active population but had limited and short-lived impact, and ALM participation remained low and declined during 2011–12 due to financing constraints.

### The 2013 Labor Market Reform — Design and Features
- Main objective: limit duality by increasing employers’ flexibility in handling open-ended contracts while strengthening legal protections for temporary employees.
- The reform targeted both open-ended and temporary segments, aiming to expand open-ended employment via lower employment protection and fiscal incentives, and to reduce some employer advantages of temporary contracts.

- Reforms to open-ended contracts:
  - Simplification of individual dismissal procedures: allowing temporary layoffs and compensation in cash instead of reintegration; shortening dismissal notice periods.
  - Reduction of firing costs: shorter notice periods (from 120 to 60–80 days) and lower severance payments.
  - Some (mostly temporary) exemptions from social contributions:
    - a 2-year exemption from contributions to unemployment insurance for new employees hired on open-ended contracts;
    - exemptions for part of the employer pension and health contributions for hiring younger or older workers on open-ended contracts;
    - a 2-year exemption from social contributions for hiring young, previously unemployed persons.

- Reforms to temporary contracts:
  - Limits on scope of repeated (“chain”) contracts: temporary contracts allowed to be renewed up to two years for the same work, after which an open-ended contract must be offered or the employee dismissed.
  - Increase in some social contributions and severance pay for employees hired on temporary contracts.
  - Student work made subject to social contributions effective from early 2015.

- Assessment of reform scope:
  - The reduction in employment protection for individual dismissals was substantial: the relevant index fell slightly below the OECD average, though it still exceeded some transition economies and some countries of comparable income levels.
  - Regulations concerning temporary contracts were tightened and remain somewhat more restrictive than the OECD average.
  - The regime regarding collective dismissals was not changed and remains relatively restrictive.

### Reform Impact — Macro Evidence and Dynamics
- Aggregate outcomes post-reform:
  - Employment growth and reduction in unemployment post-2013 fell short of the EU average despite Slovenia’s stronger post-2014 output recovery relative to the EU.
- Contract composition and duality:
  - Immediately after the reform, new open-ended contracts dominated over new temporary contracts; over the first 12 post-reform months, annual growth of new open-ended contracts substantially outpaced temporary contracts, especially for youth.
  - Near-zero post-reform total new youth contracts suggest unintended side effects.
  - The initial break with pre-reform trends was largely confined to the immediate post-reform window; the relative incidence of open-ended vs temporary contracts broadly reverted to pre-reform patterns in subsequent periods.
  - Year-on-year changes in new contracts may exaggerate medium-term impact due to base effects; analyzing shares of new contract types and stock shares provides a clearer picture:
    - The share of open-ended versus temporary new contracts in total new contracts confirms declining reform impact over time.
    - The share of temporary employment in total employment edged down in 2014 but resumed its upward trend subsequently, reaching an all-time high by 2016.

- Employment-growth relationship:
  - Low employment-growth elasticities and highly procyclical labor productivity can reflect distortions such as employment protection inducing hoarding during contractions and reliance on overtime during expansions.
  - Analysis distinguishes three sub-periods: crisis (2009–13); post-reform expansion (2014–16); pre-crisis expansion (2006-08).
  - Calculated average employment-output elasticities do not support the hypothesis that growth became more “employment-friendly” post-reform; the elasticity for the latest sub-period is slightly lower compared with the pre-crisis expansion.
  - The sharp drop in elasticity relative to the crisis period suggests that crisis-era extensive layoffs (largely via termination of temporary contracts) gave way to more intensive labor utilization rather than significant new hires during recovery.
  - Caveats: sectoral differences in labor intensities (e.g., construction vs export-oriented manufacturing) could partly explain differences, but a cursory look makes it unlikely these fully account for the observed elasticities.

- Matching job seekers and vacancies (Beveridge curve):
  - The 2009–13 crisis was associated with a major outward shift of the Beveridge curve starting in early 2009 and continuing through mid-2013, reflecting sharp deterioration in labor market matching amid asymmetric sectoral shocks (notably construction).
  - Despite increased flexibility from the reform, matching has not improved: by end-2016 the Beveridge curve was at its end-2011 position almost 4 years after the reform and 3 full years into the recovery.
  - Slovenia’s Beveridge curve appears relatively steep post-reform, implying an unfavorable vacancy-unemployment tradeoff: sizeable increases in vacancies yield only moderate declines in unemployment.
  - By contrast, several countries of comparable income levels and flexible labor markets have seen inward shifts of their Beveridge curves as they exited recessions.

### Key Findings and Implications
- The 2013 reform was comprehensive and targeted both open-ended and temporary contracts, with notable changes in dismissal procedures, firing costs, social contribution exemptions, limits on chain contracts, and changes to student work treatment.
- Some intended effects were observed in the immediate post-reform period (rise in new open-ended contracts), especially for youth, but these effects were largely transitory.
- Aggregate employment outcomes and employment-output elasticities do not provide clear evidence that growth became more employment-friendly post-reform.
- Labor market matching has not meaningfully improved post-reform; the Beveridge curve remains shifted outward relative to pre-crisis positions and is relatively steep.
- Remaining rigidities include restrictive rules on collective dismissals and tightened but still relatively restrictive temporary contract regulations; these may have limited the reform’s effectiveness in reducing duality and improving matching.
- Pervasive duality and concentration of temporary contracts among the young continue to pose welfare and long-term growth risks through possible sub-optimal human capital acquisition.

*Prepared by Ioannis Halikias.*

### 26.      Some robustness analysis suggests a more nuanced assessment of Slovenia’s labor

### 26.      Some robustness analysis suggests a more nuanced assessment of Slovenia’s labor market mismatches, but does not change the broad picture.

### Robustness check using Labor Shortage Indicator (LSI)
- Vacancy data for Slovenia were estimated (rather than directly reported) during the 2014–15 period; this motivates checking robustness with an alternative indicator that was consistently reported.
- The Labor Shortage Indicator (LSI) measures the share of employers who report labor shortages and is available consistently for Slovenia throughout the period.
- Caveats on LSI:
  - It is not a perfect substitute for a vacancy rate: it does not provide information on the level of vacancies.
  - It covers only the industry dimension.
- Empirical outcome using LSI as a proxy in an alternative Beveridge curve:
  - The relation between LSI and unemployment is much noisier relative to the standard Beveridge curve.
  - The alternative Beveridge curve shows signs of shifting back since the trough of the recession, conveying a somewhat more benign picture of matching.
  - Nonetheless, by end-2016, for given LSI, unemployment was still some 2½ percent higher relative to the pre-crisis period.
- Conclusion: the basic conclusions on substantial remaining mismatches are reasonably robust despite measurement caveats.

*Source: EUROSTAT (as cited in the text).*

### Structural contributors to Beveridge curve stickiness
- Possible drivers:
  - Insufficient liberalization—lack of progress toward loosening employment protection may make employers reluctant to hire from the unemployed pool, preferring to utilize existing workforce more intensively.
  - Structural skill mismatches—major shifts in the composition of the economy since the onset of the crisis may have generated mismatches not easily corrected by market forces alone.
- Suggestive evidence:
  - Changing composition of vacancies by sector and qualifications sought by employers supports the structural skill mismatch hypothesis.
- Policy implication from assessment:
  - Policies under the 2013 reform probably need to go further toward liberalization.
  - A different range of initiatives targeted at skill acquisition may need to be considered in parallel to achieve substantial and durable improvement in labor market matching.

### Evidence from labor market transitions (methodology and data)
- Motivation:
  - Flows between employment–unemployment–inactivity provide a more refined assessment of reform impact than stock measures alone.
  - Transition analysis captures adjustment mechanisms, policy impacts, and margins where distortions persist.
- Transition frequency definition:
  - T_{ij,t} = 100*(Flow from state j to state i in t)/(Population of state j in t-1), i, j ε {Employment, Unemployment, Inactivity}
  - In a stochastic setting, T_{ij,t} corresponds to probability π_{ij,t} of transitioning to state i in t conditional on being in state j in t-1.
- Data source:
  - Eurostat database drawing on longitudinal sample from the European Union’s Statistics on Income and Living Conditions (SILC) survey.
  - Database covers 24 EU countries over the 2003-16 period.
  - SILC decomposes employment into self-employment, open-ended employment, and temporary employment.

### Key transition findings and the 2013 reform impact
- Entrants drive cyclical unemployment:
  - Slovenia’s transition frequencies from employment to unemployment are virtually identical between the crisis and the 2014-16 recovery period, implying increases in cyclical unemployment are not dominated by layoffs but by inability of new entrants to find jobs.
- Reform impact on contract types:
  - Transition frequency from temporary to open-ended employment jumps by almost 7 percentage points between the pre- and post-reform periods.
  - The jump is concentrated in the year after the reform: a 13 percentage point jump in the transition frequency, with the frequency essentially returning to pre-reform levels during 2015–16.
  - Interpretation: fiscal incentives in the reform induced a transitory reallocation toward open-ended contracts, but inadequate loosening of employment protection led employers to revert to prior contract structures once incentives faded.
- Unintended substitution patterns:
  - Structural shift from temporary employment to unemployment: Slovenia moved from the middle of the sample distribution pre-reform to near the top post-reform; the relative constancy of transition frequencies implies a substantial structural deterioration adding to the NAIRU.
  - Transition from self-employment to open-ended employment falls by some 7 percentage points from pre- to post-reform, suggesting previously temporary workers replaced part of self-employed who would normally have been offered an open-ended job.
  - Transition from open-ended employment to self-employment almost triples, with Slovenia moving from the bottom quartile to almost the top of the distribution—self-employment increasingly a fallback for those who could not find open-ended jobs.
  - Together, these patterns suggest the fiscal elements of the reform (rather than looser employment protection) were the dominant influence on employer behavior.
- Specific numeric and temporal references:
  - Year windows referenced include 2009–2013 and 2014–2016 for transition-frequency comparisons and 2003-16 for SILC coverage.

### Other problematic transition patterns and policy implications
- Inactivity → Employment:
  - Slovenia exhibits a persistently very low transition frequency from inactivity to employment, consistently at the very low end of the sample distribution with an average transition frequency under 5 percent across 2006-2016.
  - This large-scale problem was apparently not impacted by the 2013 reform.
  - Policy recommendation: consider focusing on the tax wedge at the entry level of the labor market and other policy options to correct distortions affecting entry.
- Unemployment duration and long-term unemployment:
  - Transition frequencies from unemployment to unemployment have steadily risen, translating into persistent lengthening in average unemployment duration.
  - This trend is notable given the healthy recovery of the past three years and has recently come into policy focus (OECD (2016) cited in the text).
  - Policy implication: while further labor market liberalization and correction of unintended reform consequences would help, these measures may need to be supplemented by targeted initiatives to upgrade the skills of the long-term unemployed.

### Modeling perspective (Blanchard-Landier framework)
- Model basis:
  - Uses Blanchard and Landier (2002) framework to address labor market dualism by introducing temporary contracts alongside highly-protected open-ended contracts.
  - Temporary contracts serve as a screening device under worker heterogeneity and asymmetric information.
- Potential perverse outcomes under plausible parametrizations:
  - Employers more willing to hire for screening, but also more willing to fire temporary workers while firing costs are low, possibly preferring repeated screening over retaining profitable matches.
  - Possible labor market outcomes: more low-productivity entry-level jobs and fewer regular jobs (greater duality), lower overall productivity and output, and higher turnover in entry-level jobs.
  - Welfare risk: even if unemployment is reduced, workers may be worse off due to multiple spells of unemployment and entry-level jobs before obtaining a regular job.

*Italic: Source: EUROSTAT and text of the IMF staff report excerpt provided in the content unit.*

### 39.      For the purposes of this chapter, we made a few extensions to the B-L framework. The

### 39.      For the purposes of this chapter, we made a few extensions to the B-L framework. The

### Extensions to the B-L framework
- Two principal model extensions were introduced:
  - Fully endogenize open-ended contracts in the firms’ optimization problem by treating layoff costs on open-ended contracts as large but finite, so firms jointly determine terms for regular contracts (employment/wages) together with those for temporary contracts.
  - Incorporate payroll taxes on regular and temporary work as additional policy variables (flat payroll taxes tR wR and tT wT), to capture important features of the 2013 reform and to analyze the labor market impact of concurrent income tax reform.
- These extensions increase analytical complexity but permit richer welfare and policy evaluation.

### Model mechanics and equilibrium (search-theoretic structure)
- Framework:
  - Workers with heterogeneous, ex ante unobservable productivity are matched with employers; productivity is subject to random shocks with instantaneous probability π.
  - Layoffs can occur from both temporary and regular workers; firing cost for regular jobs is CR, firing cost for temporary jobs set to 0.
  - Firms and workers interact via repeated Nash bargaining, optimizing given distributions of shocks and parameters including layoff costs and payroll taxes.
- Endogenous thresholds:
  - ẕ : match-specific productivity at which firms are indifferent between ending temporary jobs and converting them to regular jobs.
  - ẑ : match-specific productivity at which firms are indifferent between dismissing and retaining workers under open-ended contracts.
- Equilibrium determines steady state unemployment u, temporary employment eT, and regular employment eR, and wages wT(z0) and wR(z).

### Key analytical structure and Bellman equations (selected)
- Firms (present discounted values):
  - ρ VF,T(z0) = z0 ‒ (1+ tT ) wT(z0) + π E( VF,R(z') ‒ VF,T(z0) | z'≥ẕ ) ;
  - ρ VF,R(z) = z ‒ (1+ tR ) wR(z) + π E( VF,R(z') ‒ VF,R(z) | z'≥ẑ ) + π ( VF,T(z0) ‒ VF,T(z) ‒ CR ) F(ẑ) ;
- Free entry / zero profit for temporary jobs:
  - VF,T(z0) = CV
- Nash bargaining conditions:
  - VF,T(z0) ‒ CV = VW,T(z0) ‒ U          (3)
  - VF,R(z) ‒ VF,T(z0) + CR = VW,R(z) ‒ U         (4)
- Workers (present discounted values; U is unemployed utility assumed zero):
  - ρ VW,T(z0) = (1+btT) wT(z0) + π E( VW,R(z') ‒ VW,T(z0) | z'≥ẕ ) + π [U ‒ VW,T(z0)] F(ẕ);
  - ρ VW,R(z) = (1+btR) wR(z) + π E( VW,R(z') ‒ VW,R(z) | z'≥ ẑ ) + π [U ‒ VW,R(z)] F(ẑ);
  - ρ U = θ [VW,T(z0) ‒ U]  with θ = h / u.

### Closed-form wage expressions (under model restrictions, U=0)
- Temporary job wage (from Bellman equations and U=0 assumption):
  - wR(z) = [ z ‒ ρ (CV ‒ CR) ] / [ 2 + (1+b)tR ]
- Regular job wage (same functional form displayed in source):
  - wR(z) = [ z ‒ ρ (CV ‒ CR) ] / [ 2 + (1+b)tR ]

### Productivity threshold implicit equations (Bellman-type)
- Using zero profit condition and substitutions, the productivity thresholds are implicitly defined as:
  - (ρ+ π) CV = { [(1+btR) ẕ + ρ(CV ‒ CR)] / [ 2 + (1+b)tR ] } + π E( VF,R(z') | z'≥ẑ ) + π (CV ‒ CR) F(ẑ)     (5)
  - (ρ+ π) (CV ‒ CR) = { [(1+btR) ẑ + ρ(CV ‒ CR)] / [ 2 + (1+b)tR ] } + π E( VF,R(z') | z'≥ẑ ) + π (CV ‒ CR) F(ẑ)     (6)
- Final expressions for the two productivity thresholds (as derived):
  - ẑ = (ρ+ π) (CV ‒ CR) ‒ π zmax / ρ + (π/ρ) [ F(zmax) ‒ F(ẑ) ]
  - ẕ = (ρ+ π)CV ‒ π zmax / ρ + (π/ρ) [ F(zmax) ‒ F(ẑ) ] + (ρ+ π)(1+tR)CR/(1+btR)

### Comparative statics on thresholds (policy variables)
- A reduction in CR:
  - reduces the difference between ẑ and ẕ — as ẑ falls and ẕ rises.
- A reduction in tR:
  - has the same effect as long as the link between benefits and payroll taxes is not perfect (b<1); if b=1, only ẑ falls.

### Steady-state expressions for employment and unemployment
- Identities (inactivity ruled out, labor force normalized to 1):
  - θu = π [ eT F(ẕ) + eR F(ẑ) ] = π eT
  - u + eT + eR ≡ 1
- Closed-form steady-state shares (as given):
  - u =  [π F(ẑ)] / [ π F(ẑ) + θ (F(ẑ)+π(1‒ F(ẕ))] 
  - eT = [θ F(ẑ)] / [ π F(ẑ) + θ (F(ẑ)+π(1‒ F(ẕ))] 
  - eR = [θ 1‒ F(ẕ)] / [ π F(ẑ) + θ (F(ẑ)+π(1‒ F(ẕ))]

### Main model implications and simulation findings
- B-L result preserved:
  - Lower layoff costs for open-ended contracts tend to increase both hiring and dismissals, with an ambiguous net impact on aggregate employment and unemployment.
- Extended-model departures from B-L:
  - Lower layoff costs for open-ended contracts lead to higher conversions of temporary jobs to regular jobs and a narrowing of regular-temporary wage differentials, implying an unambiguous reduction in labor market duality.
- Payroll tax effects:
  - A reduction in payroll taxes for regular contracts unambiguously reduces unemployment as well as labor market segmentation.
  - Under plausible calibrations, an increase in payroll taxes for temporary contracts tends to reduce labor market segmentation but increase unemployment.
  - Interaction effect: for a given payroll tax increase on temporary contracts, the adverse impact on unemployment is smaller, and the favorable impact on duality larger, the lower the level of layoff costs on permanent contracts.
- Consistency with Slovenia’s post-reform transitions:
  - The model interprets observed improvements in duality as consistent with combined impact of lower employment protection for regular contracts and reduced (increased) social contributions on regular (temporary) jobs.
  - Limited and mostly transitory improvement in segmentation is attributed to the temporary nature of social contribution cuts on regular jobs and insufficient liberalization of employment protection.

### Policy implications and recommendations (section G)
- Reform assessment:
  - The 2013 labor market reform had mixed outcomes; policy mix needs recalibration to support aggregate outcomes and durable objectives, and to correct unintended side effects.
- Employment protection:
  - Faster and deeper loosening of employment protection regulations should be prioritized to sustain gains in reducing dualism, limit unintended outflows of temporary workers into unemployment, and improve labor market matching and mobility.
  - Slovenia’s loosened restrictions on individual dismissals reached the OECD average, but further progress is advised (including loosening of collective dismissal regime unchanged by the reform).
- Fiscal instruments and incentives:
  - Fiscal incentives favoring open-ended contracts and penalizing temporary contracts should be rethought; in absence of sufficient employment protection liberalization they are not a good substitute and can produce suboptimal aggregate employment and distortions.
- Improving matching and skills:
  - Intensified retraining programs and an enterprise-based, apprenticeship-type system coordinated with schools recommended; authorities can support via derogations from the minimum wage and targeted fiscal incentives while systems are established.
- Labor supply and marginal tax wedge:
  - Strengthen transitions from inactivity to employment by ensuring the marginal tax wedge at entry level is appropriately low; both taxes and social benefits may need reassessment.
  - If tax-side action taken, prefer general reductions in labor taxes concentrated at the low end of the wage distribution.
- Long-term unemployment:
  - Address rising long-term unemployment through further liberalization complemented by measures on marginal tax wedge, derogations from the minimum wage for certain groups, and targeted training initiatives.

*REPUBLIC OF SLOVENIA  INTERNATIONAL MONETARY FUND*

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_Source: https://www.imf.org/-/media/files/publications/cr/2017/cr17126.pdf_