## wpiea2020059-print-pdf

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---

### Introduction and motivation
- Calls to introduce or raise the minimum wage (MW) have grown amid weak wage growth in advanced countries, attributed to technological change, higher market power by companies, declining unionization, and globalization (see IMF, 2017a).
- Recent policy moves and debates:
  - Germany introduced a MW in 2015.
  - Spain, the U.K., and some U.S. states have raised their MW more aggressively in recent years.
  - A new statutory MW in Italy is being debated.
  - Central and Eastern Europe: Lithuania, Bulgaria and Czech Republic have recently raised their MW sharply (IMF, 2016).
- Political context in the EU:
  - In 2017, the umbrella organization of European trade unions advocated a harmonized EU MW set at 60 percent of the national median or 50 percent of the national average wage (WSI, 2019).
  - The UK exited the EU on January 31, 2020 and is not included in the analysis.
  - In January 2020 the European Commission launched a consultation with social partners on actions to ensure fair MWs in all EU countries; the initiative is not limited to statutory MWs and will not propose harmonization of MW policies and levels.

### Policy framing: absolute vs. relative harmonization
- Two harmonization approaches:
  - Absolute harmonization: set the same nominal MW across countries — considered unrealistic and undesirable given large differences in average wages and productivity.
  - Relative harmonization: set the MW as the same percentage of a country-specific reference wage (Kaitz index), anchoring MW to domestic wage levels.
- Preferred benchmark in the paper:
  - Focus on Kaitz indexes using the median wage; median is preferred because it is less affected by top-earning outliers.
  - The European Trade Union confederation proposed 60 percent of the median or 50 percent of the average wage; the paper focuses on 60 percent of median.
- Trade-offs and risks of relative harmonization:
  - Would widen nominal MW differences across countries (lower-income vs. higher-income EU members).
  - May reinforce incentives for low-skill workers to migrate from lower to higher income EU countries.
  - May increase incentives to circumvent MW regulation where MW becomes more binding, raising non-compliance unless monitoring/enforcement is strengthened.
  - Could weaken labor market performance in less productive regions within countries where MW is uniform nationally.
  - Relative harmonization may not reduce cross-country poverty differences, since low Kaitz ratios are not necessarily in countries with the largest share of vulnerable households.

### Stylized cross-country MW facts
- Reported MW variation (2018 unless otherwise noted):
  - Nominal MW range: €260 per month in Bulgaria to around €2,000 in Luxembourg.
  - Some countries have no statutory MW (Austria, Cyprus, Denmark, Finland, Italy, Sweden); sectoral minima set by collective bargaining.
  - Adjusted for purchasing power, MWs remain heterogeneous; high-income countries (Luxembourg, Netherlands, Belgium, Germany) have much higher MWs than lower-income countries (Bulgaria, Romania, Latvia).
- Data and timing notes:
  - 2018 data are used rather than 2019 because median wage data are not available for 2019.
  - For Spain the estimated Kaitz ratio for 2019 is reported because of a substantial MW increase that year.
  - Table B7 in the Appendix summarizes data sources (appendix content referenced in-source).

### Effects on poverty — definitions, methodology, and baseline poverty
- Poverty definition:
  - A household is poor if its equivalized disposable income is below 60 percent of the median equivalized disposable household income in the same country in a given year (in line with European Council, 2001).
- Baseline poverty statistics:
  - The average poverty rate in the microsimulation data is around 16 percent, ranging from 7 percent in Czech Republic to 22 percent in Spain.
- Decomposition of MW effects on poverty:
  - Direct effects: impact of MW change on household wage earnings, holding other factors constant.
  - Indirect effects: behavioral responses (e.g., reduced labor demand), general equilibrium effects (e.g., higher prices), loss of means-tested social insurance benefits, higher non-compliance.
  - Indirect effects typically reduce the overall impact of MW increases on poverty; therefore the baseline microsimulation of direct effects is an upper bound.
- Microsimulation approach:
  - Uses EU-SILC micro-level survey data for 2016 to compute the direct effect of harmonized MWs on earnings of poor households and the overall poverty rate.
  - In each country the simulation computes how the new MW affects earnings of poor households and the overall poverty rate, ignoring behavioral and macroeconomic adjustments.

### Data and methodology (microsimulation)
- Data source and sample:
  - The micro-simulation uses the latest available round of the EU-SILC survey (2016) after data cleaning.
  - After excluding six countries without a national minimum wage (MW) and five countries with a MW above the EMW, the dataset has around 106,814 observations from 16 EU member states.
  - Sample size ranges from less than 4,000 observations in Malta to around 10,000 observations for Germany.
- Policy experiment:
  - Simulate relative harmonization of the MW to 60 percent of the median (EMW) using 2016 data.
  - Countries with current MWs at or above the EMW in 2016 (including France, Hungary, Portugal, Poland, and Romania) are omitted from the simulation (assumed no national MW decrease).
- Key simulation assumptions:
  - Workers earning the MW or less experience the same proportional wage increase as the MW increase.
  - Wage spillovers: baseline assumes wages of workers earning up to 75 percent of the median wage are affected, with the effect decreasing linearly and reaching zero above that threshold.
  - MW perimeter is held constant in the baseline (no expansion of coverage or enforcement changes).
- Monthly wage computation:
  - Annual gross labor income (EU-SILC variable PY010G) is divided by full time equivalent months, constructed from PL073, PL074 and median hours (PL060) using cross-sectional person weights (PB040).
  - Simulations consider only wages for employees; wages of self-employed workers are assumed unchanged.
  - Employees and self-employed are defined based on EU-SILC variable PL031.
- Spillover parameter β (as specified in source):
  - Wages below current MW rise proportionally; spillovers extend from the current MW up to 1.25 × EMW (75 percent of the median), with β defined piecewise as in the source expression.

### Baseline simulation findings — MW increases and household impacts
- Largest required MW increases (2016 basis):
  - Czech Republic: increase would have been almost 50 percent (largest percent increase).
  - Netherlands: increase of some 30 percent (almost 500 euro per month) (largest nominal increase).
- Countries with no simulated increase in MW in 2016: France, Hungary, Portugal, Poland, Romania.
- Household composition matters for direct poverty effects:
  - Share of poor households without any low-wage workers ranges from 56 percent in Luxembourg to almost 91 percent in Croatia.
  - Share of non-poor households that would benefit from the EMW ranges from about 5 percent in Greece to as high as 18 percent in Estonia.
- Effects on poor households’ incomes and poverty:
  - Estonia: MW increase of 34 percent would translate into an average increase in the income of poor households of 6 percent, implying an elasticity of 0.18.
  - Average elasticity of poor households’ income to the MW increase across all countries is 0.12.
  - Country elasticities: Belgium 0.07; Luxembourg 0.27.
  - Baseline poverty impact: average reduction of 0.5 percentage points in poverty rate across countries; largest fall of 1.3 percentage points in Estonia and smaller declines elsewhere.
  - The decline in poverty is not strongly correlated with the size of the MW increase but is positively correlated with the share of primary earners who earn the MW.
- In-work poverty:
  - Defined as fraction of individuals working full time in dependent employment living in poor households.
  - In-work poverty levels vary from 4 percent in the Czech Republic to 15 percent in Luxembourg.
  - EMW reduces in-work poverty more sharply than overall poverty; example: in Spain the decline in in-work poverty in the baseline microsimulation is twice as large as the decline in overall poverty.

### Indirect effects and risks that reduce net poverty gains
- Dis-employment effects:
  - Employment elasticity scenario: using 0.25 employment elasticity (i.e., 10 percent MW increase → 2.5 percent higher probability of becoming unemployed for affected workers), the average decline in poverty shrinks from 0.5 to 0.1 percentage points.
  - Under the 0.25 elasticity simulation, poverty would actually increase in some countries: Czech Republic, Malta, Lithuania, and the Netherlands.
  - Note: employment elasticities may vary by time horizon, worker demographics, country, and size of MW change.
  - CBO (2019) context: 0.25 chosen as the median short-term elasticity in U.S. literature; CBO median estimated elasticity for the longer term is 1.5; Hungary estimate cited is 0.15.
- Price pass-through:
  - Hungary study (Harasztosi and Lindner, 2019): about 75 percent of MW cost passed on to consumers and 25 percent absorbed by lower profits, with stronger pass-through in less competitive sectors (nontraded services).
  - IMF (2019c) indicates pass-through from wages to prices has slowed in Europe in recent years, especially in sectors exposed to international competition.
  - Price increases can erode real income gains, particularly if poor households disproportionately consume goods from high-MW sectors.
- Loss of means-tested benefits:
  - Higher earnings from MW increases can reduce eligibility for means-tested social insurance benefits, potentially eroding net income gains for some poor households.
  - Quantification of this effect depends on complex, country-specific social insurance rules and is not attempted here.
- Evasion, non-compliance, and perimeter shrinkage:
  - More binding MWs may induce employers to overreport wages, underreport hours, or increase informal employment to circumvent regulation.
  - Increased informality/contract work would reduce worker protections and net incomes.
  - Stronger monitoring and enforcement would be needed to avoid circumvention as MW becomes more binding.

### Data-adjusted alternative scenarios and sensitivity
- Less ambitious target: setting MW at 50 percent of the median would limit MW increases (maximum increase of 8 percent noted in the source) but would leave some countries well above the threshold.
- Employment-disemployment scenario:
  - With a 0.25 employment elasticity, average poverty reduction falls from 0.5 to 0.1 percentage points; in some countries poverty increases.
- Spillover sensitivity:
  - Baseline assumes spillovers up to 75 percent of median; results depend on the spillover parameter β as defined in the methodology.

### Effects on euro-area rebalancing — empirical macro findings
- Macro econometric panel approach shows increases in the MW are robustly associated with subsequent increases in unit labor costs (ULCs) after controlling for other factors, implying MW changes can affect cost competitiveness.
- Regression (IV with country and time fixed effects) cumulative coefficients for log Kaitz index on log ULCs (years 0–5):
  - Year 0: 0.178**
  - Year 1: 0.516***
  - Year 2: 0.850***
  - Year 3: 0.962***
  - Year 4: 0.779***
  - Year 5: 0.496***
- Transmission channels (IV estimates):
  - Compensation of employees main channel: Year 0: 0.137**; Year 1: 0.525***; Year 2: 0.744**; Year 3: 0.666***; Year 4: 0.427**; Year 5: 0.399**
  - Labor productivity effects: negative and intermittently significant in medium term (Year 4: -0.404*; Year 5: -0.448*)
  - Total employment: modest negative cumulative responses becoming significant by years 4–5 (Year 4: -0.291*; Year 5: -0.454**)

### Simulated impacts of a coordinated EMW at 60 percent of median (rebalancing exercise)
- Method:
  - Use IV baseline ULC response to compute medium-term ULC changes for each EA country after harmonizing MW to 60 percent of median.
  - Convert simulated REER changes into current account (CA) changes using CA-REER elasticities from the IMF ESR.
- Baseline simulated CA impacts (medium term, cumulative over five years):
  - Deficit EA countries: reduce negative current account gaps by around 0.1–0.3 percentage points of GDP.
  - Surplus EA countries: reduce positive current account gaps by around 0.6–2 percentage points of GDP.
  - Context: latest staff-assessed CA gaps are around -0.5 and 1.8 percent of GDP for deficit and surplus EA countries, respectively.
- Aggregate and country-group effects:
  - Implied medium-term cumulative REER appreciations in surplus countries is around 5 percent on average (or between 1-2 standard deviations of the observed medium-term historical changes in the REER).
  - The magnitude of medium-term cumulative CA adjustments is around 2 percentage points of GDP on average (in line with past experience or between 1 and 2 standard deviations of medium-term changes in the current account).
  - About 85 percent of the rebalancing would come from adjustments by the surplus countries or net creditor countries.
- Notable country-specific simulated outcomes:
  - Germany would need to increase its Kaitz ratio by 14½ percentage points, while its CA gap is assessed at 4½ percent.
  - Surplus countries with lowered CA gaps include Germany, the Netherlands, and Estonia.
  - Deficit countries that would see improvement due to trading-partner MW increases include Cyprus, Portugal, France, and Italy.
  - Deficit countries that would experience further deterioration: Spain, Greece, and Belgium.
  - Belgium’s loss in competitiveness appears particularly sizeable, at around 2 standard deviations of its past REER changes.
  - Slovakia and Ireland (minor imbalances) would experience deterioration in the simulation.
- Dynamics:
  - Rebalancing impacts become more evident by year 3 and peak around year 3, then taper off.
  - Near-term (years 1–2) effects are smaller and can be close to zero under some regression specifications.

### Regression robustness, identification, and limitations
- Endogeneity and instruments:
  - Instrumental variables: political orientation of largest government party (left/center/right), number of years left in executive term, vote share of largest government party, and the two-year lag of the Kaitz index.
  - First-stage F-statistics reported: 30.71 (IV with country-specific time trends), 13.35 (IV with country and time fixed effects).
  - Over-identification test P-values (IV with country-specific trends): 0.655, 0.394, 0.658, 0.333, 0.849, 0.339 (years 0–5).
- Alternative measures and checks:
  - Using log level of the minimum wage (in euros) yields broadly similar ULC responses (IV estimates Year 0: 0.100***; Year 1: 0.207***; Year 2: 0.396***; Year 3: 0.438***; Year 4: 0.251**; Year 5: -0.00747).
  - Interaction with Low Pay (share of workers earning < 65% of median) indicates stronger medium-term impacts when the low-pay sector is large.
  - CPI-based REER and GVC-weighted trade weights produce qualitatively similar rebalancing results.
- Model and simulation limitations:
  - Reduced-form regression subject to omitted variables, potential endogeneity bias, and robustness concerns despite IVs.
  - Simulations may not fully capture general equilibrium effects or changes in compliance/non-linearity associated with large MW increases.
  - Simulations ignore changes in wages and ULCs in non-EA EU trading partners.
  - Coefficients converting ULC-based REER changes into CPI-based REER changes are historical and may overestimate pass-through today.
  - Simulations do not capture aggregate savings and investment effects that do not operate through the REER.
  - External adjustment driven by MW increases may be accompanied by lower productivity and long-term output in surplus countries via lower investment, compressed profit margins, and weaker incentives for skill upgrading.

### Design recommendations and policy implications
- If EU countries pursue harmonization, design considerations include:
  - Prefer relative harmonization (Kaitz index) over absolute nominal harmonization to account for cross-country productivity and living standard differences.
  - Anchor MW to the median wage (e.g., 60 percent of median) rather than the average wage, given median’s robustness to outliers.
  - Implement harmonization gradually to avoid disruptive large MW increases in countries far from the benchmark.
  - Complement harmonization with improved monitoring and enforcement to reduce non-compliance where MWs become more binding.
  - Account for regional within-country disparities in productivity when setting a uniform national MW to avoid weakening less productive regions.
  - Recognize that MW harmonization alone is unlikely to solve poverty for households without employed members; broader social protection and labor market measures remain important.
- Alternative or complementary instruments:
  - Job training and life-long learning programs to increase access to better-paid jobs and human capital.
  - Well-targeted social benefits for the most vulnerable households.
  - In-work tax credits to support incomes of the working poor while incentivizing employment (noting fiscal cost and potential partial capture by employers).
- Enforcement and perimeter:
  - To maximize poverty-reducing effects, accompany MW increases with measures to broaden and enforce MW coverage (address measurement error, exemptions, and compliance).

*Source: Excerpts and excerpts synthesis from the provided IMF working paper chapter and appendices (wpiea2020059-print-pdf).*

### REFERENCES .............................................................................................................

### wpiea2020059-print-pdf - REFERENCES .............................................................................................................

### Introduction and motivation
- Calls to introduce or raise the minimum wage (MW) have grown amid weak wage growth in advanced countries, attributed to technological change, higher market power by companies, declining unionization, and globalization (see IMF, 2017a).
- Recent policy moves and debates:
  - Germany introduced a MW in 2015.
  - Spain, the U.K., and some U.S. states have raised their MW more aggressively in recent years.
  - A new statutory MW in Italy is being debated.
  - Central and Eastern Europe: Lithuania, Bulgaria and Czech Republic have recently raised their MW sharply (IMF, 2016).
- Political context in the EU:
  - In 2017, the umbrella organization of European trade unions advocated a harmonized EU MW set at 60 percent of the national median or 50 percent of the national average wage (WSI, 2019).
  - The UK exited the EU on January 31, 2020 and is not included in the analysis.
  - In January 2020 the European Commission launched a consultation with social partners on actions to ensure fair MWs in all EU countries; the initiative is not limited to statutory MWs and will not propose harmonization of MW policies and levels.

### Policy framing: absolute vs. relative harmonization
- Two harmonization approaches:
  - Absolute harmonization: set the same nominal MW across countries — considered unrealistic and undesirable given large differences in average wages and productivity.
  - Relative harmonization: set the MW as the same percentage of a country-specific reference wage (Kaitz index), anchoring MW to domestic wage levels.
- Preferred benchmark in the paper:
  - Focus on Kaitz indexes using the median wage; median is preferred because it is less affected by top-earning outliers.
  - The European Trade Union confederation proposed 60 percent of the median or 50 percent of the average wage; the paper focuses on 60 percent of median.
- Trade-offs and risks of relative harmonization:
  - Would widen nominal MW differences across countries (lower-income vs. higher-income EU members).
  - May reinforce incentives for low-skill workers to migrate from lower to higher income EU countries.
  - May increase incentives to circumvent MW regulation where MW becomes more binding, raising non-compliance unless monitoring/enforcement is strengthened.
  - Could weaken labor market performance in less productive regions within countries where MW is uniform nationally.
  - Relative harmonization may not reduce cross-country poverty differences, since low Kaitz ratios are not necessarily in countries with the largest share of vulnerable households.

### Stylized cross-country MW facts (from the source)
- Reported MW variation (2018 unless otherwise noted):
  - Nominal MW range: €260 per month in Bulgaria to around €2,000 in Luxembourg.
  - Some countries have no statutory MW (Austria, Cyprus, Denmark, Finland, Italy, Sweden); sectoral minima set by collective bargaining.
  - Adjusted for purchasing power, MWs remain heterogeneous; high-income countries (Luxembourg, Netherlands, Belgium, Germany) have much higher MWs than lower-income countries (Bulgaria, Romania, Latvia).
- Data and timing notes:
  - 2018 data are used rather than 2019 because median wage data are not available for 2019.
  - For Spain the estimated Kaitz ratio for 2019 is reported because of a substantial MW increase that year.
  - Table B7 in the Appendix summarizes data sources (appendix content referenced in-source).

### Effects on poverty — key findings and methodology
- Definitions and baseline poverty:
  - A household is defined as poor if its equivalized disposable income is below 60 percent of the median equivalized disposable household income in the same country in a given year (in line with European Council, 2001).
  - The average poverty rate in the microsimulation data is around 16 percent, ranging from 7 percent in Czech Republic to 22 percent in Spain.
- Decomposition of MW effects on poverty:
  - Direct effects: impact of MW change on household wage earnings, holding other factors constant.
  - Indirect effects: behavioral responses (e.g., reduced labor demand), general equilibrium effects (e.g., higher prices), loss of means-tested social insurance benefits, higher non-compliance.
  - Indirect effects typically reduce the overall impact of MW increases on poverty; therefore the baseline microsimulation of direct effects is an upper bound.
- Microsimulation approach:
  - Uses EU-SILC micro-level survey data for 2016 to compute the direct effect of harmonized MWs on earnings of poor households and the overall poverty rate.
  - In each country the simulation computes how the new MW affects earnings of poor households and the overall poverty rate, ignoring behavioral and macroeconomic adjustments (see Technical Appendix A for details in the source).

### Main simulation insights on poverty
- Harmonizing MW to 60 percent of the median:
  - Would benefit the working poor but the effect on the overall poverty rate is likely small.
  - Many poor households either have no members in dependent employment or are self-employed and thus do not directly benefit from higher MW.
  - While in-work poor gain, the share of non-working or self-employed poor households limits the aggregate poverty reduction from MW harmonization.
  - Behavioral responses (reduced labor demand, withdrawal of means-tested benefits, avoidance/non-compliance) and macroeconomic effects (higher consumer prices from pass-through) could further reduce or even reverse the direct beneficial effect on overall poverty.
  - The higher MW would have a more pronounced direct effect on in-work poverty (relative poverty rate of people in work).

- Alternative, less ambitious target:
  - Setting MW at 50 percent of the median would limit MW increases (maximum increase of 8 percent noted in the source) but would leave some countries well above the threshold.

### Effects on external rebalancing within the Euro Area (EA)
- Empirical macro finding:
  - A macro econometric panel approach shows increases in the MW are robustly associated with subsequent increases in unit labor costs (ULCs) after controlling for other factors, implying MW changes can affect cost competitiveness.
- Rebalancing implications:
  - Harmonizing toward a higher MW could allow for some overall external rebalancing within the EA:
    - Countries with an external position that is too strong (e.g., Germany, the Netherlands) would see their position weaken, aiding rebalancing.
    - Some countries with weak external positions would experience improvements.
    - However, some countries with already weak external positions (e.g., Spain, Greece, Belgium) would see their situation worsen further.
- Simulation and robustness:
  - Appendix B contains regression analysis on the impact of MW changes on ULCs, transmission channels (main channel is average compensation of employees), and country-specific rebalancing simulation exercises (appendix tables and figure listings provided in the source).

### Design recommendations and cautions
- If EU countries pursue harmonization, design considerations include:
  - Prefer relative harmonization (Kaitz index) over absolute nominal harmonization to account for cross-country productivity and living standard differences.
  - Anchor MW to the median wage (e.g., 60 percent of median) rather than the average wage, given median’s robustness to outliers.
  - Implement harmonization gradually to avoid disruptive large MW increases in countries far from the benchmark.
  - Complement harmonization with improved monitoring and enforcement to reduce non-compliance where MWs become more binding.
  - Account for regional within-country disparities in productivity when setting a uniform national MW to avoid weakening less productive regions.
  - Recognize that MW harmonization alone is unlikely to solve poverty for households without employed members; broader social protection and labor market measures remain important.

*Source: Excerpts from the referenced IMF working paper chapter and appendices (content supplied).*

### 14.      The micro-simulation is based on the latest wave of the EU-SILC database, a

### 14.      The micro-simulation is based on the latest wave of the EU-SILC database, a

### Data and methodology
- Data source and sample
  - The micro-simulation uses the latest available round of the EU-SILC survey (2016) after data cleaning.
  - After excluding six countries without a national minimum wage (MW) and five countries with a MW above the EMW, the dataset has around 106,814 observations from 16 EU member states.
  - Sample size ranges from less than 4,000 observations in Malta to around 10,000 observations for Germany.
- Policy experiment
  - Simulate relative harmonization of the MW to 60 percent of the median (EMW) using 2016 data.
  - Countries with current MWs at or above the EMW in 2016 (including France, Hungary, Portugal, Poland, and Romania) are omitted from the simulation (assumed no national MW decrease).
- Key simulation assumptions
  - Workers earning the MW or less experience the same proportional wage increase as the MW increase (e.g., if MW rises 10 percent, all workers earning the MW or less receive a 10 percent wage increase).
  - Wage spillovers: baseline assumes wages of workers earning up to 75 percent of the median wage are affected, with the effect decreasing linearly and reaching zero above that threshold.
  - MW perimeter is held constant in the baseline (no expansion of coverage or enforcement changes).

### Baseline simulation findings — MW increases and household impacts
- Largest required MW increases (2016 basis)
  - Czech Republic: increase would have been almost 50 percent (largest percent increase).
  - Netherlands: increase of some 30 percent (almost 500 euro per month) (largest nominal increase).
- Countries with no simulated increase in MW in 2016: France, Hungary, Portugal, Poland, Romania.
- Household composition matters for direct poverty effects
  - Share of poor households without any low-wage workers ranges from 56 percent in Luxembourg to almost 91 percent in Croatia.
  - Share of non-poor households that would benefit from the EMW ranges from about 5 percent in Greece to as high as 18 percent in Estonia.
- Effects on poor households’ incomes and poverty
  - Estonia: MW increase of 34 percent would translate into an average increase in the income of poor households of 6 percent, implying an elasticity of 0.18.
  - Average elasticity of poor households’ income to the MW increase across all countries is 0.12.
  - Country elasticities: Belgium 0.07; Luxembourg 0.27.
  - Baseline poverty impact: average reduction of 0.5 percentage points in poverty rate across countries; largest fall of 1.3 percentage points in Estonia and smaller declines elsewhere.
  - The decline in poverty is not strongly correlated with the size of the MW increase but is positively correlated with the share of primary earners who earn the MW.
- In-work poverty
  - Defined as fraction of individuals working full time in dependent employment living in poor households.
  - In-work poverty levels vary from 4 percent in the Czech Republic to 15 percent in Luxembourg.
  - EMW reduces in-work poverty more sharply than overall poverty; example: in Spain the decline in in-work poverty in the baseline microsimulation is twice as large as the decline in overall poverty.

### Indirect effects and risks that reduce net poverty gains
- Dis-employment effects
  - Employment elasticity scenario: using 0.25 employment elasticity (i.e., 10 percent MW increase → 2.5 percent higher probability of becoming unemployed for affected workers), the average decline in poverty shrinks from 0.5 to 0.1 percentage points.
  - Under the 0.25 elasticity simulation, poverty would actually increase in some countries: Czech Republic, Malta, Lithuania, and the Netherlands.
  - Note: employment elasticities may vary by time horizon, worker demographics, country, and size of MW change.
  - CBO (2019) context: 0.25 chosen as the median short-term elasticity in U.S. literature; CBO median estimated elasticity for the longer term is 1.5; Hungary estimate cited is 0.15.
- Price pass-through
  - Firms may absorb MW costs via lower profits, higher productivity, cost reductions, or higher prices.
  - Hungary study (Harasztosi and Lindner, 2019): about 75 percent of MW cost passed on to consumers and 25 percent absorbed by lower profits, with stronger pass-through in less competitive sectors (nontraded services).
  - IMF (2019c) indicates pass-through from wages to prices has slowed in Europe in recent years, especially in sectors exposed to international competition.
  - Price increases can erode real income gains, particularly if poor households disproportionately consume goods from high-MW sectors.
- Loss of means-tested benefits
  - Higher earnings from MW increases can reduce eligibility for means-tested social insurance benefits, potentially eroding net income gains for some poor households.
  - Quantification of this effect depends on complex, country-specific social insurance rules and is not attempted here.
- Evasion, non-compliance, and perimeter shrinkage
  - More binding MWs may induce employers to overreport wages, underreport hours, or increase informal employment to circumvent regulation.
  - Increased informality/contract work would reduce worker protections and net incomes.
  - Stronger monitoring and enforcement would be needed to avoid circumvention as MW becomes more binding.

### Policy implications and alternatives
- Given limited direct poverty effects and material indirect risks, alternative or complementary instruments merit consideration:
  - Policies to increase access to better-paid jobs and human capital: job training and life-long learning programs.
  - Well-targeted social benefits for the most vulnerable households.
  - In-work tax credits to support incomes of the working poor while incentivizing employment (but these do not help poor households outside the labor force and may be partly captured by employers).
- Enforcement and perimeter issues
  - To maximize poverty-reducing effects, accompany MW increases with measures to broaden and enforce MW coverage (address measurement error, exemptions, and compliance).

### Effects on euro-area rebalancing
- Background on intra-euro area imbalances
  - Persistent external imbalances in the euro area reflect limited adjustment channels within a monetary union; rebalancing has been asymmetric and relied more on demand compression than relative price adjustment.
- Possible EMW macro effects
  - If larger MW increases translate into relative wage/cost increases in surplus (creditor) countries and lower effective costs in deficit (debtor) countries, the EMW could help reduce intra-euro area imbalances.
  - The paper explores how a hypothetical EMW may change relative unit labor costs, real exchange rates, and current account balances within the euro area (technical details in Technical Appendix B).
- Policy coordination message
  - IMF calls for a two-way rebalancing: creditor countries should allow wage competitiveness to adjust and domestic demand to strengthen (including via higher wage growth), while deficit countries should pursue reforms to contain labor costs and boost productivity.

*Italic: Source — IMF working paper content (excerpt provided).*

### 29.      Countries with the largest gap to a minimum wage of 60 percent of the median

### 29.      Countries with the largest gap to a minimum wage of 60 percent of the median

### Interactions between minimum wages, competitiveness, and rebalancing: framework and estimation
- Approach proceeds in three steps:
  - Panel data and a reduced-form local-projections empirical model to estimate how past changes in the minimum wage in EA countries affected average wages, unit labor costs (ULCs), and real exchange rates (REERs) in the short and medium run.
  - Use estimated coefficients to compute hypothetical changes in competitiveness (ULC-based REERs and CPI-based REERs) if all countries raise their current minimum wage to 60 percent of the median wage.
  - Map changes in competitiveness into changes in current account (CA) balances using CA-REER elasticities from staff’s External Sector Assessment.
- Definitions and signs:
  - For the current account, a positive (negative) gap corresponds to an external position that is too strong (weak).
  - For the REER, a positive (negative) gap indicates that the REER is overvalued (undervalued).
- Data notes:
  - Minimum wage gaps to the hypothetical EMW are computed based on the latest available data; they differ from those used in the micro-simulation which referred to 2016.
  - Main data source for the Kaitz ratio is the OECD (publishes the ratio until 2018, with some country-specific adjustments noted).

### Key empirical findings
- Wage and cost impacts
  - Regression results show that higher minimum wages lead to significantly higher labor costs in the short and medium term.
  - The impact mainly comes from higher average wages (rather than lower productivity) in the short run.
  - The rise in the minimum wage is associated with a commensurate appreciation of various REER measures (ULC-based REER, CPI-based REER).
- Productivity
  - Regressions find a negative association between MW increases and labor productivity in the medium term; short-run impact on productivity is not statistically significant but medium-run effect is negative and significant.
  - Some part of the ULC strengthening in surplus countries reflects weaker labor productivity rather than only higher wages.

### Simulated impacts of a coordinated EMW at 60 percent of median
- Aggregate and country-group effects
  - The coordinated minimum wage increases would help adjust competitiveness in countries with an excessively strong external position, while for countries that need to gain competitiveness the picture is mixed.
  - Implied medium-term cumulative REER appreciations in surplus countries is around 5 percent on average (or between 1-2 standard deviations of the observed medium-term historical changes in the REER).
  - The magnitude of medium-term cumulative CA adjustments is around 2 percentage points of GDP on average (in line with past experience or between 1 and 2 standard deviations of medium-term changes in the current account).
  - About 85 percent of the rebalancing would come from adjustments by the surplus countries or net creditor countries.
- Notable country-specific simulated outcomes (based on staff calculations and ESR assessments)
  - Example: Germany would need to increase its Kaitz ratio by 14½ percentage points, while its CA gap is assessed at 4½ percent.
  - Some countries already at or above the 60 percent threshold (France and Portugal) would see no MW change but are among countries that need to strengthen their external position.
  - Surplus countries where CA gaps would be substantially lowered include Germany, the Netherlands, and Estonia.
  - Deficit countries that would see improvement in their negative current account gap due to higher minimum wages in trading partners include Cyprus, Portugal, France, and Italy (some of which are already above the 60 percent threshold).
  - Deficit countries that would experience a further deterioration in external competitiveness because their own MW would need to rise: Spain, Greece, and Belgium.
  - Belgium’s loss in competitiveness appears particularly sizeable, at around 2 standard deviations of its past REER changes.
  - Slovakia and Ireland (two countries with minor imbalances) would experience a deterioration of their external position in the simulation.
- Mechanisms
  - Movements in competitiveness reflect both changes in the country’s own labor costs and changes in the labor costs of EA trading partners (EA trade share of relevant surplus countries is about 41 percent of their total trade).

### Notes of caution and model limitations
- Estimation and identification
  - The impact of MW changes on average labor costs is estimated with a reduced-form regression subject to omitted variables, potential endogeneity bias, and robustness concerns.
  - To address endogeneity, instrumental variables (political variables) were used; regressions control for cyclical conditions and structural country characteristics. Robustness checks (including replacing the Kaitz index with nominal wages in euros and including the size of the low-pay sector as an interaction term) are reported in Technical Appendix B.
- Elasticities and general equilibrium
  - The link between relative ULC changes and CA imbalances relies on elasticities derived from reduced-form estimates and may fail to capture important general equilibrium effects that only a full model can address.
  - CA-REER elasticities are country-specific and taken from staff’s External Sector Assessment; this choice is crucial because responsiveness of the current account to REER fluctuations depends on country-specific factors.
- Compliance and non-linearities
  - Simulations may not fully account for reduced MW compliance ex post; large increases required to reach EMW in some countries might affect compliance more strongly than past changes suggest, implying smaller effects on ULCs, competitiveness, and rebalancing.
  - Where structural unemployment is high (Greece and Spain), compliance with a higher MW may be difficult as many workers might accept sub-minimum wages to find a job.
- Scope limitations
  - Simulations ignore changes in wages and ULCs in non-EA EU trading partners, though MW harmonization there could affect EA trading-partner REERs.
  - Simulations may overestimate wage-inflation pass-through and hence the effect of relative wage movement on the REER; coefficients converting ULC-based REER changes into CPI-based REER changes are based on historical data and may overestimate pass-through in the current environment.
  - If pass-through is lower, changes in relative ULCs may not translate into the relative price changes needed for “expenditure switching” to adjust the CA.
  - Simulations do not capture aggregate savings and investment effects that do not operate through the REER; for example, higher MWs not passed through to prices may redistribute income toward lower income households (reducing aggregate savings and the CA), while lower corporate profits may reduce investment (tending to increase the CA).
- Productivity and long-term output risks
  - External adjustment driven by MW increases may be accompanied by lower productivity and long-term output in surplus countries; reasons proposed include compressed profit margins depressing investment, reduced incentives for skill upgrading, and effects of employment protection on productivity.

### Policy implications and conclusions
- Poverty impacts
  - Simulation using household survey data for 2016 suggests an EMW at 60 percent of the median would lift incomes of many low-wage workers and reduce in-work poverty and poverty rates in several EU countries, but the effect on overall poverty would be quantitatively modest because many poor households have no members working at or below the MW.
  - For poverty among working individuals, MW is more effective, but targeted policies (in-work subsidies) would be better targeted and avoid possible adverse employment and price effects, albeit with fiscal cost.
  - Significantly higher MWs can worsen non-compliance or increase use of legal loopholes unless accompanied by stronger anti-avoidance measures.
- Role of MW in external rebalancing
  - Convergence to a common EMW would appreciate the REER in countries with excessively strong external positions, helping reduce large external surpluses.
  - Some countries with weak external positions would benefit from rising labor costs in trading partners even if their own MW need not change; however, several deficit countries would see imbalances deepen because of higher labor costs and worse competitiveness.
  - There is some alignment between external imbalances and MW differentials within the EA, but alignment is imperfect.
- Trade-offs and alternative tools
  - Moving MWs differentially may contribute to faster adjustment of imbalances but may conflict with social or economic objectives and constrain future use of MW policy to respond to asymmetric shocks.
  - Other policies can achieve external rebalancing, including fiscal policy, pension reforms (to affect household savings), innovation policies (to foster domestic investment), and policies to boost labor productivity growth in deficit countries.
  - National wage negotiations could also facilitate labor cost realignments inside a monetary union.

*Italic: Source — IMF staff calculations and External Sector Report assessments as presented in the chapter "Countries with the largest gap to a minimum wage of 60 percent of the median" from the provided PDF content.*

### REFERENCES

### REFERENCES

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- Schulten T., Müller T., and Eldring, L., 2015, Prospects and Obstacles of a European Minimum Wage Policy, Chapter 10 in Van Gies G. and Schulten. T., Eds., Wage Bargaining under the New European Governance, Brussels.
- Wirtschafts und SozialWissenschaftliches Institut (WSI), Minimum Wage Report 2019, March 2019.

### APPENDIX A: EFFECTS OF HARMONIZING MINIMUM WAGES RATIOS ON POVERTY — Overview
- Purpose: Provides additional details about simulations of the effects of the hypothetical EMW on poverty.
- Data sources used:
  - Minimum wages: Eurostat.
  - Household data: 2018 wave of the EU-SILC survey, including monthly wages for employees.

### Poverty simulation methodology — steps
- Compute share of households in poverty in each country in 2016:
  - A household is in poverty if its overall income is below 60 percent of the median household income in the country in year 2016.
- Change monthly wage of every employee in the household to reflect changes in wages as per rules described.
- Recompute household income using the simulated monthly wage of each employee.
- Check whether household remains below 60 percent of the original median household income.
- Reported reduction in poverty equals the percentage of households that exit poverty as a result of the policy change.

### Monthly wage computation (method)
- Because only annual labor income is reported in the SILC database, monthly wages are computed using Brandolini et al. (2011) methodology.
- Annual gross labor income (EU-SILC variable PY010G) is divided by full time equivalent months.
- Full time equivalent months measure is built by summing:
  - Number of months worked full time (#푖푖푔푔푖푖푚푚 ℎ푔푔 퐹퐹퐹퐹,푖푖, PL073), and
  - Number of weeks worked part-time (#푖푖푔푔푖푖푚푚 ℎ푔푔 푃푃퐹퐹 푖푖, PL074) scaled by a scalar.
- The scalar equals the ratio between:
  - the median number of hours worked in the week prior to the interview (PL060) by full-time workers in a year-country-sex group 푔푔(푖푖) to which individual 푖푖 belongs (ℎ푔푔표표푔푔 푔푔  푚푚푚푚푚푚,퐹퐹퐹퐹,푔푔(푖푖))
  - and the number of hours worked by part-time workers in the same group (ℎ푔푔표표푔푔 푔푔  푚푚푚푚푚푚,푃푃퐹퐹,푔푔(푖푖)).

- The medians are computed in each cell using cross sectional person weights (PB040).
- Formal expression reported in source:
  - 푤푤푤푤푔푔푒푒 퐹퐹퐹퐹퐹퐹,푖푖 = 푔푔푔푔푔푔푔푔푔푔 푖푖푖푖푖푖 푔푔푖푖푒푒 푖푖 / (#푖푖푔푔푖푖푚푚 ℎ푔푔 퐹퐹퐹퐹,푖푖 + ℎ푔푔표표푔푔 푔푔  푚푚푚푚푚푚,퐹퐹퐹퐹,푔푔(푖푖)/ℎ푔푔표표푔푔 푔푔  푚푚푚푚푚푚,푃푃퐹퐹,푔푔(푖푖) × #푖푖푔푔푖푖푚푚 ℎ푔푔 푃푃퐹퐹 푖푖).

- The measure is computed only for employees, excluding self-employed workers.
- Simulations consider only wages for employees; wages of self-employed workers are assumed unchanged.
- Employees and self-employed are defined based on EU-SILC variable PL031.
- Figures regarding in-work poverty pertain to full-time employees.

### Wage adjustment and spillover assumptions
- Key assumption: wages of workers earning less than the MW are assumed to rise in the same proportion as the MW.
- Additional assumption: the change in the MW spills over to all wages in the range between the current MW and 75 percent of the median wage (equivalently 1.25 times the EMW).
- Spillover parameter β is defined as follows (as presented in source):
  - New wage = β × FTE wage,
  - β = 
    - 퐸퐸퐸퐸퐸퐸 (60% 퐸퐸푒푒푀푀.푤푤푤푤푔푔푒푒) / 푖푖표표푔푔푔푔푒푒푖푖푚푚 퐸퐸퐸퐸 (푖푖표표푔푔.퐸퐸퐸퐸), 퐹퐹푇푇퐸퐸 < 푖푖표표푔푔.퐸퐸퐸퐸
    - 1 + 1/푖푖표표푔푔.퐸퐸퐸퐸 × (퐸퐸퐸퐸퐸퐸 − 푖푖표표푔푔.퐸퐸퐸퐸) / (1.25 퐸퐸퐸퐸퐸퐸 − 푖푖표표푔푔.퐸퐸퐸퐸) × (1.25 퐸퐸퐸퐸퐸퐸 − 퐹퐹푇푇퐸퐸), 푖푖표표푔푔.퐸퐸퐸퐸 ≤ 퐹퐹푇푇퐸퐸 < 1.25 퐸퐸퐸퐸퐸퐸
    - 1, 퐹퐹푇푇퐸퐸 푤푤푤푤푔푔푒푒 ≥ 125% 퐸퐸퐸퐸퐸퐸
- The source includes a diagram illustrating wage spillover β as a function of FTE wage with markers at 1.25 퐸퐸퐸퐸퐸퐸 and 퐹퐹푇푇퐸퐸 푤푤푤푤푔푔푒푒.

### Disemployment effects and labor demand elasticity
- Simulations that include disemployment effects use a labor demand elasticity of 0.25.
  - Interpretation: a 10 percent increase in the minimum wage would lead to a 2.5 percent higher probability of becoming unemployed.
  - This number comes from the median estimate in the CBO report “The Effects on employment and Family Income of Increasing the Federal Minimum Wage” (July, 2019), table A-2.
- Implementation in simulation:
  - Since all workers earning less than 1.25 percent of the MW experience a wage increase in simulation, all workers in this group experience a higher incidence of unemployment.
  - If a worker is drawn into unemployment (this happens with probability 0.25*[percent increase in minimum wage]), that worker is assigned a wage of 0, and household income is recomputed.
  - The share of households in poverty is recomputed to obtain the new change in poverty under this scenario.

*Source: wpiea2020059-print-pdf - REFERENCES*

### APPENDIX B: EFFECTS OF HARMONIZING MINIMUM WAGES RATIOS ON REBALANCING

### APPENDIX B: EFFECTS OF HARMONIZING MINIMUM WAGES RATIOS ON REBALANCING WITHIN THE EURO AREA

### A. The impact of a change in the minimum wage on ULCs: regression analysis
- Empirical approach
  - Local projection technique (Jorda, 2005) to estimate cumulative responses up to h = 5 years.
  - Baseline dependent variable: log of unit labor costs (ULCs).
  - Main regressor: log of the Kaitz index (minimum wage / median wage); alternative specifications use log minimum wage level (in euros).
  - Controls: country and year fixed effects (or country-specific time trends), lagged ULCs, output gap, labor force participation rate, inflation, growth expectations, trade openness, industrial share, euro entry, and other structural variables.
  - Endogeneity addressed via instrumental variables: political orientation of largest government party (left/center/right), number of years left in executive term, vote share of largest government party, and the two-year lag of the Kaitz index.
  - First-stage F-statistics reported: 30.71 (IV with country-specific time trends), 13.35 (IV with country and time fixed effects).

- Key IV (country and time fixed effects) cumulative coefficients — Kaitz index, in log (second-stage)
  - Year 0: 0.178**
  - Year 1: 0.516***
  - Year 2: 0.850***
  - Year 3: 0.962***
  - Year 4: 0.779***
  - Year 5: 0.496***

- OLS (country and year fixed effects) cumulative coefficients — Kaitz index, in log (for comparison)
  - Year 0: 0.0287
  - Year 1: 0.0969*
  - Year 2: 0.231***
  - Year 3: 0.402***
  - Year 4: 0.515***
  - Year 5: 0.470***

- Identification and bias
  - IV coefficients are considerably larger than OLS, especially in the short run, consistent with IV removing a downward bias in OLS estimates.
  - Over-identification test P-values (reported for IV with country-specific trends): 0.655, 0.394, 0.658, 0.333, 0.849, 0.339 (years 0–5).

### B. Transmission channels: average compensation of employees is the main channel
- Main channel evidence (IV estimates)
  - ULCs (Kaitz index, in log): Year 0: 0.178**; Year 1: 0.516***; Year 2: 0.850***; Year 3: 0.962***; Year 4: 0.779***; Year 5: 0.496***
  - Compensation of employees (Kaitz index, in log): Year 0: 0.137**; Year 1: 0.525***; Year 2: 0.744**; Year 3: 0.666***; Year 4: 0.427**; Year 5: 0.399**
  - Labor productivity (Kaitz index, in log): Year 0: -0.0554; Year 1: -0.196; Year 2: -0.130; Year 3: -0.198; Year 4: -0.404*; Year 5: -0.448*
  - Total employment (Kaitz index, in log): Year 0: -0.0466; Year 1: -0.0977; Year 2: -0.0858; Year 3: -0.135; Year 4: -0.291*; Year 5: -0.454**

- Interpretation
  - The primary mechanism by which higher minimum wages raise ULCs is through higher average wages (employees’ compensation).
  - Effects on labor productivity are small and only intermittently statistically significant in the medium term.
  - Employment shows modest negative cumulative responses becoming statistically significant by years 4–5.

### C. Alternative measures and identification checks
- Alternative regressor: log level of the minimum wage (in euros) instead of the Kaitz index
  - IV estimates (minimum wage level, in log) — ULCs:
    - Year 0: 0.100***
    - Year 1: 0.207***
    - Year 2: 0.396***
    - Year 3: 0.438***
    - Year 4: 0.251**
    - Year 5: -0.00747
  - Conclusion: results broadly robust when isolating variation in the nominal minimum wage.

- Nonlinearity by low-pay sector size (interaction with Low Pay = share of workers earning < 65% of median)
  - Estimation of equation with (log Kaitz) * Low Pay interaction finds medium-term impacts of MW on ULC are stronger when the low-pay sector is large (consistent with a genuine cost channel rather than pure joint endogeneity).
  - Selected interaction coefficients (OLS with country-specific trends, (log Kaitz) * Low Pay):
    - Year 2: 0.00738***
    - Year 3: 0.00891***
    - Year 4: 0.0123***
    - Year 5: 0.00755**

### D. Rebalancing in the euro area: simulation exercises and main results
- Method overview
  - Decompose changes in CPI- or ULC-based REER into relative ULC changes (domestic vs. trading partners) while holding nominal effective exchange rates constant across euro area (EA) countries.
  - Use the IV baseline ULC response (IV with country and time fixed effects) to compute medium-term changes in ULC for each EA country.
  - Convert simulated REER changes into current account (CA) changes using a reduced-form trade elasticity from the IMF ESR exercise.
  - Baseline harmonization target: MW = 60 percent of national median wage.

- Baseline simulated CA impacts (medium term, cumulative impacts over five years)
  - Harmonizing minimum wage at 60 percent of the national median wage:
    - Deficit EA countries (those with weak external position): reduce negative current account gaps by around 0.1–0.3 percentage points of GDP in the medium term.
    - Surplus EA countries (those with strong external position): reduce positive current account gaps by around 0.6–2 percentage points of GDP.
  - Context: latest staff-assessed CA gaps are around -0.5 and 1.8 percent of GDP for deficit and surplus EA countries, respectively.

- Dynamics
  - Rebalancing impacts become more evident by year 3 and peak around year 3, then taper off.
  - Near-term (years 1–2) effects are smaller and can be close to zero under some regression specifications.

### E. Robustness checks for rebalancing results
- Regression and identification robustness
  - Rebalancing results are robust across different ULC–minimum wage regression specifications and identification strategies; baseline IV results lie within the range of alternative estimates.
  - Some specifications (e.g., interaction with low-pay incidence with country-and-time fixed effects) yield more extreme simulated outcomes; such extreme results should be interpreted with caution.

- Alternative REER measure: CPI-based REER
  - Procedure: estimate country-specific relationship between ULCs and CPI, apply coefficient to simulated ULC changes, compute CPI-based REER changes.
  - Result: qualitative robustness — surplus countries generally appreciate in CPI-based REER; some deficit countries (e.g., France, Portugal, Italy) show CPI-based REER depreciation mainly because trading partners’ CPI rises; other deficit countries (e.g., Spain, Greece, Slovakia, Belgium) show competitiveness worsening consistent with main results.

- Alternative trading weights: global value chain (GVC) weights
  - Using GVC value-added weights yields broadly similar rebalancing results.
  - Impact slightly smaller for deficit EA countries and slightly larger for surplus EA countries relative to bilateral trade weights.

- Alternative harmonization targets
  - Target MW/P50 = 50% (less ambitious than 60%):
    - Smaller rebalancing impacts: about 0.1 and 0.4 percentage points lower reduction in the current account gap for deficit and surplus EA countries, respectively, compared with the 60% baseline.
  - Target MW/P50 = 70% (more ambitious than 60%):
    - For deficit countries: only a 0.05 percentage-point-of-GDP reduction of the CA gap because many deficit countries would need large increases in their minimum wage.
    - For surplus countries: moving to 70% would close positive CA gaps and could bring surplus countries into a small negative CA gap.

### F. Supporting empirical details (selected estimates and diagnostics)
- Selected first-stage IV diagnostics (Table B1)
  - Political orientation dummy (Center): significant negative correlation with Kaitz index in first-stage regressions (e.g., -0.057*** with country-specific time trends; -0.048** with country and time fixed effects).
  - Two-year lagged Kaitz index strong predictor in first-stage (e.g., 0.653*** with country-specific time trends; 0.555*** with country and time fixed effects).
  - First-stage F-statistics: 30.71 and 13.35 (two first-stage specifications).

- Full IV specification (selected coefficients from Table B6 — instrumented Kaitz index with country and time fixed effects)
  - Kaitz index, log (Year 0–5): 0.178**, 0.516***, 0.850***, 0.962***, 0.779***, 0.496***
  - Lagged ULCs (1-year and 2-year) and other controls reported in Table B6; share of industrial sector, lagged, is strongly positive and significant across horizons (e.g., 1.104*** at Year 0; 4.468*** at Year 3).

*Italic line: Source: IMF staff calculations and regression estimates reported in APPENDIX B: EFFECTS OF HARMONIZING MINIMUM WAGES RATIOS ON REBALANCING (wpiea2020059-print-pdf).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020059-print-pdf.pdf_