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### Introduction and context
- Statutory minimum wages widespread in CESEE since at least the early 1990s.
- Mid-2000s to 2007/8: wages rose rapidly without active minimum wage policy.
- 2008/09 crisis: wages stagnated or were cut while minimum wages remained unchanged in most countries.
- Recovery: wages growing again more slowly; governments increasingly use minimum wage policy to support low-income workers and address income inequality.

### Recent trends and key statistics
- 2011-15: real minimum wages grew by some 3 percent annually in CESEE; real labor productivity gains around 1 percent for CESEE on average.
- Latvia, Lithuania, Poland, Romania: minimum wage growth some 6 percent during 2011-15 against productivity growth of around 2½ percent.
- CESEE minimum wages about one third the level of Western Europe in absolute terms; minimum wages relative to average wages in CESEE about 40 percent.
- Lithuania and Slovenia: minimum-average wage ratios rivaling the highest western minimum wage ratio of over 50 percent (France).
- Relative to labor productivity: minimum wages in CESEE 17 percent against 22 percent in Western Europe.

### Incidence of minimum wages
- 2014 incidence examples:
  - Estonia: 6 percent of total employment
  - Turkey: 35 percent
  - Lithuania and Poland: 9 percent
  - Latvia: 15 percent
  - Romania: 22 percent
- Post-2014 hikes projected to raise incidence sharply (examples):
  - Lithuania estimated to 20 percent
  - Turkey estimated to 60 percent
- Comparators: U.K. and U.S. incidence less than 5 percent; France and Germany around 10 percent.
- Sectoral pattern (Lithuania, 2014): highest incidence in accommodation and food services, and wholesale and retail trade.
- Firm-size and education gradients (Estonia study): incidence four times average for workers with only primary education and twice average in small firms with up to 10 employees.

### Institutional arrangements and setting mechanisms
- In CESEE:
  - Four countries: government sets the minimum wage outright.
  - Nine countries: government fixes it after consultations with social partners.
  - Turkey and Serbia: decisions outsourced to specialized bodies with government representation.
  - Bosnia-Herzegovina and Estonia: minimum wage negotiated between social partners then given legal force by government decree.
  - None give expert opinion a formal role.
  - Differentiation by age, contract, or sector is rare.
- Western Europe examples of alternative arrangements:
  - U.K.: government acts on Low Pay Commission recommendations.
  - Belgium: links hikes to price developments.
  - Netherlands: links adjustments to average wages in collective wage agreements with an escape clause tied to welfare recipient counts.
  - France: formula-driven increases (price and real wage developments) with government discretion for additional hikes.

### Shadow economy and circumvention
- Estimated size of the shadow economy in CESEE: 10-30 percent of economic activity.
- Forms of minimum wage violations:
  - Envelope payments:
    - Affect an estimated 11 percent of workers.
    - Account for two fifth of their total pay in Central and Eastern Europe (Williams, 2009).
    - Account for between 12 and 20 percent in the Baltics (SEE Riga, 2015).
  - Underreporting of hours worked: possibly by as much as 13–20 percent in the Baltic countries (Žukauskas, 2015).
  - Complete shift of firms from the formal sector to the shadow economy.
- Proxies and correlations (2000–14): higher minimum wages associated with more part-time work, lower labor force participation, more cash use, and fewer hours worked per employee; individual relationships not robust alone but jointly suggest increased informality.

### Pass-through of minimum wage hikes to general wages
- Direct effect: pay of workers below the new minimum rises roughly proportional to the share of minimum-wage earners.
- Indirect (ripple/signaling) effects raise wages above the minimum.
- Selected estimates (preserved exactly):
  - Neumark, Schweitzer, and Wascher (2000), US 1979-1997:
    - 0.8 percent on gross wages for workers at or just above the minimum wage.
    - 0.25-0.40 percent on gross wages for those above the minimum wage.
  - Rama (2001), Indonesia 1993: 0.025-0.075 percent on the average wage.
  - Kertesi and Kollo (2003), Hungary 1986-1996: 0.04 percent immediate impact on the average wage.
  - Dickens and Manning (2004), UK 1998-1999: Direct effect on average log wage of 5.1 percent, rising to 7.2 percent if ripple effects included.
  - Maloney and Mendez (2004), Columbia 1997-1999:
    - 0.59-0.87 percent on hourly salaries for workers at or just above the minimum wage.
    - 0.28-0.38 percent on hourly salaries for those above the minimum wage.
  - Andreica et al. (2010), Romania 1999-2009: 0.0144 percent decrease of the growth rate of the average wage.
- CESEE-specific estimates:
  - Panel VAR (quarterly data from 2010 onward): general wage level estimated to rise by between 0.01–0.15 percent following a one percent increase in the real minimum wage over two years.
  - Firm-level analysis (~200,000 firms): firms’ overall wages increase by 0.12 percent for a one-percent minimum wage hike; tradable sector pass-through 0.17 percent (statistically significant).
  - Sector/country-level:
    - Lithuania and Romania: for a one percent rise in the minimum wage, general wages increase by between 0.30 and 0.45 percent.
    - Lithuania’s construction sector: pass-through almost 60 percent.
    - Lithuania’s financial sector: pass-through around 10 percent.
- Wage Dynamics Network firm survey highlights:
  - Latvia, January 2014, Share of minimum wage earners 20.7 percent: 20.6 percent of firms had to increase wages of employees earning above the minimum wage.
  - Lithuania, January 2013, Share of minimum wage earners 17.8 percent: a substantial increase of about 17 percent would increase total labor costs by approximately 11.1 percent; small enterprises about 12.7 percent; manufacturing 14.5 percent; construction 18 percent; business services 11.1 percent.
  - Romania, January 2014–January 2015, Share of minimum wage earners 11.3 percent: increases directly contributed about 0.5 percent to month-on-month growth of average gross wages in the private sector.
  - Slovenia, February 2010, Share of minimum wage earners 7.1 percent: 20.4 percent of firms had to increase wages of employees earning above the minimum wage.

### Compliance, offsets, and fiscal effects
- Translation of statutory hikes into higher remuneration depends on bindingness, compliance, employer offsets (cuts in non-wage benefits, reduced hours, envelope payments), and high social security/labor taxes reducing take-home pay.
- Non-compliance evidence (examples preserved):
  - Ashenfelter and Smith (1979): non-compliance with U.S. federal minimum wage in 1973 around 35 percent.
  - California garment sector surveys: non-compliance in two-thirds of factories (Milkman et. al., 2010).
  - U.K. social care sector: 11 percent of workers paid less than the minimum wage (Low Pay Commission, 2014).
  - Non-compliance documented for Brazil, Peru, Indonesia, and Mexico.

### Employment effects: evidence and estimates
- Literature range: significant disemployment to positive employment impacts; many studies show insignificant results.
- Selected CESEE findings:
  - Estonia: a 10 percent minimum wage hike reduces employment by 0.4-0.66 percent for those directly affected (Hinnosaar and Room, 2003).
  - Hungary: 2001 minimum wage increase of 57 percent reduced employment in small firms by almost 4 percent, particularly in remote regions (Kertesi and Kollo, 2003).
  - Poland: probability of remaining employed for a worker newly bound by the minimum wage declines by 11.5 percentage points (Baranowska-Rataj and Magda, 2014).
  - Lithuania: labor force participation rates found to have risen following hikes in net real minimum wages (Hazans, 2007).
- Survey evidence on intended employer responses:
  - Romania: almost half employers would cut back hiring because of hikes.
  - Slovenia: 20 percent said they would cut back hiring.
  - Latvia: 16 percent reported layoffs or reduced hiring.
  - Lithuania: 10 percent reported layoffs or reduced hiring; 18 percent reduced non-labor costs and 25 percent said they increased productivity.
- Regional context: unemployment averaged 9 percent in CESEE in 2015; youth unemployment more than twice as high.

### Estimation approach and key regression results
- Cross-country pooled regressions (17 CESEE countries, 2000-15) use youth employment-to-population ratio on minimum-to-average wage ratio controlling for other influences; youth employment proxies low-income employment.
- Baseline and reported elasticities:
  - Table A4.1 — Youth employment elasticity: -0.28; -0.09; -0.06 (columns 1–3).
  - Table A4.3 — Youth employment elasticity: -0.28; -0.15; -0.11; -0.09 (columns 1–4).
- MW/LP (minimum wage relative to labor productivity) specification:
  - Preferred linear specification with lagged dependent variable and lagged GDP growth: negative but statistically insignificant impact of 0.04 ppts on youth employment ratio for a 1 ppt increase in MW/AW.
  - If expressed as ratio to labor productivity: coefficient significant at negative 0.19.
    - Interpretation: youth employment declines by 1½ percent in response to a minimum wage increase of 10 percent at an initial MW/LP ratio of 20 percent.
- Non-linear (quadratic) findings:
  - Quadratic specification suggests stronger and rising disemployment effects as MW ratios increase; results significant when MW relative to labor productivity.
  - Example elasticities:
    - At MW = 10 percent (of productivity) elasticity -0.08.
    - At MW = 20 percent elasticity -0.16.
    - Quadratic specification: at 17 percent of labor productivity elasticity -0.2; at 24 percent elasticity -0.5 percent.
  - Tabulated illustrative impacts (Table A4.2):
    - Linear specification (Table A4.1 column 3) — 10 pct MW increase at MW/AW = 30 percent → -0.50 percent youth employment.
    - Quadratic specification — 10 pct MW increase at MW/AW = 45 percent → -2.04 percent youth employment.
- Firm-level (Orbis) regressions (Table A3.3) highlights:
  - ∆ minimum wage → ∆ Employment: -0.042*** (a 10 percent minimum wage increase associated with employment reduction of 0.4 percent).
  - Interaction ∆ minimum wage × Tradeable Sector → ∆ Employment: -0.0173*** (disemployment 50 percent higher for tradable firms).
  - ∆ minimum wage → ∆ Wage per employee: 0.127***.
  - ∆ minimum wage → ∆ Profit margin: -0.335* (larger negative effect in tradable sector: -0.490**).
  - No discernible impact on capital-labor ratio.
  - Productivity measures mixed: operating revenue per employee negative and significant; gross value added per employee positive, barely significant.

### Minimum wages and income distribution
- Minimum wages tend to mitigate wage disparities and improve household income distribution by affecting the lower tail of the wage distribution, but many drivers of income inequality lie beyond wages (capital income, tax progressivity, social protection).
- Limitations:
  - Very poor may not work or may belong to non-poor households.
  - Job losses from hikes can produce perverse distributional effects.
  - In-work poverty often due to too few working hours rather than low hourly pay.
- CESEE evidence:
  - Mixed results; some improvements in inequality measures but limited impact on poverty.
  - Latvia example: top-to-bottom decile ratio fell from 14.6 in 2006 to 12.1 in 2014 while minimum-to-average wage ratio rose from 30.1 to 41.8 percent.
  - Pooled unconditional correlation between annual changes of MW/AW and poverty incidence is positive but small and statistically insignificant.
- Romania EU-SILC case study (2007–2016 projections):
  - MW annual income (selected): 2007 = 4,680; 2014 = 10,500; 2015 (proj) = 12,150; 2016 (proj) = 14,200.
  - Share of MW paid workers: 2007 = 28.8; 2014 = 35.2; 2015 (proj) = 38.9; 2016 (proj) = 46.2 (percent of paid workers).
  - Median annual wage: 2007 = 8,013; 2014 = 13,233; 2015 (proj) = 14,065; 2016 (proj) = 15,014.
  - Estimated pass-through on gross wage for Romania: around 0.4 percent for a one percent increase of the minimum wage.
  - Wage gap (top-to-bottom income): 2007 = 21.2; 2014 = 15.6; 2016 (proj) = 13.1.
  - Distributional benefits diminished at higher minimum wage levels; increases during 2013–16 primarily lifted the entire wage scale without substantially changing wage relativities.

### Minimum wages and competitiveness
- Firms’ adjustment channels: cut employment/new hiring; reduce non-wage costs; raise prices; boost productivity; accept reduced profits; most firms use a mix.
- Tradable firms have less scope to pass costs into prices; tradable firms face larger employment and profit impacts.
- CESEE-specific competitiveness risks:
  - Labor-intensive exports are a larger share of total exports in CESEE (notably Latvia, Lithuania, Romania).
  - In several countries, wage growth has outstripped productivity growth recently, raising unit labor costs faster than in the EU as a whole and eroding competitiveness.
  - Tradable-sector productivity cushions from post-crisis adjustment largely exhausted.
  - Continued sharp MW-driven general wage growth could weaken competitiveness; many CESEE countries have lost global export market shares or gained them at much lower rates than in the past.
  - Real effective exchange rates have appreciated and often stand much higher now than in 2005.

### Policy-relevant implications and recommendations
- Measurement and analysis:
  - Prefer MW/LP (minimum-wage-to-productivity) ratios in CESEE analyses due to underreporting of average wages; account for noisier productivity data and business-cycle co-movement (include controls like GDP growth).
  - Test for non-linear (quadratic) effects to detect rising disemployment as MW bite increases.
- Appropriate ranges and pacing:
  - Empirical literature situates appropriate minimum wage between 25 and 50 percent of the average wage; Rutkowski (2003) rule: not exceed 40 percent of the average wage in developing countries.
  - Joint ILO/OECD/IMF/World Bank (G20, 2012): 30-40 percent of the median wage suitable.
  - Paper’s assessment for CESEE: range where trade-offs become stark at 40 to 50 percent of average wages; trade-offs much starker around mid-40s.
  - Quantitative illustration:
    - At MW/AW = 45 percent, a further 10 percentage MW hike associated with reduction of youth employment by 2 percent.
    - At MW/AW = 30 percent, a further 10 percentage MW hike associated with reduction of youth employment by 0.4 percent.
  - Pace guidance:
    - Calibrate MW policy to avoid adding further fuel to already high general wage growth.
    - Where general wage growth unsustainable, keep minimum wage increases aligned with productivity gains.
- Design and institutional reforms:
  - Depoliticize MW setting: involve independent experts or commissions; use indexation/formula-driven adjustments with escape clauses; ensure unemployed interests considered.
  - Consider differentiated minimum wages (by age, region, experience, or sector) to alleviate adverse side effects; many CESEE countries currently use a single national minimum wage.
  - Complement MW policy with targeted social protection, more progressive income taxation, and comprehensive taxation of capital and wealth to address inequality more effectively.
  - Monitor enforcement and the shadow economy to limit evasion and preserve distributional benefits.

### Data, methodologies, and annex findings (high-level)
- Panel VARs across 14 CESEE countries (quarterly 1995q1–2015q2) used to add time dimension to pass-through analysis; variables in real terms using CPI deflator; system estimated using GMM.
- Orbis firm-level analysis: about half a million observations related to 200,000 firms covering a sizable portion of national employment; regressions estimated using Arellano-Bond dynamic two-step panel estimator with robust standard errors.
- Annex IV pooled regression framework for MW and employment uses youth employment rate and tests linear, quadratic, threshold, and MW/LP specifications with country and time fixed effects.
- Table A3.2 (Determinants of Value-Added Exports, 2000-13) reports a negative and significant coefficient for Relative Minimum Wage in Labor Intensive Manufacturing (-0.732**) and Labor Intensive Services (-0.332*), and various controls with significance levels preserved exactly as reported.
- Table A3.3 reports ∆ minimum wage → ∆ Employment: -0.042***; ∆ minimum wage → ∆ Wage per employee: 0.127***; interactions and other coefficients as presented in source tables.

*Source: CROSS-COUNTRY REPORT ON MINIMUM WAGES (April 25, 2016), INTERNATIONAL MONETARY FUND.*

### 1. Minimum Wages in CESEE _____________________________________________________________________ 5

### 1. Minimum Wages in CESEE

### Introduction and context
- Many CESEE countries have increasingly used minimum wage policies in recent years; statutory minimum wages have been in place at least since the early 1990s.
- During the mid-2000s to 2007/8 wages rose rapidly without active minimum wage policy; after the 2008/09 crisis wages stagnated or were cut while minimum wages remained unchanged in most countries.
- With the recovery, wages are growing again more slowly, prompting governments to use minimum wage policy more prominently to support low-income workers and address income inequality.

### Recent trends and key statistics
- During 2011-15, real minimum wages grew by some 3 percent annually in CESEE, considerably exceeding real labor productivity gains of around 1 percent for CESEE on average.
- For Latvia, Lithuania, Poland, and Romania, minimum wage growth was some 6 percent during 2011-15 against productivity growth of around 2½ percent.
- CESEE minimum wages are about one third the level of Western Europe in absolute terms, but minimum wages relative to average wages in CESEE are about 40 percent (Figure 1).
- Lithuania and Slovenia have minimum-average wage ratios rivaling the highest western minimum wage ratio of over 50 percent (France).
- Relative to labor productivity, minimum wages in CESEE are still somewhat lower than in Western Europe—17 percent against 22 percent—because of more widespread wage underreporting and a lower labor share of income.

### Incidence of minimum wages
- Incidence varies widely across the region (2014): Estonia 6 percent of total employment; Turkey 35 percent; Lithuania and Poland 9 percent; Latvia 15 percent; Romania 22 percent.
- Minimum wage hikes since 2014 are projected to raise incidence sharply (examples cited): Lithuania estimated to 20 percent; Turkey estimated to 60 percent.
- Comparators: U.K. and U.S. incidence less than 5 percent; France and Germany around 10 percent.
- Sectoral pattern (example Lithuania, 2014): highest incidence in accommodation and food services, and wholesale and retail trade.
- Firm-size and education gradients: an Estonia study found minimum wage incidence four times average for workers with only primary education and twice average in small firms with up to 10 employees (Bank of Estonia, 2015).

### Institutional arrangements and setting mechanisms
- In CESEE, governments are the key players in minimum wage determination (Annex I, Table A1.1):
  - In four countries government sets the minimum wage outright.
  - In nine countries government fixes it after consultations with social partners.
  - In Turkey and Serbia, decisions are outsourced to specialized bodies with government representation.
  - In Bosnia-Herzegovina and Estonia, minimum wage is negotiated between social partners and then given legal force by government decree.
  - None of the CESEE countries give expert opinion a formal role.
  - Differentiation by age, contract, or sector is rare and limited where it exists.
- Western Europe tends to rely more on expert opinion and formula-driven adjustment (Annex I, Table A1.2):
  - U.K.: government acts on recommendations from the independent Low Pay Commission.
  - Belgium links minimum wage hikes to price developments.
  - Netherlands links adjustments to average wages in collective wage agreements with an escape clause tied to welfare recipient counts.
  - France uses formula-driven increases (price and real wage developments) with government discretion for additional hikes.

### Key policy tradeoffs and decision questions
- Governments face key tradeoffs when setting minimum wages: equity versus efficiency, employment and competitiveness effects versus distributional gains.
- Two core questions for policymakers:
  - What is an appropriate range for minimum wage levels? Consider how minimum wage hikes further equity goals and efficiency relative to alternatives; assess potential unemployment, automation, or productivity responses; identify levels that balance pros and cons.
  - What is the appropriate pace for minimum wage growth? Evaluate how minimum wage hikes fuel overall wage growth directly (for minimum earners) and indirectly (ripple and signaling effects); consider productivity developments and competitiveness constraints.

### The economics of minimum wages: channels and possible outcomes
- Minimum wage policy aims to improve income distribution but may have important implications for economic efficiency.
- Preliminary check: if minimum wages are non-binding or not implemented, hikes have no economic effect.
- Risk of evasion in countries with large shadow economies:
  - Envelope payments (under-the-table supplements) can lead to reshuffling between official and unofficial pay without changing actual labor costs.
  - Employers may reduce reported hours while keeping total pay unchanged.
  - Economic activity could be pushed into the shadow economy, undermining productivity growth and respect for the law (Farrell, 2004).
- Distributional mechanics:
  - If firms fully pass higher wage costs to prices, consumers (rich and poor) bear the cost.
  - If firms cannot pass costs on, owners earn less profit.
  - Carryover from wage distribution to overall income distribution is typically muted; those at the very bottom often do not work and minimum wage recipients are often second-income earners in reasonably well-to-do households (Low Pay Commission, 2014, p.55).
- Efficiency considerations:
  - In competitive labor markets, binding minimum wages above market-clearing levels can reduce job creation, growth, and investment, and induce automation or substitution of capital/high-skilled labor for low-skilled labor.
  - Minimum wage hikes could blunt incentives for career development and investment in education if they overly compress the wage scale.
  - Conversely, minimum wages can improve efficiency if labor market distortions exist (e.g., monopsony power, asymmetric information, job search costs), potentially increasing employment.

### Minimum wages and remuneration: compliance, offsets, and fiscal effects
- Whether minimum wage hikes translate into higher worker remuneration depends on:
  - Bindingness of the statutory minimum.
  - Compliance and implementation in contexts with shadow economies.
  - Employer offsets (cuts in non-wage benefits, reduced hours, “envelope payments”).
  - High social security and labor taxes that reduce take-home pay from gross wage increases.
- Evidence of non-compliance in other settings:
  - Ashenfelter and Smith (1979) estimated non-compliance with the U.S. federal minimum wage in 1973 around 35 percent.
  - California low-wage garment sector surveys found non-compliance in two-thirds of factories (Milkman et. al., 2010).
  - U.K. social care sector: 11 percent of workers paid less than the minimum wage (Low Pay Commission, 2014).
  - Non-compliance also documented for Brazil, Peru, Indonesia, and Mexico (various studies cited).

### Framework for assessing impacts (Figure 2 summary)
- Possible branches from a statutory minimum wage increase:
  - If not binding → official wage of minimum wage earners ± 0 → no economic or fiscal effects.
  - If binding and fully enforced → official wage of minimum wage earners ↑ → remuneration of minimum wage earners may ↑ or be offset (through envelope payments, benefit cuts, more informality), with attendant fiscal and distributional consequences.
  - Possible economy-wide outcomes include: ripple effects raising average remuneration; full pass-through to prices; firms responding with productivity enhancements, automation, job cuts, or profit absorption; monopsonistic responses that could expand employment.
- Net outcomes depend on enforcement, market structure, firms’ ability to pass through costs, and extent of preexisting distortions.

*Cross-Country Report on Minimum Wages (April 25, 2016), INTERNATIONAL MONETARY FUND*

### 15.      The large size of the shadow economy in CESEE offers ample scope for circumventing

### _cr16151 - 15.      The large size of the shadow economy in CESEE offers ample scope for circumventing

### Shadow economy and minimum wage circumvention
- Estimated size of the shadow economy in CESEE: 10-30 percent of economic activity.
- Regional trend: the relative size of the shadow economy has declined somewhat over the past decade as income convergence advanced, but it remains large.
- Principal forms of minimum wage violations:
  - Envelope payments:
    - Affect an estimated 11 percent of workers.
    - Account for two fifth of their total pay in Central and Eastern Europe (Williams, 2009).
    - Account for between 12 and 20 percent in the Baltics (SEE Riga, 2015).
    - Regularization of envelope payments could reduce net pay as tax and social security deductions rise.
  - Underreporting of hours worked:
    - Possibly by as much as 13–20 percent in the Baltic countries (Žukauskas, 2015).
    - Minimum wage hikes could be offset by reducing officially reported hours.
  - Complete shift of firms from the formal sector to the shadow economy.

### Evidence and proxies for non-compliance in CESEE (2000–14)
- Literature focuses on proxies for non-compliance: labor market participation rates, hours worked, incidence of part-time work, and the use of cash.
- Correlations observed between changes in the minimum-to-median wage ratio and these proxies:
  - Higher minimum wages associated with more part-time work.
  - Higher minimum wages associated with lower labor force participation.
  - Higher minimum wages associated with more cash use.
  - Higher minimum wages associated with fewer hours worked per employee.
- Caveats: None of the individual relationships controls for other influences or is statistically robust on its own; jointly they are nonetheless suggestive of increased informality undermining minimum wage effectiveness.

### Pass-through of minimum wage hikes to general wages
- Direct and indirect effects:
  - Direct: pay of all workers below the new minimum rises; magnitude roughly proportional to the share of minimum-wage earners.
  - Indirect: wage adjustments above the minimum to maintain wage differentials; signaling effects for wage-setting.
- Evidence summary (selected estimates preserved exactly as reported):
  - Neumark, Schweitzer, and Wascher (2000), US 1979-1997:
    - 0.8 percent on gross wages for workers at or just above the minimum wage.
    - 0.25-0.40 percent on gross wages for those above the minimum wage.
  - Rama (2001), Indonesia 1993:
    - 0.025-0.075 percent on the average wage.
  - Kertesi and Kollo (2003), Hungary 1986-1996:
    - 0.04 percent immediate impact on the average wage.
  - Dickens and Manning (2004), UK 1998-1999:
    - Direct effect on average log wage of 5.1 percent, rising to 7.2 percent if ripple effects are included.
  - Maloney and Mendez (2004), Columbia 1997-1999:
    - 0.59-0.87 percent on hourly salaries for workers at or just above the minimum wage.
    - 0.28-0.38 percent on hourly salaries for those above the minimum wage.
  - Andreica et al. (2010), Romania 1999-2009:
    - 0.0144 percent decrease of the growth rate of the average wage.
- CESEE-specific findings:
  - Panel VAR estimation (quarterly data from 2010 onward):
    - General wage level estimated to rise by between 0.01–0.15 percent following a one percent increase in the real minimum wage over two years.
    - Pass-through is likely at the upper end of this range given higher minimum wage incidence and rising minimum-to-average wage ratios.
    - Statistical significance not established when covering longer periods including boom-bust cycles (caution advised).
  - Firm-level analysis (about 200,000 firms across CESEE):
    - Firms’ overall wages increase by 0.12 percent for a one-percent minimum wage hike.
    - Firms in the tradable sector: pass-through rises to 0.17 percent.
    - Results are statistically significant (Annex III referenced).
  - Sectoral/country-level estimates:
    - Lithuania and Romania (sector-level data): for a one percent rise in the minimum wage, general wages increase by between 0.30 and 0.45 percent.
    - Lithuania’s construction sector: pass-through almost 60 percent.
    - Lithuania’s financial sector: pass-through around 10 percent.

### Firm survey evidence on wage responses (Wage Dynamics Network)
- Around a fifth of firms increased wages of employees earning above the minimum wage when minimum wages were hiked.
- Table 2 selected findings (preserved exactly):
  - Latvia, January 2014, Share of minimum wage earners 20.7 percent:
    - 20.6 percent of firms had to increase the wages of employees earning above the minimum wage level.
  - Lithuania, January 2013, Share of minimum wage earners 17.8 percent:
    - A substantial increase in minimum wage in January 2013 of about 17 percent would increase total labor costs by approximately 11.1 percent.
    - Small enterprises experienced higher labor cost increase or about 12.7 percent.
    - Larger cost increases reported in manufacturing (14.5 percent), construction (18 percent), and business services (11.1 percent).
  - Romania, January 2014–January 2015, Share of minimum wage earners 11.3 percent:
    - Minimum wage increases in Jan. 2014, Jul. 2014, and Jan. 2015 directly contributed about 0.5 percent to the month-on-month growth of average gross wages in the private sector.
  - Slovenia, February 2010, Share of minimum wage earners 7.1 percent:
    - 20.4 percent of firms had to increase the wages of employees earning above the minimum wage level.

### Employment effects of minimum wage hikes
- Ongoing debate: critics argue minimum wages reduce employment among low-income earners; proponents cite monopsony models where moderate hikes can raise employment.
- Empirical literature:
  - Findings span significant disemployment to positive employment impacts; many studies show insignificant results.
  - Early studies: employment elasticities often in range negative 0.1-0.3 but methodologies questioned.
  - More recent work: elasticities clustered around zero; impacts on low-skilled and young workers typically negative and larger.
- CESEE studies (selected findings preserved exactly):
  - Estonia: a 10 percent minimum wage hike reduces employment by 0.4-0.66 percent for those directly affected; impact on other wage groups insignificant (Hinnosaar and Room, 2003).
  - Hungary: 2001 minimum wage increase of 57 percent reduced employment in small firms by almost 4 percent, particularly strongly in remote regions (Kertesi and Kollo, 2003).
  - Poland: probability of remaining employed for a worker newly bound by the minimum wage declines by 11.5 percentage points (Baranowska-Rataj and Magda, 2014).
  - Lithuania: labor force participation rates found to have risen following hikes in net real minimum wages (Hazans, 2007).
- Survey evidence on intended employer responses:
  - Romania: almost half the employers stated they would cut back hiring because of minimum wage hikes.
  - Slovenia: 20 percent said they would cut back hiring.
  - Latvia: 16 percent reported layoffs or reduced hiring.
  - Lithuania: 10 percent reported layoffs or reduced hiring; 18 percent of firms reduced non-labor costs and 25 percent said they increased productivity.
- Regional unemployment context:
  - Unemployment averaged 9 percent in CESEE in 2015.
  - Unemployment rates for the young were more than twice as high.

### Estimation approach for employment effects in this paper
- Data: 17 CESEE countries during 2000-15.
- Baseline empirical specification: pooled linear regression of youth employment-to-population ratio on the minimum-to-average wage ratio (following OECD (1998) and Neumark and Wascher (2004)), controlling for other influences.
- Rationale: youth employment used as a proxy for low-income employment; statistical effects on youth employment easier to recover amid noise, short data series, and lagged effects.

*Source: CROSS-COUNTRY REPORT ON MINIMUM WAGES, INTERNATIONAL MONETARY FUND (chapter excerpt).*

### 30.      Alternative specifications are then considered to test for non-linearities in the

### _cr16151 - 30.      Alternative specifications are then considered to test for non-linearities in the

### Non-linear specifications and youth employment
- Specification tested: linear and quadratic terms for the minimum wage ratio; alternative measure uses minimum wages as a ratio to labor productivity rather than average wages.
- Rationale: using productivity addresses underreporting of average wages in CESEE due to “envelope payments,” but productivity data are noisier and can co-move with the business cycle; estimations include controls (e.g., GDP growth) and robustness checks.
- Key linear-specification findings:
  - When minimum wages are expressed as a ratio of productivity, minimum wages are negatively associated with employment with statistical significance (Table A4.1 columns 1–3 and Table A4.3 columns 1-4).
  - Preferred linear specification (lagged dependent variable and lagged GDP growth as controls) finds a negative but statistically insignificant impact of 0.04 ppts on the youth employment ratio for a 1 ppt increase in the minimum-to-average wage ratio.
  - If minimum wages are expressed as a ratio to labor productivity, the coefficient becomes significant at a negative 0.19.
    - Interpretation: implies that youth employment declines by 1½ percent in response to a minimum wage increase of 10 percent at an initial minimum-wage-to-productivity ratio of 20 percent.
  - Employment elasticities implied by the linear relationship increase as minimum wage ratios rise:
    - At a ratio of 10 percent the elasticity is -0.08.
    - At a ratio of 20 percent the elasticity is double (implied -0.16).
- Key quadratic-specification findings:
  - Quadratic specification suggests stronger and rising disemployment effects as minimum wage ratios increase; results significant when minimum wages are relative to labor productivity (Tables A4.1 and A4.3 columns 5).
  - At a ratio of 45 percent of average wages, a 10 percent minimum wage hike is associated with a youth employment reduction of 2 percent in the quadratic specification, compared to 0.7 percent in the linear specification.
  - The squared minimum wage ratio has the expected negative sign but minimum wage term coefficients are jointly insignificant unless expressed relative to labor productivity.
  - Youth employment elasticities from quadratic specification:
    - When minimum wages stand at 17 percent of labor productivity (the current average level in CESEE), elasticity is -0.2.
    - When minimum wages reach 24 percent of labor productivity (as in some CESEE countries), elasticity rises to -0.5 percent.

- Tabulated illustrative impacts (derived from regressions in Table A4.3):
  - Underlying regressions use minimum wages (MW) relative to labor productivity (LP).
  - Linear specification (derived from Table A4.3 column 3) and Quadratic specification (derived from Table A4.3 column 5) produce percent impacts on youth employment for varying initial MW/LP levels and MW increases; the source lists a matrix of impacts (excerpted rows shown in the source).

### Firm-level evidence
- A 10 percent increase in minimum wages is associated with an employment reduction of 0.4 percent (Annex III, Table A3.3); results are significant and refer to total firm employment.
- Disemployment effect is 50 percent higher for firms in the tradable sector (interpretation: tradable firms less able to pass on costs to customers).
- Additional firm-level findings:
  - Export growth in value-added terms is adversely affected by minimum wage hikes, statistically significant for labor-intensive manufacturing and labor-intensive services (Table A3.2).
  - A 10 percent increase in minimum wages reduces profit margins by 3 percent on average and by 8 percent for firms in the tradable sector (Table A3.3).
  - No discernible impact on the capital-labor ratio (no empirical support for substitution of capital for labor).
  - Effect on productivity is mixed:
    - Operating revenue per employee shows a significant negative impact.
    - Gross value added per employee shows a positive impact, barely significant.

### Minimum wages and income distribution
- Purpose of minimum wages: improve income distribution by elevating the bottom of the wage distribution; however, many drivers of income inequality lie beyond wages (capital income, tax progressivity, social protection).
- Literature synthesis:
  - Minimum wage hikes tend to mitigate wage disparities and improve distribution of household income by affecting the lower tail of the wage distribution.
  - Minimum wages can help guard against “social dumping.”
  - Very poor may not work or may belong to non-poor households, weakening the wage–income link.
  - If minimum wage hikes cause substantial job losses, distributional effects can be perverse.
  - In-work poverty often results from too few working hours rather than low hourly pay; minimum wages alone cannot fully address poverty.
- CESEE-specific evidence:
  - Minimum wages are suggestive of improving inequality measures (Gini coefficient, income gap) in some CESEE countries but largely fail to scale back poverty.
  - Examples:
    - Latvia: ratio between incomes in the highest and the lowest deciles fell from 14.6 times in 2006 to 12.1 times in 2014, while minimum-to-average wage ratio rose from 30.1 to 41.8 percent.
    - Poland, Romania, Slovakia: recent sharp minimum wage increases seem associated with improvements in equality; other CESEE countries show mixed results.
  - For the region overall, the pooled unconditional correlation between annual changes of minimum-to-average wage ratios and poverty incidence is positive but small and statistically insignificant.
- Romania case study (micro-level EU-SILC analysis):
  - Since 2007, real wages increased substantially and the share of workers earning less than a certain real income declined.
  - Estimated share of workers earning the minimum wage rose to 45 percent in 2016 from around 30 percent in 2007—bunching at or near the minimum wage became more pronounced.
  - Wage distribution improvements in Romania:
    - Ratio of top to bottom wage deciles declined from 21.2 in 2007 to 15.6 in 2014, and to an estimated 13.1 in 2016.
    - Minimum-to-average wage ratio: 2007 = 28 percent; 2010 = 31 percent; 2014 = 37 percent; 2016 (projected) = 45 percent.
  - Distributional benefits diminished at higher minimum wage levels:
    - Strong redistribution during 2009–12 when wage disparities fell sharply.
    - Increases during 2013–16 primarily lifted the entire wage scale without substantially changing wage relativities.
  - Wage distribution gains partially carry over to household income distribution, but unemployed and small-pension households do not benefit; frequency spike observed at very low incomes but not at very low wages.

### Minimum wages and competitiveness
- Adjustment channels available to firms facing higher minimum wages: cut employment/new hiring, reduce non-wage costs, raise prices, boost productivity, accept reduced profits; most firms use a mix of responses.
- Tradable vs domestic sectors:
  - Tradable (exporting) firms have less scope to pass higher labor costs into prices, making other channels (employment cuts, productivity, profit adjustments) more important.
- Literature highlights:
  - Stock market valuations of low-pay U.S. firms largely unaffected by minimum wages (Card and Krueger, 1994); U.K. profits materially hurt when national minimum wage introduced in 1999 (Draca et al., 2011).
  - Limited price pass-through in U.S. studies except restaurants; similar U.K. findings.
  - Some evidence of productivity gains in response to higher minimum wages for firms with many low-paying jobs, though this may reflect reduced official hours worked.
  - Hungary: a 57 percent minimum wage hike in 2001 entailed a 20 percent increase in total labor costs for highly exposed firms, but profits did not materially fall and nominal sales rose—suggesting pass-through into prices.
- CESEE-specific competitiveness risks:
  - CESEE more exposed because labor-intensive exports are a larger share of total exports in CESEE (notably Latvia, Lithuania, Romania).
  - In several countries, wage growth has outstripped productivity growth recently, raising unit labor costs faster than in the EU as a whole and eroding competitiveness.
  - Tradable-sector productivity cushions from post-crisis adjustment are largely exhausted.
  - Continued sharp minimum wage-driven general wage growth could weaken competitiveness; many CESEE countries have lost global export market shares or gained them at much lower rates than in the past.
  - Real effective exchange rates have appreciated and often stand much higher now than in 2005.

### Policy-relevant implications (derived from findings in the source text)
- Measurement and analysis:
  - Use minimum-wage-to-productivity ratios as a preferred metric in CESEE analyses due to underreporting of average wages; account for noisier productivity data and business-cycle co-movement (include controls like GDP growth).
  - Test for non-linear (quadratic) effects when assessing minimum wage impacts to detect rising disemployment effects as minimum wage bite increases.
- Design considerations for minimum wage policy:
  - Be cautious of pushing minimum wages to levels where youth employment elasticities and disemployment effects rise substantially (e.g., elasticity magnitudes increase as MW/LP rises from 17 percent to 24 percent).
  - Monitor sectoral exposures: tradable and labor-intensive sectors face larger employment and profit impacts and may transmit competitiveness losses.
  - Complement minimum wage policy with other measures to address poverty and inequality (targeted social protection, tax progressivity), recognizing minimum wages alone may not reduce poverty and may have limited distributional reach for non-working poor and pensioners.
  - Consider phased or targeted approaches and broader labor-market and productivity-enhancing policies to mitigate adverse employment and competitiveness effects.

*Source: IMF staff calculations and analysis in the Cross-Country Report on Minimum Wages (excerpted content units)._

### 46.      The above analysis provides some important pointers to answer the two questions

### The above analysis provides some important pointers to answer the two questions

### Key cross-cutting pointers and country considerations
- Main contribution: clarifies trade-offs associated with minimum wage policies; does not analyze in detail country-specific main challenges.
- Country-specific notes:
  - Income inequality seems an important issue in Latvia, Lithuania, and Romania.
  - Poland is broadly in line with European standards, according to Gini coefficients and income gap indicators.
  - Competitiveness challenge appears largest in Latvia judging from market share and real effective exchange rate developments.
  - Unemployment is particularly high in Latvia and Lithuania.
  - Fast wage growth needs to be watched especially in Latvia and Romania.
  - All four countries (Latvia, Lithuania, Poland, Romania) should be mindful of their large shadow economies.
- Paper does not address social preferences (trade-off between efficiency and equity).

### What is an appropriate range for minimum wage levels in CESEE?
- Conceptual framing:
  - Optimal minimum wage exists between levels where hikes are beneficial (when low) and counterproductive (when very high).
  - Benefits at low levels: improve wage distribution with limited employment effects; potential efficiency gains via higher aggregate demand, productivity incentives, or correcting market imperfections (monopsony, costly job search, information asymmetries). Direct evidence of these efficiency effects remains scant.
  - There is an upper bound beyond which adverse side effects dominate.
- Empirical and policy references:
  - Few studies address appropriate level directly; those that do situate it between 25 and 50 percent of the average wage.
  - IMF policy examples (contingent on prevailing minimum wage level):
    - U.S.: increase the federal minimum wage—currently pegged at less than 30 percent of the average wage (IMF, 2015b).
    - France: consider a temporary freeze of its minimum wage—currently exceeds 50 percent of the average wage (IMF, 2013).
    - Columbia: address binding minimum wage of some 70 percent of the average wage (IMF, 2011).
  - Rutkowski (2003) rule of thumb: minimum wage should not exceed 40 percent of the average wage in developing countries; threshold lower when unemployment is high and concentrated among young and low-skilled.
  - Joint ILO, OECD, IMF, World Bank report (G20, 2012): minimum wage of 30-40 percent of the median wage suitable; roughly corresponds to 25-35 percent minimum-to-average wage ratio.
  - Council of Europe (European Social Charter (revised), 1996, Part I, §4): right to “fair remuneration sufficient for a decent standard of living” interpreted as minimum net pay of at least 60 percent of average net pay; in gross pay terms may correspond to around 50 percent.
- Paper’s assessment for CESEE:
  - Sees the range where further minimum wage hikes run into stark trade-offs at 40 to 50 percent of average wages for CESEE countries.
  - Evidence suggests negative employment effects from minimum wage hikes; disemployment becomes more of a problem at higher initial levels of the minimum wage.
  - Trade-offs become much starker around a minimum-to-average wage ratio in the mid-40s.
  - Quantitative illustration from estimation results:
    - At a ratio of 45 percent, a further 10 percentage minimum wage hike is associated with a reduction of youth employment by 2 percent.
    - At a ratio of 30 percent, a further 10 percentage minimum wage hike is associated with a reduction of youth employment by 0.4 percent.
  - Reporting caveats for CESEE:
    - Wages tend to be underreported in CESEE, artificially inflating reported minimum-to-average ratios; a reported ratio of 50 percent may correspond to a true ratio close to 40-45 percent.
    - Distributional benefits diminish once minimum wage reaches high levels.
    - Very high minimum wages likely spur evasion and informality, hampering distributional benefits, undermining rule of law, and slowing productivity growth.
    - Empirical analysis may understate adverse employment effects because it is mostly based on episodes of relatively low minimum wages and is not designed to capture long-run effects.

### What is an appropriate pace of minimum wage hikes in CESEE?
- Dependence on country-specific circumstances.
- Literature focus: quantifying pass-through from minimum wages to general wages rather than prescribing pace.
- Pass-through range found in several exercises in the paper: 10 to 45 percent.
  - Wide range due to methodological differences and case-specific factors.
- Factors increasing pass-through:
  - Larger share of workers on minimum wage.
  - Tighter overall wage structures and benefit systems linked to the minimum wage.
  - Tight labor markets (pass-through stronger than at bottom of business cycle).
- Firm adjustment channels influencing ultimate effects:
  - Ability to pass costs to customers via higher prices.
  - Availability of profit buffers to absorb cost increases.
- Policy guidance on pace:
  - Minimum wage policies should be calibrated so as not to add further fuel to general wage growth where it is already high.
  - In recent years, general wage growth has typically exceeded productivity growth by a wide margin.
  - Tradable sector so far absorbed pressure by drawing on buffers from the post-crisis adjustment period, but these buffers appear largely depleted.
  - To protect competitiveness, wage growth needs to moderate and minimum wage policy ought to be consistent with this objective.
  - Perpetuating current pace of minimum wage hikes would be risky, especially since pass-through to general wages is now likely higher (minimum wage applies to more workers; CESEE’s relatively labor intensive exports are sensitive to rising wage costs).
  - Countries where general wage growth is unsustainable should keep minimum wage increases aligned with productivity gains, even when current minimum wage level is unproblematic.

### Policies for appropriate minimum wages and better income distribution
- Depoliticize the process of setting minimum wages:
  - Minimum wages have strong political appeal but limits exist to what they can achieve.
  - Firms may pass costs into higher prices, reducing purchasing power of all consumers, including poorer households.
  - Profit impacts can hurt competitiveness, growth, and employment.
  - These indirect effects are difficult to communicate and can be lost in political debate.
  - Create distance between minimum wage setting and political process; involve independent experts (positive role seen in France, Germany, U.K.).
  - Ensure the interest of the unemployed is taken into account.
  - Indexation and formula-driven mechanisms as default for adjustments can depoliticize; escape clauses needed for unforeseen developments (example: Netherlands).
  - Caution: generous indexation that becomes a floor with expected discretionary top-ups risks pushing minimum wages too high over time (example: France).
- Differentiate minimum wages to alleviate adverse side effects:
  - Appropriate level unlikely to be uniform across economy; poorer regions, low-skill workers, young people without experience, and long-term unemployed may need different treatment.
  - Many countries use differentiated minimum wages (examples: Germany exempts apprentices; U.K. has lower rates for the young).
  - With few exceptions, CESEE countries set a single national minimum wage; they should consider tailoring more to circumstances.
- Broader policy approach needed to address income inequality:
  - Income inequality is high in CESEE by European standards.
  - Minimum wages can improve wage distribution but overreliance risks negative side effects and limited impact:
    - Those at the very bottom may not work at all.
    - Minimum-wage earners may be part of better-off households.
  - Making larger inroads into income equality requires:
    - Higher and better targeted social protection spending.
    - More progressive income tax systems.
    - More comprehensive taxation of capital and wealth.
  - Minimum wages are politically appealing due to limited direct fiscal costs but are insufficient alone.

### Annex I — Minimum wage arrangements (high-level patterns)
- Many CESEE countries: decision-making typically government following social partners’ recommendations; most adjustments annual or ad hoc; geographical coverage national; few exceptions stated for youth, apprentices, disabled, or job complexity across countries.
- Selected Western Europe: varied institutional arrangements (commissions, councils, formula linkages, indexation), frequency ranging from semiannual to annual to biannual, and common age- or experience-based lower rates.

### Annex II — Methodology: minimum wages and general wage growth
- Study adds time dimension to pass-through analysis via panel VAR technique (allows gradual adjustment characterization and identification of temporary vs. persistent effects).
- Data and estimation details:
  - Panel VARs estimated across 14 CESEE countries.
  - Yt is stacked vector of changes in real average wages, employment growth, real labor productivity growth, changes in the terms of trade, and changes in real minimum wages for each country i=1, 2, ..., N and t=1, 2, ..., T.
  - Choice of variables follows Blanchard and Katz (1999) and Goretti (2008).
  - All variables in real terms using the consumer price index as deflator.
  - Data are quarterly from 1995q1 to 2015q2.
  - Panel is unbalanced.
  - Lags chosen to minimize information criterion statistics.
  - System estimated using the GMM method.

### Annex III — Firm-level analysis using Orbis database (high-level)
- Data source: Orbis database by Bureau van Dijk (standardized company data; includes non-listed companies). Coverage uneven across countries; not all variables uniformly available.
- Regression analysis:
  - About half a million observations related to 200,000 firms.
  - Covers a sizable portion of national employment (see Table A3.1 in source).
  - Firm size categories: micro (1-10), small (10-50), medium (50-250), large (above 250) employees.
  - Tradable sector defined as agriculture, manufacturing, transportation and storage, IT and communication, and professional services.

*International Monetary Fund — CROSS-COUNTRY REPORT ON MINIMUM WAGES (content unit: _cr16151 - 46.)*

### 3.      The full regression results are reported in Tables A3.2 and A3.3.

### _cr16151 - 3.      The full regression results are reported in Tables A3.2 and A3.3.

### Table A3.2 — Determinants of Value-Added Exports in CESEE, 2000-13 (OLS regression of exports in value added)
- Relative Minimum Wage (minimum to mean wage, %):
  - Total: -13.341
  - Manufacturing: 0.390
  - Services: 0.911
  - Labor Intensive Manufacturing: -0.732**
  - Labor Intensive Services: -0.332*
- Supply Chain Integration Index:
  - Total: 0.738***; Manufacturing: 0.329***; Services: 0.352**; Labor Intensive Manufacturing: 0.0398; Labor Intensive Services: 0.0708
- Tertiary education attainment in labor force:
  - Total: 0.105; Manufacturing: -0.221***; Services: 0.227***; Labor Intensive Manufacturing: 0.000; Labor Intensive Services: -0.00451
- Real effective exchange rate:
  - Total: 0.0935; Manufacturing: 0.0357; Services: 0.175; Labor Intensive Manufacturing: 0.0471; Labor Intensive Services: -0.107
- GDP per capita:
  - Total: 0.000487***; Manufacturing: 0.000272**; Services: 3.47e-06; Labor Intensive Manufacturing: 4.92e-05; Labor Intensive Services: 0.000203**
- Constant terms:
  - Total: -46.68**; Manufacturing: -21.89; Services: -37.19**; Labor Intensive Manufacturing: -4.437; Labor Intensive Services: 12.32
- Sample and fit:
  - Observations: Total: 4545; Manufacturing: 4545; Services: 4545; Labor Intensive Manufacturing: 901; Labor Intensive Services: 135
  - R-squared: Total: 0.578; Manufacturing: 0.580; Services: 0.473; Labor Intensive Manufacturing: 0.139; Labor Intensive Services: 0.112
- Significance and inference:
  - *** p<0.01, ** p<0.05, * p<0.1
  - Errors are robust to country clustering

### Table A3.3 — Impact of Minimum Wage on Firms’ Employment, Wages, Productivity, and Profits, 2009-13 (Arellano-Bond dynamic two-step panel)
- Dependent variables across columns: ∆ Employment; ∆ Wage per employee; ∆ Productivity (1); ∆ Productivity (2); ∆ Capital to Labor Ratio; ∆ Profit margin
- Lagged dependent variable:
  - ∆ Employment: 1.525***; ∆ Wage per employee: 0.512***; ∆ Productivity (1): 0.326***; ∆ Productivity (2): 0.213***; ∆ Capital to Labor Ratio: -6.428; ∆ Profit margin: 0.020
- ∆ minimum wage:
  - ∆ Employment: -0.042***; ∆ Wage per employee: 0.127***; ∆ Productivity (1): -0.071***; ∆ Productivity (2): 0.049*; ∆ Capital to Labor Ratio: -63.313; ∆ Profit margin: -0.335*
- Interaction: ∆ minimum wage × Tradeable Sector:
  - ∆ Employment: -0.0173***; ∆ Wage per employee: 0.055***; ∆ Productivity (1): -0.018**; ∆ Productivity (2): 0.0082; ∆ Capital to Labor Ratio: 2.688; ∆ Profit margin: -0.490**
- Interaction: ∆ minimum wage × Small:
  - ∆ Employment: 0.0494; ∆ Wage per employee: -0.184***; ∆ Productivity (1): 0.073**; ∆ Productivity (2): -0.063**; ∆ Capital to Labor Ratio: 72.275; ∆ Profit margin: 0.190
- Tradeable Sector (dummy):
  - ∆ Employment: 0.406***; ∆ Wage per employee: -0.100***; ∆ Productivity (1): 0.035; ∆ Productivity (2): -0.060**; ∆ Capital to Labor Ratio: -74.992; ∆ Profit margin: 4.436
- Small (firms with fewer than 50 employees):
  - ∆ Employment: -2.624***; ∆ Wage per employee: 0.937***; ∆ Productivity (1): -0.599***; ∆ Productivity (2): 0.012; ∆ Capital to Labor Ratio: -689.996; ∆ Profit margin: -0.375**
- GDP Growth:
  - ∆ Employment: 0.037***; ∆ Wage per employee: 0.008***; ∆ Productivity (1): 0.022***; ∆ Productivity (2): 0.017***; ∆ Capital to Labor Ratio: 7.861; ∆ Profit margin: 1.224***
- Sample and instruments:
  - Observations: 755,776; 739,003; 751,787; 655,793; 754,998; 812,298 (by column)
  - Number of firms: 266,607; 260,948; 265,448; 237,967; 266,420; 267,214
  - Note: ∆ minimum wage, ∆ Employment, and ∆ Wage per employee are in percent. Other dependent variables are simple differences. Productivity (1) = operating revenue / employment. Productivity (2) = remuneration plus profits / employment (resembles gross value added per employee). Profit margin = EBITDA / operating revenue. Model estimated using Arellano-Bond dynamic two-step panel estimator with robust standard errors. Lagged dependent and independent variables used as instruments.
- Significance notation (note contains inconsistency in text):
  - Source: IMF staff calculations using annual firm level data in the ORBIS database for 11 CESEE countries.
  - "*** is significant at 1 percent; ** at 5 percent; * at 1 percent." (as printed in source)

### Annex IV — Methodology: Minimum Wages and Employment (pooled regressions, countries i over years t, 2000-14)
- Baseline regression specification:
  - Eit = a + b wit + d Xit + fi + gt + hit
  - E = employment rate of young workers (less than 25 years); w = minimum-to-average wage ratio (MW/AW); X includes adult unemployment and/or GDP growth, relative youth population; fi country-fixed effects; gt time-fixed effects.
- Key technical considerations:
  - Pooled regressions justified by roughly similar economic structures and variation in MW increases and cyclical conditions; potential omission of factors and endogeneity concerns noted.
  - MW/AW may reflect changes in average wages (AW) rather than MW; various specifications and controls tested within data limitations.
- Threshold, quadratic and alternative ratio specifications:
  - Threshold (tipping point) specifications estimated: Eit = a + b max(wit - w0, 0) + ... and Eit = a + b wit + c dummy_wit + ...
  - Quadratic specification estimated: Eit = a + b wit + c(wit)^2 + ...
  - Alternative metric MW/LP (minimum wage to labor productivity) used as conceptually direct measure; may raise identification issues, mitigated by controls for GDP growth.
- Empirical findings from Tables A4.1–A4.3:
  - Baseline MW/AW results (Table A4.1):
    - Column 1: significant negative coefficient on MW ratio implying youth employment elasticity of almost -0.3, but serial correlation present (Durbin-Watson low).
    - Column 2: adding lagged dependent variable lowers significance; elasticity ~ -0.1 (statistically significant at 15 percent).
    - Column 3: adding lagged GDP growth further reduces significance; elasticity ~ -0.05.
    - Adult employment regressions: implied elasticities insignificant and positive.
  - Quadratic MW/AW (Table A4.1 column 4):
    - Squared term has expected negative sign: stronger impact of MW increases on youth employment when initial MW/AW is higher.
    - Table A4.2 quantifies impacts: e.g., quadratic model implies a 5 percent MW increase when MW/AW = 45 percent would reduce youth employment by about 1 percent; impact < 0.5 percent when MW/AW = 35 percent.
    - Coefficients on MW and squared term are jointly statistically insignificant; results should be viewed with caution.
  - MW/LP specification (Tables A4.3):
    - Estimated MW impact using MW/LP is modestly negative and robustly statistically significant.
    - Coefficient on MW/LP remains statistically significant when additional controls introduced.
    - Adding MW/AW yields an insignificant coefficient and does not materially alter employment elasticity.
    - Regressions with real labor productivity or real GDP as separate controls did not alter main results (though worsened statistical properties).
    - Quadratic MW/LP (column 5) confirms earlier results; MW/LP and squared term jointly significant at 5 percent (F-statistic 3.68; p-value 0.03).
- Reported elasticities (from tables):
  - Table A4.1 — Youth employment elasticity: -0.28; -0.09; -0.06 (columns 1–3)
  - Adult employment elasticity (lower panel): 0.02; 0.03
  - Table A4.3 — Youth employment elasticity: -0.28; -0.15; -0.11; -0.09 (columns 1–4 as reported)
- Model diagnostics reported:
  - Representative Durbin-Watson statistics: 0.53; 1.60; 1.69; 1.7 (Table A4.1 columns 1–4)
  - R-squared values high in levels regressions (e.g., 0.84, 0.92, 0.93)
- Table A4.2 — Impact matrix (derived impacts on youth employment in percent):
  - Linear specification (derived from Table A4.1 column 3). Rows are MW increase (5, 10, 20 pct) and columns initial MW/AW levels (25, 30, 35, 40, 45, 50, 55, 60):
    - 5 pct: -0.21, -0.25, -0.29, -0.33, -0.37, -0.41, -0.45, -0.50
    - 10 pct: -0.41, -0.50, -0.58, -0.66, -0.74, -0.83, -0.91, -0.99
    - 20 pct: -0.83, -0.99, -1.16, -1.32, -1.49, -1.65, -1.82, -1.98
  - Quadratic specification (derived from Table A4.1 column 4):
    - 5 pct: -0.03, -0.19, -0.40, -0.66, -0.97, -1.33, -1.75, -2.21
    - 10 pct: -0.09, -0.42, -0.86, -1.40, -2.04, -2.79, -3.64, -4.60
    - 20 pct: -0.31, -1.03, -1.96, -3.11, -4.49, -6.07, -7.88, -9.91

### Annex V — Methodology: Wage and Income Distribution in Romania (EU-SILC based analysis; 2007–2016 projections)
- Data and scope:
  - EU-SILC data for Romania from INSSE, annual cross-sectional and longitudinal data 2007–2014; projections for 2015 and 2016 to capture sharp minimum wage hikes.
  - 2014 sample: 7,508 households; 15,661 persons interviewed; 2,499 unemployed; 13,162 employees or self-employed; study considers payroll-employed: 6,836 persons (~43.6 percent of the 2014 sample).
- Projection assumptions for 2015–2016:
  - Sub-minimum wage workers assumed to receive wage hikes at the growth rate of the minimum wage.
  - Workers at minimum wage immediately paid at the new minimum wage.
  - Workers above minimum wage receive raises as suggested by the estimated wage pass-through.
  - Estimated pass-through effect on gross wage for Romania: around 0.4 percent for a one percent increase of the minimum wage.
- Selected series from Table A5 (Romania: Wage and Income Distribution; 2007–2016 with proj for 2015–2016; values are annual unless noted):
  - MW annual income:
    - 2007: 4,680
    - 2008: 6,000
    - 2009: 7,200
    - 2010: 7,200
    - 2011: 8,040
    - 2012: 8,400
    - 2013: 9,000
    - 2014: 10,500
    - 2015 (proj): 12,150
    - 2016 (proj): 14,200
  - % change in MW annual income:
    - 2007: 18.2
    - 2008: 28.2
    - 2009: 20.0
    - 2010: 0.0
    - 2011: 11.7
    - 2012: 4.5
    - 2013: 7.1
    - 2014: 16.7
    - 2015 (proj): 15.7
    - 2016 (proj): 16.9
  - Gross wage annual:
    - 2007: 16,916
    - 2008: 20,906
    - 2009: 22,672
    - 2010: 23,240
    - 2011: 24,383
    - 2012: 25,606
    - 2013: 26,890
    - 2014: 28,320
    - 2015 (proj): 30,714
    - 2016 (proj): 33,601
  - % change in Gross wage annual:
    - 2007: 22.6
    - 2008: 23.6
    - 2009: 8.4
    - 2010: 2.5
    - 2011: 4.9
    - 2012: 5.0
    - 2013: 5.0
    - 2014: 5.3
    - 2015 (proj): 8.5
    - 2016 (proj): 9.4
  - Paid workers at or under MW:
    - 2007: 2,276
    - 2008: 2,139
    - 2009: 2,066
    - 2010: 1,970
    - 2011: 2,024
    - 2012: 2,041
    - 2013: 2,218
    - 2014: 2,409
    - 2015 (proj): 2,659
    - 2016 (proj): 3,161
  - Paid workers (total in sample considered):
    - 2007: 7,906
    - 2008: 7,339
    - 2009: 7,207
    - 2010: 7,154
    - 2011: 6,920
    - 2012: 6,841
    - 2013: 6,932
    - 2014: 6,836
    - 2015 (proj): 6,836
    - 2016 (proj): 6,836
  - Share of MW paid workers (percent of paid workers):
    - 2007: 28.8
    - 2008: 29.1
    - 2009: 28.7
    - 2010: 27.5
    - 2011: 29.2
    - 2012: 29.8
    - 2013: 32.0
    - 2014: 35.2
    - 2015 (proj): 38.9
    - 2016 (proj): 46.2
  - Median annual wage:
    - 2007: 8,013
    - 2008: 9,349
    - 2009: 10,997
    - 2010: 11,726
    - 2011: 12,081
    - 2012: 12,130
    - 2013: 12,432
    - 2014: 13,233
    - 2015 (proj): 14,065
    - 2016 (proj): 15,014
  - Median gross annual income (series ends with n.a. for projections 2015–2016):
    - 2007: 4,200
    - 2008: 5,317
    - 2009: 6,765
    - 2010: 7,736
    - 2011: 8,200
    - 2012: 8,371
    - 2013: 8,400
    - 2014: 8,798
    - 2015 (proj): n.a.
    - 2016 (proj): n.a.
  - Selected distributional percentiles and inequality measures (selected years):
    - 10th percentile wage: 2007: 900; 2008: 1,400; 2009: 1,650; 2010: 1,500; 2011: 1,600; 2012: 1,800; 2013: 1,750; 2014: 1,743; 2015 (proj): 2,016; 2016 (proj): 2,357
    - 90th percentile wage: 2007: 19,101; 2008: 21,347; 2009: 24,188; 2010: 24,387; 2011: 24,199; 2012: 24,462; 2013: 25,146; 2014: 27,226; 2015 (proj): 28,937; 2016 (proj): 30,890
    - Wage gap (top-to-bottom income): 2007: 21.2; 2008: 15.2; 2009: 14.7; 2010: 16.3; 2011: 15.1; 2012: 13.6; 2013: 14.4; 2014: 15.6; 2015 (proj): 14.4; 2016 (proj): 13.1
- Additional distributional statistics reported in Table A5 include skewness and kurtosis for wages and income, and income gap (90th to 20th percentiles); some series labeled n.a. for projected years.

*Source: IMF staff calculations using Orbis and EU-SILC/INSSE data as presented in the source content.*

### References

### References

### Empirical studies of minimum wage effects
- Abowd, J., F. Kramarz, T. Lemieux, and D. Margolis, 2009, Minimum Wages and Youth Employment in France and the United States, in: Blanchflower, D. and R. Freeman (eds.), Youth Employment and Joblessness in Advanced Countries.
- Andreica, M., L. Aparaschivei, A. Cristescu, and N. Cataniciu, 2010, Models of the Minimum Wage Impact upon Employment, Wages and Prices: the Romanian Case, Recent Advances in Mathematics and Computers in Business, Economics, Biology and Chemistry, Proceedings of the 11th WSEAS Int. Conf. MCBE 2010.
- Ashenfelter, O. and R. Smith, 1979, Compliance with the Minimum Wage Law, The Journal of Political Economy.
- Autor, D., A. Manning, and C. Smith, 2014, The Contribution of the Minimum Wage to U.S. Wage Inequality over Three Decades: A Reassessment, NBER Working Paper No. 16533.
- Baanante, M., 2005, Minimum Wage Effects under Endogenous Compliance: Evidence from Peru, Económica, Vol. 50.
- Baranowska-Rataj, A. and I. Magda, 2015, The Impact of Minimum Wages On job Separations and Hours of Work Among Young People in Poland, Institute of Statistics and Demography of the Warsaw School of Economics, Working Paper No. 45.
- Bell, L., 1997, The Impact of Minimum Wages in Mexico and Colombia, Journal of Labor Economics, 15(S3).
- Broecke, S., A. Forti, and M. Vandeweyer, 2015, The Effect of Minimum Wages on Employment in Emerging Economies: A Literature Review, http://nationalminimumwage.co.za/wp-content/uploads/2015/09/0221-Effect-of-Minimum-Wages-on-Employment-in-Emerging-Economies-A-Literature-Review.pdf.
- Brown, C., C. Gilroy, and A. Kohen, 1982, The Effect of the Minimum Wage on Employment and Unemployment, Journal of Economic Literature, 20(2).
- Card, D. and A. Krueger, 1994, The Effect of the Minimum Wage on Shareholder Wealth, Princeton University, Department of Economics, Industrial Relations Section, Working Paper No. 337.
- Card, D. and A. Krueger, 2015, Myth and Measurement, Princeton University Press.
- Dickens, R. and A. Manning, 2004, Spikes and Spillovers: the Impacts of the National Minimum Wage on the Wage Distribution in a Low-Wage Sector, The Economic Journal, 114 (March).
- Draca, M., S. Machin, and J. Van Reenen, 2011, Minimum Wages and Firm Profitability, American Economic Journal: Applied Economics, 3.
- Eriksson, T. and M. Pytlikova, 2004, Firm-level Consequences of Large Minimum-wage Increases in the Czech and Slovak Republics, Labour 18(1).
- Goretti, M., 2008, Wage-Price Setting in New EU Member States, IMF Working Paper WP/08/243, https://www.imf.org/external/pubs/ft/wp/2008/wp08243.pdf.
- Harasztosi, P. and A. Lindner, 2015, Who Pays for the Minimum Wage? http://faculty.chicagobooth.edu/workshops/micro/pdf/LindnerJMP.pdf.
- Hinnosar, M. and T. Rõõm, 2003, The Impact of Minimum Wage on the Labour Market in Estonia: An Empirical Analysis, Eesti Bank Working Papers of No. 8.
- Kertesi, G. and J. Köllõ, 2003, Fighting “Low Equilibria” by Doubling the Minimum Wage? Hungary’s Experiment, IZA DP (Bonn, IZA), No. 970.
- Lemos, S., 2004, Minimum Wage Policy and Employment Effects: Evidence from Brazil, Economia, 5(1).
- Lemos, S., 2008, A Survey of the Effects of the Minimum Wage on Prices, Journal of Economic Surveys, 22(1).
- Maloney, W. and J. Mendez, 2004, Measuring the Impact of Minimum Wages—Evidence from Latin America, in: Heckman, J. and C. Pages (eds.), Law and Employment: Lessons from Latin American and the Caribbean, University of Chicago Press.
- Neumark, D., 2015, Reducing Poverty via Minimum Wages—Alternatives, FRBSF Economic Letter, No. 2015-38, December 2015.
- Neumark, D., J. Salas, and W. Wascher, 2013, Revisiting the Minimum Wage-Employment Debate: Throwing Out the Baby with the Bathwater? IZA DP No. 7166.
- Neumark, D., M. Schweitzer, and W. Wascher, 2005, The Effects of Minimum Wages on the Distribution of Family Incomes: A Non-Parametric Analysis, Journal of Human Resources, Vol. 40, No. 4 (Autumn 2005).
- Neumark, D. and W. Wascher, 2003, Minimum Wages, Labor Market Institutions, and Youth Employment: A Cross-National Analysis, Federal Reserve Finance and Economics Discussion Paper Series, No. 23.
- Rama, M., 2001, The Consequence of Doubling the Minimum Wage: the Case of Indonesia, Industrial and Labor Relations Review, Vol.54, No.4.
- Rutkowski, J., 2003, The Minimum Wage: Cure or Curse?, Human Development Economics, Europe and Central Asia Region, The World Bank, http://siteresources.worldbank.org/INTECONEVAL/Resources/MinimumWageNoteJul03v2.pdf.
- Schmitt, J., 2013, Why Does the Minimum Wage Have No Discernible Effect on Employment? Center for Economic and Policy Research.
- Wadsworth, J., 2010, Did the National Minimum Wage Affect UK Prices? Fiscal Studies, 31(1).

### Reviews, meta-analyses, and literature syntheses
- Betcherman, G., 2013, Labor Market Institutions: A Review of the Literature, Background Paper for the World Development Report 2013, http://siteresources.worldbank.org/EXTNWDR2013/Resources/8258024-1320950747192/8260293-1320956712276/8261091-1348683883703/WDR2013_bp_Labor_Market_Institutions.pdf.
- Doucouliagos, H. and T. Stanley, 2009, Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis, British Journal of Industrial Relations, Vol. 47, No. 2.
- Giotis, G. and M. Chletsos, 2015, Is There Publication Selection Bias in Minimum Wage Research During the Five-year Period from 2010 to 2014? Economics, Discussion Paper No. 2015-58.
- Schmitt, J., 2013, Why Does the Minimum Wage Have No Discernible Effect on Employment? Center for Economic and Policy Research.
- Vaughn-Whitehead, D., 2010, The Minimum Wage Revisited in the Enlarged EU, Edward Elgar.

### Country reports, national analyses, and institutional reports
- Bank of Estonia, 2015, Labor Market Review, 2/2015, http://www.eestipank.ee/en/publication/labour-market-review/2015/labour-market-review-22015.
- Bank of Lithuania, 2015, The Effect of Minimum Wage Increase on Macroeconomic Variables: Survey Results of Lithuanian Enterprises, Lithuania Economic Review, December 2015, https://www.lb.lt/lithuanian_economic_review.
- Bernhardt, A., R. Milkman, D. Theodore, M. Heckathorn, M. Auer, J. DeFilippis, and M. Spiller, 2010, Broken Laws, Unprotected Workers, National Employment Law Project, New York: NELP.
- European Trade Union Confederation, 2015, Discussion Note on Minimum Wages in Europe, Warsaw Seminar of the ETUC Collective Bargaining Committee, http://www.ftf.dk/fileadmin/Billedbase/Ledelse/DISCUSSION_NOTE_WARSAW_CONFERENCE_final_EN.pdf.
- Fadejeva, L. and O. Krasnopjorovs, 2015, Labour Market Adjustment During 2008-2013 in Latvia: Firm level Evidence, Bank of Latvia Working Paper No. 2/2015.
- Fialová, K. and M. Mysíková, 2009, Minimum Wage: Labour Market Consequences in the Czech Republic, Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Working Paper: 6/2009.
- Igan, D., 2014, More Jobs That Pay Decent Wages: How To Fight Poverty in The United States, iMFdirect blog, August 2014, https://blog-imfdirect.imf.org/2014/08/28/more-jobs-that-pay-decent-wages-how-to-fight-poverty-in-the-united-states/.
- IMF, 2011, Colombia—Staff Report for the 2011 Article IV Consultation, IMF Country Report 11/224, https://www.imf.org/external/pubs/cat/longres.aspx?sk=25113.0.
- IMF, 2013, France—Staff Report for the 2013 Article IV Consultation, IMF Country Report 13/251, https://www.imf.org/external/pubs/cat/longres.aspx?sk=40854.0.
- IMF, 2014a, It Takes Two to Tango: Wages and Productivity in Lithuania, IMF Country Report No. 15/139, https://www.imf.org/external/pubs/ft/scr/2015/cr15139.pdf.
- IMF, 2014b, Youth Unemployment in Advanced Economies in Europe: Searching for Solutions, IMF Staff Discussion Note, SDN/14/11, https://www.imf.org/external/pubs/ft/sdn/2014/sdn1411.pdf.
- IMF, 2014c, Germany—Staff Report for the 2014 Article IV Consultation, IMF Country Report No.14/216, https://www.imf.org/external/pubs/ft/scr/2014/cr14216.pdf.
- IMF, 2014d, United States—Staff Report for the 2014 Article IV Consultation, IMF Country Report No.14/221, https://www.imf.org/external/pubs/ft/scr/2014/cr14221.pdf.
- IMF, 2015, United States—Staff Report for the 2015 Article IV Consultation, IMF Country Report 15/168, https://www.imf.org/external/pubs/cat/longres.aspx?sk=43053.0.
- IMF, 2016, Inequality and Income Distribution in Lithuania International Comparison: Trends, Causes, and Politics, Selected Issues Paper for the 2016 Article IV Consultation with the Republic of Lithuania.
- Iordache, Ș., M. Militaru, and M. Pandioniu, 2016, Jobless Recovery in Romania: the Role of Sticky Wages and Other Frictions—Firm-level Evidence from the WDN Survey, National Bank of Romania Occasional Papers No. 20.
- National Bank of Romania, 2015, The Increase in the Minimum Gross Wage—Effects on the Labour Market, Inflation Report, May 2015, http://www.bnr.ro/files/d/Pubs_en/InflationReport/2015/IR_201505.pdf.
- Riley, R. and C. Bondibene, 2015, The Impact of the National Minimum Wage on UK Businesses—Report to the Low Pay Commission.
- Rizov, M. and R. Croucher, 2011, The Impact of the UK National Minimum Wage on Productivity by Low-paying Sectors and Firm-size Groups, Research Report for the Low Pay Commission.
- Schnattinger, P., N. Jemec, M. Lozej, M. Vodopivec, and P. Mohorič Peterne, 2015, Results of the 2014 Wage Dynamics Network Survey in Slovenia, Bank of Slovenia, https://www.bsi.si/library/includes/datoteka.asp?DatotekaId=6139.

### Compliance, enforcement, and informal economy
- Bernhardt, A., R. Milkman, D. Theodore, M. Heckathorn, M. Auer, J. DeFilippis, and M. Spiller, 2010, Broken Laws, Unprotected Workers, National Employment Law Project, New York: NELP.
- Milkman, R., A. González, and V. Narro, 2010, Workplace Violations in Los Angeles—The Failure of Employment and Labor Law for Low-wage Workers, UCLA Institute for Research on Labor and Employment, http://www.irle.ucla.edu/publications/documents/LAwagetheft-Milkman-Narro-110.pdf.
- Putniņš, T. and A. Sauka, 2015, Measuring the Shadow Economy Using Company Managers, Journal of Comparative Economics, 43(2).
- Schneider, F., 2015, Size and Development of the Shadow Economy of 31 European and 5 Other OECD Countries from 2003 to 2015: Different Developments, http://www.econ.jku.at/members/Schneider/files/publications/2015/ShadEcEurope31.pdf.
- SEE Riga, 2015, Shadow Economy Index for the Baltic Countries, 2009-2014, The Center for Sustainable Business at SEE Riga, http://www.sseriga.edu/en/centres/csb/shadow-economy-index-for-baltics.
- Williams, C., 2009, The Commonality of Envelope Wages in Eastern European Economies, Eastern European Economics, vol. 47(2).
- Žukauskas, V., 2015, Shadow Economies in the Baltic Region, Lithuanian Free Market Institute, http://www.llri.lt/wp-content/uploads/2015/11/Shadow-Economies-in-a-Baltic-Sea-Region.pdf.

### Legal, institutional, and policy perspectives
- Council of Europe, 1996, European Social Charter (revised), https://rm.coe.int/CoERMPublicCommonSearchServices/DisplayDCTMContent?documentId=090000168007cf93.
- Council of Europe, 2008, Digest of the Case Law of the European Committee of Social Rights, http://www.coe.int/en/web/turin-european-social-charter/case-law.
- G20, 2012, Boosting Jobs and Living Standards in G20 Countries, A Joint Report by the ILO, OECD, IMF, and the World Bank, June 2012, http://www.ilo.org/global/publications/books/WCMS_183705/lang--en/index.htm.
- Low Pay Commission, 2014, National Minimum Wage: Low Pay Commission Report, https://www.gov.uk/government/publications/national-minimum-wage-low-pay-commission-report-2014.
- The United States Congressional Budget Office, 2014, The Effects of a Minimum Wage Increase on Employment and Family Income, Publication No. 4856.

### Sectoral, price, and productivity effects
- Card, D. and A. Krueger, 1994, The Effect of the Minimum Wage on Shareholder Wealth, Princeton University, Department of Economics, Industrial Relations Section, Working Paper No. 337.
- Draca, M., S. Machin, and J. Van Reenen, 2011, Minimum Wages and Firm Profitability, American Economic Journal: Applied Economics, 3.
- Goretti, M., 2008, Wage-Price Setting in New EU Member States, IMF Working Paper WP/08/243, https://www.imf.org/external/pubs/ft/wp/2008/wp08243.pdf.
- Lemos, S., 2008, A Survey of the Effects of the Minimum Wage on Prices, Journal of Economic Surveys, 22(1).
- Rizov, M. and R. Croucher, 2011, The Impact of the UK National Minimum Wage on Productivity by Low-paying Sectors and Firm-size Groups, Research Report for the Low Pay Commission.
- Wadsworth, J., 2010, Did the National Minimum Wage Affect UK Prices? Fiscal Studies, 31(1).

*Cross-Country Report on Minimum Wages — References (IMF).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16151.pdf_