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---

### I. Introduction — Key findings and context
- COVID-19 characterized as “the most devasting human, economic and social crisis of modern times” with deep disruption to labor markets (ILOa, 2020).
- Youths defined as aged 16 to 24 (ILO); in a typical EMDE, youths account for roughly one-third of the working-age population.
- Pre-pandemic youth labor challenges:
  - In Latin America, about one in every five youths were unable to find a job.
  - That youth unemployment rate is three times higher than for adults (ILO, 2018).
  - In Uruguay, youth unemployment was five times higher than for adults even before the health crisis.
  - Uruguay ranked in the 90th percentile of global distributions of both the youth/adult unemployment ratio and youth unemployment.
- Pandemic-era impacts on youth:
  - More than one in six young adults stopped working (ILOb, 2020).
  - Employed youths experienced a 23 percent reduction in working hours (ILOb, 2020).
  - Education impacts: 50 per cent of learners reported delays in finishing their courses; 10 percent are uncertain about completing their studies.
- Uruguay-specific pandemic labor-note:
  - Government policy for enhanced unemployment insurance allowed companies to temporarily suspend contracts to preserve jobs.
  - The aggregate unemployment rate “has barely increased,” partly due to a fall in labor participation as discouraged workers stopped looking for jobs.
- Research scope:
  - Paper analyzes determinants of youth unemployment for Brazil, Chile, Colombia, Mexico, Peru and Uruguay using a synthetic panel of household surveys from 1990–2018.
  - Unemployment rate decreases progressively with age, largest decline for prime-aged adults (30–54); age effects largest in Uruguay.
  - Decomposition analysis: socio-demographic features explain a significant portion of Uruguay’s youth unemployment differential, but a large fraction remains unexplained by traditional characteristics, suggesting possible roles for labor market institutions.

### II. Labor Market Characteristics — Uruguay
- Post-2002 recovery and social indicators:
  - Uruguay achieved a remarkable recovery after 2002 and ranks favorably among Latin American peers.
  - Welfare and social protection institutions established earlier (pensions, unemployment insurance, contributory health).
- Collective bargaining and unionization:
  - 2005 labor reform reinstated and expanded wage councils, fostering tripartite agreements.
  - Collective bargaining coverage increased from 28 percent in 2000 to over 97 percent in 2005.
  - Uruguay has the second highest unionization rate in the region: 30 percent.
- Recent negotiation adjustments:
  - Revisions incorporated productivity considerations and delinked wages from past inflation.
  - 2020 negotiation introduced a temporary real wage decline tied to GDP growth decline to preserve employment during the pandemic.
- Labor costs, protections and perceived rigidity:
  - Labor laws grant high premiums for overtime, substantial paid leave days, generous severance payments, and relatively short fixed-term contracts.
  - Executive opinion surveys indicate perceived low flexibility and elevated labor costs relative to regional peers; taxes and contributions seen as substantially increasing labor costs.
  - These features can discourage creation of stable jobs, potentially increasing informality and temporary positions and creating “insider-outsider” problems.
- Historical and boom-period markers:
  - During the 2002 crisis: unemployment rose to a record of 19.8 percent; real wages fell by nearly 11 percent.
  - 2004–2014 boom period: Growth averaged 5.3 percent (compared to Latin American average of 2.3 percent).
  - Employment and labor force participation rose by 10 and 6 percentage points, respectively.

### III. Labor market trends and recent changes
- Growth averaged 4.1 percent per year.
- Since 2015 labor market indicators have gradually deteriorated, averaging 1.4 percent since 2015.
- Since 2015 and until the period before the Covid-19 pandemic:
  - Employment declined by 2.5 percentage points.
  - Labor force participation declined by 1.8 percentage points.
  - Annual real wage increases slowed to 1.6 percent.
- Since 2012:
  - Median duration of unemployment increased to 8.5 weeks (from 6.8 in 2012), implying a decline in monthly job finding rate of 5 percentage points.
  - Median employment duration fell to 92 weeks (from 101 in 2012), translating into a higher monthly job separation rate of 0.4 percentage points.
- Since the onset of the pandemic, the unemployment rate in Uruguay increased to about 11 percent (footnote notes uncertainty about the eventual job losses from suspended contracts under enhanced unemployment insurance).

### IV. Youth unemployment: levels, composition, and vulnerabilities
- At end 2018 Uruguay’s headline unemployment rate was about 9 percent; regional peers averaged 7 percent and other emerging economies averaged 8 percent.
- At end 2018 youth unemployment was nearly 27 percent — roughly three times the headline number.
- Uruguay ranks in the 90th percentile of the global distributions of both the youth/adult unemployment ratio and youth unemployment rate.
- Forms and quality of youth employment:
  - Nearly 50 percent of youths in Uruguay are hired on temporary/seasonal contracts.
  - Youth contracts are largely informal and concentrated in small and medium-sized firms, which commonly lack legal or social protection and employees’ benefits.
  - Youth employment is concentrated in temporary, low-quality and mostly informal jobs that are usually not tele-workable.
- Sectoral patterns (2015–period before pandemic):
  - Commerce, hotels and restaurants, and social services (sectors with high youth concentration) experienced minimal job losses, while agriculture, manufacturing and construction (lower proportion of young workers) shed between 5 to 10 percent of workers since 2015.
  - Employment in the social services sector grew by nearly 9 percent in the same period.
- Remote work feasibility is much less pronounced among workers who are young, in less secure work arrangements, and employed in SMEs.

### V. Education, job quality, and the “experience gap”
- Even well-educated young in Uruguay experience high unemployment rates; the unemployment rate of the highly educated young is more than three times higher than the rate for adults with little education.
- Relative to the average cohort (primary education) and conditional on other demographics:
  - Individuals with some tertiary education have lower unemployment rates by 5 percentage points.
  - Individuals with graduate or higher education have lower unemployment rates by 8 percentage points.
- The “experience gap of young people” and resulting “experience trap”: general education alone is often insufficient without job-related competencies, apprenticeships or work experience.

### VI. Data and empirical methodology
- Data:
  - National Statistical Institute (INE) household surveys from 1990–2018 for all countries in sample.
  - Main sample: economically active population aged 15–64.
  - Sample excludes those in school, retired, or in the military/armed forces; includes formal, informal and self-employed.
  - Constructed synthetic (pseudo) panel data by averaging observations by age, gender and education to form 56 cohorts (seven age groups × two genders × four education levels).
  - Seven age groups: 15-17; 18-21; 22-24; 25-29; 30-54; 55-60; over 61.
  - Four education levels: primary, secondary, some tertiary and graduate or higher.
  - Resulting sample size for Uruguay: 1,624 observations with 29 survey years.
- Empirical strategy:
  - Reduced-form unemployment equation estimated with random effects: u_ct regressed on log real GDP (Y_t), age intervals (age_c), cohort-specific effects (X_ct: marital status, education, region, gender), and error term.
  - Controls include real GDP growth or output gap and region fixed effect.
  - Reference group: female, unmarried, aged 15-17, with primary education, informally attached to hotels and restaurants sector.

### VII. Key regression and decomposition results (selected figures preserved exactly)
- Table 1 — Age effects for URY:
  - 18-21: -1.43**
  - 22-24: -0.99*
  - 25-29: -1.13**
  - 30-54: -1.57***
  - 55-60: -1.53***
  - 61+: -1.73***
- Table 1 — Education effects for URY:
  - some tertiary: -0.05**
  - graduate: -0.08***
- Table 1 — Other controls for URY:
  - GDP: -0.10***
  - male: -0.05***
  - urban: 0.12**
  - married: -0.05
- Table 1 — Sample size and fit for URY:
  - N: 1,530; R-sq: 0.66.
- Table 2 — Decomposition of Changes in Youth Unemployment Rates, 1990–2018:
  - Panel A (Difference between country youth unemployment and LA5 average):
    - URY Difference: 0.179 (standard error (0.022))
    - URY Endowment (explained): 0.110 (0.019)
    - URY Structural (unexplained): 0.070 (0.013)
  - Panel B (Fraction explained by covariates for URY):
    - GDP: -0.009 (0.005)
    - education: -0.003 (0.017)
    - industry: 0.005 (0.011)
    - region: 0.036 (0.018)
    - married: 0.082 (0.018)
- Interpretation:
  - Youth unemployment in Uruguay is 18pp higher than would be expected if persons from this age group lived in the average LA5 country; roughly one half of the difference is explained by endowments, but about 40 percent of Uruguayan unemployment remains unexplained by observable controls.

### VIII. Interpretation of unexplained gap and heterogeneity
- The unexplained portion of the youth-to-adult unemployment gap in Uruguay is substantial and falls with age:
  - Persons aged 15–17: unemployment rate is 30 percent higher than those older than 25; 17 percent attributable to characteristics and 13 percent unexplained.
  - Unexplained portion falls to 9 percent for ages 18–21 and 6 percent for ages 22–24.
- Covariates with significant explanatory power:
  - Marital status and geographical region jointly generate an unemployment gap that is 12 percent higher for youths in Uruguay relative to youths in LA5.
  - Married young adults appear more affected by unemployment; urban young Uruguayans face an unemployment rate 4 percent higher than observationally equivalent youths in rural areas and LA5.
- Much of the youth unemployment gap is likely due to structural factors and socio-demographic features; data limitations prevent formal testing with additional labor-market institutional covariates.

### IX. Policy implications and recommendations
- Priority: multi-faceted recovery focused on youth vulnerability given marginal labor-market attachment and pandemic impact.
- Targeted policy solutions to facilitate faster transitions into the labor market:
  - Specialized skills training and apprenticeships (dual training systems combining general education with apprenticeships/training in specialized skills such as programming).
- Attention to informal and temporary employment among youths:
  - Design relief and recovery policies recognizing that many youths lack formal employment ties (unemployment benefits, insurance, payroll and income tax reductions, and paid sick leave are often linked to formal employment).
- Consider socio-demographic constraints:
  - Policies that enhance mobility and employment prospects for married and urban youths.
- Research needs:
  - Further research using microlevel administrative data to better understand structural causes of Uruguay’s youth unemployment gap.

*Source: wpiea2020281-print-pdf - 4.1 percent per year.*

### REFERENCES ______________________________________________________________________________________ 23

### REFERENCES

### Figures and Tables Inventory
- Figures included:
  - 1. Cross-country Social and Economic Indicators
  - 2. Indicators of Labor Market Rigidities across Countries and Regions, 2018
  - 3. Labor Force Participation and Employment Rates for Uruguay, 2006–20
  - 4. Unemployment, Job Finding and Job Separation Rates, 2006–20
  - 5. Unemployment Rates Decomposed by Age, 2018
  - 6. Forms of Employment, 2018
  - 7. Sectoral Employment in Uruguay, 2018
  - 8. Youth and Adult Unemployment Rates by Educational Levels, 2005–18
- Tables included:
  - 1. Determinants of Youth Unemployment
  - 2. Decomposition of Changes in Youth Unemployment Rates, 1990–2018

### I. Introduction — Key findings and context
- COVID-19 characterized as “the most devasting human, economic and social crisis of modern times” with deep disruption to labor markets (ILOa, 2020).
- Youth definition and demographic weight:
  - Youths defined as aged 16 to 24 (ILO).
  - In a typical EMDE, youths account for roughly one-third of the working-age population.
- Pre-pandemic youth labor challenges:
  - In Latin America, about one in every five youths were unable to find a job.
  - That youth unemployment rate is three times higher than for adults (ILO, 2018).
  - In Uruguay, youth unemployment was five times higher than for adults even before the health crisis.
  - Uruguay ranked in the 90th percentile of global distributions of both the youth/adult unemployment ratio and youth unemployment.
- Pandemic-era impacts on youth:
  - More than one in six young adults stopped working (ILOb, 2020).
  - Employed youths experienced a 23 percent reduction in working hours (ILOb, 2020).
  - Education impacts: 50 per cent of learners reported delays in finishing their courses; 10 percent are uncertain about completing their studies.
- Uruguay-specific pandemic labor-note:
  - Government policy for enhanced unemployment insurance allowed companies to temporarily suspend contracts to preserve jobs.
  - The aggregate unemployment rate “has barely increased,” partly due to a fall in labor participation as discouraged workers stopped looking for jobs.
- Research scope and main empirical findings:
  - Paper analyzes determinants of youth unemployment for Brazil, Chile, Colombia, Mexico, Peru and Uruguay using a synthetic panel of household surveys from 1990–2018.
  - Unemployment rate decreases progressively with age, largest decline for prime-aged adults (30–54); age effects largest in Uruguay.
  - Decomposition analysis: socio-demographic features (e.g., marital status, geographical location) explain a significant portion of Uruguay’s youth unemployment differential, but a large fraction remains unexplained by traditional characteristics, suggesting possible roles for labor market institutions.

### II. Literature connections and policy-relevant evidence
- Cyclical sensitivity:
  - Youth unemployment is more sensitive to cyclical fluctuations and tends to rise rapidly at recession onset with sluggish mean reversion (Ahn, et al., 2019; Mitra & Xu, 2017; O'Higgins, 2012).
- Active Labor Market Policies (ALMPs) evidence:
  - Meta-analyses for Europe (Card et al., 2010; Kluve, 2010): programs targeting the young, particularly training, are less effective short-term than untargeted programs for unemployed persons; training and job search programs yield better medium-term outcomes.
- Institutional reform evidence:
  - Stricter employment protection associated with higher shares of temporary contracts, often filled by youth (Hijzen et al., 2017).
  - More restrictive dismissal regulations reduce within-industry job-to-job transitions toward permanent jobs (Bassanini & Garnero, 2013).
- Latin America context:
  - Research links reducing labor market rigidities to improved functioning and better youth employment prospects (David et al., 2019; Kugler, 2019; Amarante & Dean, 2012; Perry et al., 2007).

### II. Labor Market Characteristics — Uruguay specific findings
- Post-2002 recovery and social indicators:
  - Uruguay achieved a remarkable recovery after 2002, now ranking favorably among Latin American peers.
  - Welfare and social protection institutions established earlier (pensions, unemployment insurance, contributory health).
- Collective bargaining and unionization:
  - 2005 labor reform reinstated and expanded wage councils, fostering tripartite agreements.
  - Collective bargaining coverage increased from 28 percent in 2000 to over 97 percent in 2005.
  - Uruguay has the second highest unionization rate in the region: 30 percent.
  - Evidence suggests centralized collective bargaining had positive impacts on wages and employment (David, Lambert, & Toscani, 2019; Amarante, Arim, & Yapor, 2016; Mitra & Xu, 2017).
- Recent negotiation adjustments:
  - Revisions incorporated productivity considerations and delinked wages from past inflation.
  - 2020 negotiation introduced a temporary real wage decline tied to GDP growth decline to preserve employment during the pandemic.
- Labor costs, protections and perceived rigidity:
  - Labor laws grant high premiums for overtime, substantial paid leave days, generous severance payments, and relatively short fixed-term contracts.
  - Executive opinion surveys indicate perceived low flexibility and elevated labor costs relative to regional peers; taxes and contributions seen as substantially increasing labor costs.
  - These features can discourage creation of stable jobs, potentially increasing informality and temporary positions and creating “insider-outsider” problems.
- Historical crisis reference:
  - During the 2002 crisis:
    - Unemployment rose to a record of 19.8 percent.
    - Real wages fell by nearly 11 percent.
- 2004–2014 boom period:
  - Growth averaged 5.3 percent (compared to Latin American average of 2.3 percent).
  - Employment and labor force participation rose by 10 and 6 percentage points, respectively.
  - Real wages increases averaged (text truncated in source; exact average not provided in supplied excerpt).

### A. Labor Dynamics in Uruguay — Implications
- Strong social performance (high social security contributions, pension coverage, low informality) coexists with rigid labor protections and high labor costs, creating potential tradeoffs between protection for insiders and barriers for youth entry.
- Empirical decomposition indicates that observable socio-demographic factors account for part—but not all—of Uruguay’s elevated youth-to-adult unemployment gap, pointing to institutional and policy considerations.

*Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020281-print-pdf.pdf*

### 4.1 percent per year.

### 4.1 percent per year.

### Labor market trends and recent changes
- Growth averaged 4.1 percent per year.
- Since 2015 labor market indicators have gradually deteriorated, averaging 1.4 percent since 2015.
- Since 2015 and until the period before the Covid-19 pandemic:
  - Employment declined by 2.5 percentage points.
  - Labor force participation declined by 1.8 percentage points.
  - Annual real wage increases slowed to 1.6 percent.
- Since 2012:
  - Median duration of unemployment increased to 8.5 weeks (from 6.8 in 2012), implying a decline in monthly job finding rate of 5 percentage points.
  - Median employment duration fell to 92 weeks (from 101 in 2012), translating into a higher monthly job separation rate of 0.4 percentage points.
- Since the onset of the pandemic, the unemployment rate in Uruguay increased to about 11 percent (footnote notes uncertainty about the eventual job losses from suspended contracts under enhanced unemployment insurance).

### Youth unemployment: levels, composition, and vulnerabilities
- At end 2018 Uruguay’s headline unemployment rate was about 9 percent; regional peers averaged 7 percent and other emerging economies averaged 8 percent.
- At end 2018 youth unemployment was nearly 27 percent — roughly three times the headline number.
- Uruguay ranks in the 90th percentile of the global distributions of both the youth/adult unemployment ratio and youth unemployment rate.
- Forms and quality of youth employment:
  - Nearly 50 percent of youths in Uruguay are hired on temporary/seasonal contracts.
  - Youth contracts are largely informal and concentrated in small and medium-sized firms, which commonly lack legal or social protection and employees’ benefits.
  - Youth employment is concentrated in temporary, low-quality and mostly informal jobs that are usually not tele-workable.
- Sectoral patterns (2015–period before pandemic):
  - Commerce, hotels and restaurants, and social services (sectors with high youth concentration) experienced minimal job losses, while agriculture, manufacturing and construction (lower proportion of young workers) shed between 5 to 10 percent of workers since 2015.
  - Employment in the social services sector grew by nearly 9 percent in the same period.
  - Differential sectoral losses may reflect cyclical conditions, technological shifts (as in agriculture) or competitiveness issues (as in manufacturing).
- Remote work feasibility is much less pronounced among workers who are young, in less secure work arrangements, and employed in SMEs.

### Education, job quality, and the “experience gap”
- Even well-educated young in Uruguay experience high unemployment rates; the unemployment rate of the highly educated young is more than three times higher than the rate for adults with little education.
- Relative to the average cohort (primary education) and conditional on other demographics:
  - Individuals with some tertiary education have lower unemployment rates by 5 percentage points.
  - Individuals with graduate or higher education have lower unemployment rates by 8 percentage points.
- The “experience gap of young people” and resulting “experience trap” are highlighted: general education alone is often insufficient without job-related competencies, apprenticeships or work experience.

### Data and empirical methodology
- Data:
  - National Statistical Institute (INE) household surveys from 1990–2018 for all countries in sample.
  - Main sample: economically active population aged 15–64.
  - Sample excludes those in school, retired, or in the military/armed forces; includes formal, informal and self-employed.
  - Constructed synthetic (pseudo) panel data by averaging observations by age, gender and education to form 56 cohorts (seven age groups × two genders × four education levels).
  - Seven age groups: 15-17; 18-21; 22-24; 25-29; 30-54; 55-60; over 61.
  - Four education levels: primary, secondary, some tertiary and graduate or higher.
  - Resulting sample size for Uruguay: 1,624 observations with 29 survey years.
- Empirical strategy for estimating youth unemployment:
  - Reduced-form unemployment equation estimated with random effects:
    - Unemployment in cohort c at time t (u_ct) regressed on log real GDP (Y_t), age intervals (age_c), cohort-specific effects (X_ct: marital status, education, region, gender), and error term.
    - Controls include real GDP growth or output gap and region fixed effect.
    - Reference group: female, unmarried, aged 15-17, with primary education, informally attached to hotels and restaurants sector.
  - Findings: unemployment rate decreases with age (except in Mexico); age effects are much larger in Uruguay.
  - Controlling for education and other demographics renders the effect of GDP no longer significant or with the wrong sign (possible multicollinearity with age).

### Key regression and decomposition results (selected figures preserved exactly)
- Table 1 highlights:
  - Age effects for URY:
    - 18-21: -1.43**
    - 22-24: -0.99*
    - 25-29: -1.13**
    - 30-54: -1.57***
    - 55-60: -1.53***
    - 61+: -1.73***
  - Education effects for URY:
    - some tertiary: -0.05**
    - graduate: -0.08***
  - Other controls for URY:
    - GDP: -0.10***
    - male: -0.05***
    - urban: 0.12**
    - married: -0.05
  - Sample size N for URY: 1,530; R-sq: 0.66.
- Table 2 — Decomposition of Changes in Youth Unemployment Rates, 1990–2018:
  - Panel A (Difference between country youth unemployment and LA5 average):
    - URY Difference: 0.179 (standard error (0.022))
    - URY Endowment (explained): 0.110 (0.019)
    - URY Structural (unexplained): 0.070 (0.013)
  - Panel B (Fraction explained by covariates for URY):
    - GDP: -0.009 (0.005)
    - education: -0.003 (0.017)
    - industry: 0.005 (0.011)
    - region: 0.036 (0.018)
    - married: 0.082 (0.018)
  - Interpretation: youth unemployment in Uruguay is 18pp higher than would be expected if persons from this age group lived in the average LA5 country; roughly one half of the difference is explained by endowments, but about 40 percent of Uruguayan unemployment remains unexplained by observable controls.

### Interpretation of unexplained gap and heterogeneity
- The unexplained portion of the youth-to-adult unemployment gap in Uruguay is substantial and falls with age:
  - Persons aged 15–17: unemployment rate is 30 percent higher than those older than 25; 17 percent attributable to characteristics and 13 percent unexplained.
  - Unexplained portion falls to 9 percent for ages 18–21 and 6 percent for ages 22–24.
- Covariates that explain a significant part of the observed youth unemployment differential:
  - Marital status and geographical region jointly generate an unemployment gap that is 12 percent higher for youths in Uruguay relative to youths in LA5.
  - Married young adults appear more affected by unemployment; urban young Uruguayans face an unemployment rate 4 percent higher than observationally equivalent youths in rural areas and LA5.
- Much of the youth unemployment gap is likely due to structural factors and socio-demographic features; data limitations prevent formal testing with additional labor-market institutional covariates.

### Policy implications and recommendations
- Priority for multi-faceted recovery focused on youth vulnerability given marginal labor-market attachment and pandemic impact.
- Targeted policy solutions to facilitate faster transitions into the labor market:
  - Specialized skills training and apprenticeships (dual training systems combining general education with apprenticeships/training in specialized skills such as programming).
- Attention to informal and temporary employment among youths:
  - Design relief and recovery policies recognizing that many youths lack formal employment ties (unemployment benefits, insurance, payroll and income tax reductions, and paid sick leave are often linked to formal employment).
- Consider socio-demographic constraints:
  - Policies that enhance mobility and employment prospects for married and urban youths.
- Need for further research using microlevel administrative data to better understand structural causes of Uruguay’s youth unemployment gap.

*Source: wpiea2020281-print-pdf - 4.1 percent per year.*

### REFERENCES

### REFERENCES

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*Source: wpiea2020281-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020281-print-pdf.pdf_