## 1. Demographics of AI Exposure

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### Key findings and headline projections
- Well-targeted reforms in credit markets, trade facilitation, and labor market flexibility could increase output growth by up to 1.5 percentage points over the medium term.
- Advancements in artificial intelligence (AI) could raise GDP by as much as 10 percent over the next 30 years.
- Over the last 60 years Uruguay’s average growth has been 2.2 percent; this is lower than the 3.9 percent of its other LA7 peers.
- Average growth over the last 10 years has been 1.2 percent per year.
- Potential growth estimates using filtering methods yield a range of 1.9-2.5 percent; the median estimate for recent years is around 2.2 percent.

### Growth accounting and baseline outlook
- Historical drivers:
  - Since 1990, capital accumulation has been a consistent growth component.
  - Total factor productivity (TFP) has been important but volatile.
  - Labor’s contribution has declined as employment rose while average hours worked fell.
- Baseline assumptions for a potential growth scenario around 2.2 percent:
  - Real investment grows around 3-4 percent per year (the average of the last 25 years).
  - Labor force grows at 0.2 percent (migration flows and population growth according to the historical trend of the last 10 years).
  - Contribution from TFP growth is around 1 percent (close to the average over the last 20 years, excluding 2020-2021).
- Estimated long-term output gains from AI adoption:
  - Uruguayan output due to AI adoption is expected to increase between 8.5 percent and 9 percent over 30 years (estimated output gain between 2014-2048).
  - Productivity rise: Acemoglu (2025) forecasts a 1.5 percent increase due to AI adoption (annual rise in TFP of 0.05 percent, taken as 1.5 percent over 30 years).

### Structural gaps and competitiveness
- Strengths relative to LAC peers:
  - Strong performance on human capital, poverty reduction, macroeconomic stability, and governance.
- Persistent gaps:
  - Investment and total factor productivity growth remain disappointing.
  - High youth unemployment and regional disparities (notably in northern departments).
  - Limited global integration and participation in global value chains due to regulatory barriers and low competitiveness.
  - Dominance of state-owned enterprises (SOEs) in key sectors contributes to inefficiencies.
  - Infrastructure bottlenecks in logistics and transport hinder competitiveness.
  - Financial sector is stable but heavily dollarized, restricting credit access for micro, small, and medium-sized enterprises.
  - Firms perceive labor market practices as rigid; education faces challenges in quality, completion rates, and early learning outcomes.
- Gaps relative to global frontiers (2000–2022 normalization):
  - Business regulation, the credit market, external sector, governance, and the labor market show notable gaps.
  - Economic Complexity Index (ECI) analysis: widening trade gaps between 1998–2010 and 2011–2023; technology gap narrowed somewhat while trade-related complexity lagged.

### AI exposure and preparedness
- IMF AI Preparedness Index (AIPI) results:
  - Uruguay ranks highly in AI preparedness among Emerging Market economies.
  - Uruguay ranks in the top 5 for the regulation and ethics category.
- Labor force AI exposure and complementarity:
  - Overall exposure to AI in Uruguay is similar to the average Emerging Market economy.
  - The share of the workforce in occupations highly complementary with AI is similar to the Emerging Market average.
  - Uruguay’s AI exposure is below Chile and Mexico and similar to Brazil and Colombia.
- AI technologies and institutional context:
  - Generative AI (large language learning models) broaden application and accessibility.
  - AI models can be specialized for discrete tasks such as pattern identification or repetition, enabling automation of repetitive tasks and reshaping job functions.
  - Uruguay has agreed to adhere to the OECD AI Principles.
  - Uruguay ranks third in Latin America in the Latin American Artificial Intelligence Index by CEPAL and scores highly in the Government AI Readiness Index by Oxford Insights (2024) across Government, Technology Sector, and Data & Infrastructure pillars.

### Demographics and distribution of AI exposure and complementarity
- Age:
  - As workers age, they are less likely to be in high-exposure low-complementarity occupations; the share of high-exposure high-complementarity workers increases especially between ages 20-40 and stabilizes in the 40s and 50s.
- Education:
  - More years of education are associated with a greater share of employment in high-exposure occupations, particularly pronounced for high-complementarity occupations.
- Gender:
  - Females tend to work in occupations more exposed to AI than males.
  - A larger share of female workers are in occupations highly complementary to AI compared to males.
- Occupational and sectoral patterns:
  - Majority of Uruguayans work as service and sales workers, with varied AI exposure.
  - Examples: cooks, waiters, and hairdressers have low AI exposure; sales assistants are highly exposed with low complementarity; teachers’ aides and childcare workers face high exposure with high complementarity.
  - Managerial and professional occupations account for above 15 percent of the employed population and exhibit high exposure and high complementarity.
  - Clerical support workers and technician occupations generally present high exposure and low complementarity.
  - Skilled agricultural workers, craftsmen, plant operators and workers in elementary occupations have low exposure to AI.
  - Sectoral patterns by ISIC Rev. 4:
    - Wholesale, retail trade, and services contain a mix of occupations with varying exposure and complementarity.
    - Education: many roles both highly exposed and highly complementary to AI.
    - Health sector: benefits from AI in diagnostics, patient care, and efficiency.
    - Agriculture, construction, and manufacturing: less exposed due to manual labor, though innovation initiatives exist (e.g., INIA).
- Geography and informality:
  - Montevideo: 40 percent of the population live in Montevideo; workforce comprises a mix of high-exposure/high-complementarity, high-exposure/low-complementarity, and no-exposure occupations.
  - Canelones: majority share of workers not directly exposed to AI.
  - Uruguay’s informality rate is around 20-25 percent.
  - Within the informal workforce, the majority of workers are in low-exposure occupations.
  - The formal workforce (around 75 percent of workers) displays a mixture of high exposure and low exposure workers.
  - Risk: AI adoption could displace formal-sector jobs (notably high-exposure, low-complementarity) and potentially incentivize informality if not managed, given the labor tax wedge cost of formality.

### Distributional and inequality effects
- Baseline inequality:
  - Uruguay’s Gini index is around 40.
- Projected effects of AI adoption on inequality (estimated change between 2014-2048):
  - Wage inequality is expected to decrease in most scenarios.
  - Wealth inequality is expected to rise, with gains accruing predominantly to top earners via capital income.
  - Increased wealth concentration is particularly pronounced for the top 5 percentile and even stronger for the top 0.1 percentile.
- Income distribution patterns:
  - AI exposure is spread along the labor income distribution.
  - Employment in occupations at risk of displacement (high-exposure, low complementarity) is increasing across income quantiles (mildly positive trend).
  - High-exposure, high-complementarity jobs are more concentrated in upper-income quantiles, especially the top 20 percent of earners.
  - Positive correlation exists between high income workers and high complementarity to AI.

### Sectoral exposure and contribution to growth (Box 2)
- Relationship between sectoral value added and AI exposure:
  - Industry data from INE for 2017 (108 industries aggregated to 2-digit classification) show a positive correlation between sectors that contribute to GDP growth and those with higher AI exposure.
  - Figure described: "Value Added of Industry per Employee and by AI Exposure" (Value Added of Industry per Employee in Millions of Uruguayan Pesos; categories: Low Exposure to AI, High Exposure to AI).
- Implications:
  - Sectors integrating advanced technologies drive expansion but become sensitive to technological changes, implying volatility and potential instability in output.
- Model outcomes and distributional effects:
  - Wages increase due to aggregate productivity gains from AI.
  - Some workers experience negative wage impacts due to displacement if highly exposed to AI.
  - High-wage earners who are displaced are more likely to have capital income, which increases due to higher capital returns.
  - Baseline calibration indicates overall impact on income for all earners is positive compared to a no-AI world.
  - Numeric indicators shown in source figure ("Total Income Growth from AI over the Income Distribution"): 5.4, 5.6, 5.8, 6, 6.2, 6.4.
  - Exposure to AI axis annotated from 0 5 10 15 20 25.
- Uncertainty and assumptions:
  - Analysis is highly uncertain.
  - Model assumes the distribution of workers in AI-exposed professions does not change over the forecast period.
  - Analysis abstracts from global benefits of AI through trade linkages and from unpredictable pace of AI adoption.

### Macroeconomic impacts, reform dividends, and calibration
- Structural reform dividends (Budina et al. 2023 local projection estimates applied to Uruguay):
  - Closing the gap to the most advanced country in governance, business regulation, or external orientation could increase Uruguay’s annual growth by up to 1 percentage points.
  - Achieving the same score as the global frontier in credit market could increase growth by up to 1.2 percentage points.
  - Labor market reforms enhancing flexibility could dynamize the economy (weaker statistical significance reported).
- Calibration and modeling inputs:
  - Model calibrated using ECH household income distribution and AI exposure, EFHU-2 (2014 Uruguayan Household Finance Survey) for investor behavior, and Penn World Table data for capital and labor shares.
  - Results show ranges arising from varying the share of investors, capital risk, and assumed degree of complementarity.

### Exporter determinants, innovation, and firm-level evidence
- Firm-level exporter probit results (Table 3 highlights):
  - Longer import clearance times negatively impact export probability; time required to clear exports not significant.
  - Foreign-owned firms are more likely to export.
  - Firm size (log of workers) positively and significantly affects export decision.
- Selected coefficients (as reported):
  - Government efficiency coefficient: -0.0768*** (0.0161)
  - Legal/property rights coefficient: 0.217*** (0.0201)
  - Sound macroeconomics coefficient: 0.104*** (0.00775)
  - Free trade coefficient: 0.170*** (0.0119)
  - Regulation coefficient: 0.189*** (0.0185)
  - Exporter coefficients: 0.141*** (0.0423), 0.125*** (0.0429), 0.224*** (0.0502)
  - Foreign ownership coefficients (selected): 0.151*** (0.0428), 0.0723* (0.0434), 0.108** (0.0501), 0.207*** (0.0408), 0.198*** (0.0409), 0.169*** (0.0413), 0.170*** (0.0412), 0.187*** (0.0412)
  - Age of establishment coefficients (selected): -0.000174 (0.000695), -0.000806 (0.000703), 0.00161* (0.000855)
  - Number workers (logs) coefficients (selected): 0.192*** (0.0259), 0.143*** (0.0259), 0.221*** (0.0312), 0.210*** (0.0247)
- Sample and controls:
  - Observations across specifications: 11,955; 11,942; 6,562; 11,955; 11,955; 11,955; 11,955; 11,955
  - Other firm variables: YES (all columns)
  - Year fixed effects: YES (all columns)
  - Country fixed effects: YES for first three columns; NOT for remaining columns
- Exporters and innovation (Table 4 and Table 7 summaries):
  - Export firms are more likely to introduce new products and process innovations and to invest in R&D (statistically significant in three out of four specifications).
  - Export status does not significantly influence whether a product is entirely new to the main market.
  - Foreign-owned firms strongly associated with product innovation and market novelty.
  - Larger firms (log of workers) significantly more likely to innovate.
  - Economic freedom indicators influence firm likelihood of introducing a new product or service.

### Annex I—Methodology highlights for reform impact estimation
- Empirical framework:
  - Local projection approach in a panel data setting estimating impact of reforms on output.
  - Dependent variable: log of real GDP (PPP) for country i in year t+k.
  - Controls: country and year fixed effects (αi and λt), change in average structural reform score (ΔReform_i,t), and time-varying controls X′i,t including lags of dependent variable and past reforms.
  - Note: local projection approach is less robust to addressing endogeneity; large coefficients should be interpreted with caution.
  - Empirical estimates of βk from Budina and others (2023) are applied to Uruguay gap measures.

### Annex II—Selected WBES indicators evolution for Uruguay (selected values)
- Regulation/licensing:
  - Days to obtain an import license: 13.6 (URY 2006), 10.0 (URY 2010), 14.1 (URY 2017), 37.4 (URY 2024)
  - Days to obtain an operating license: 133.8 (URY 2006), 108.0 (URY 2010), 176.0 (URY 2017), 167.0 (URY 2024)
  - Percent of firms identifying business licensing/permits as a major constraint: 11.1, 8.4, 11.1, 25.1
  - Days to clear direct exports through customs: 2.7, 6.9, 2.1, 2.9
  - Days to clear imports from customs: 6.7, 9.2, 11.4, 3.4
  - Percent of firms identifying customs and trade regulations as a major constraint: 15.5, 8.4, 17.9, 28.4
- Labor market and skills:
  - Percent of firms identifying labor regulations as a major constraint: 22.7, 25.5, 24.0, 22.5
  - Percent of firms identifying an inadequately educated workforce as a major constraint: 24.5, 30.8, 37.0, 41.7
- Innovation and digital adoption:
  - Percent of firms that introduced a new product/service over last 3 years: 63.3, 58.5, 71.4, 72.6
  - Percent of firms whose new product/service is also new to the main market: 0.0, 46.3, 66.9, 59.4
  - Percent of firms that introduced a process innovation over last 3 years: 57.5, 42.5, 66.6, 53.7
  - Percent of firms that spend on R&D in the last fiscal years: 42.7, 55.5, 18.2, 29.8
  - Percent of firms having their own web site: 33.3, 50.9, 69.1, 80.6
- Credit access and demand:
  - Percent of firms with a bank loan/line of credit: 45.0, 48.6, 54.1, 64.1
  - Percent of firms identifying access to finance as a major constraint: 31.3, 16.0, 20.6, 18.6
  - Percent of firms not needing a loan: 40.2, 44.7, 48.1, 54.2
  - Percent of firms whose recent loan application was rejected: 0.0, 0.0, 4.2, 2.6
  - Percent of firms that are fully credit constrained: 0.0, 0.0, 2.4, 2.6
  - Percent of firms that are partially credit constrained: 0.0, 0.0, 14.2, 10.0
  - Percent of firms that are credit unconstrained: 0.0, 0.0, 83.4, 87.4

### Annex III—“Exporter Premium” and Uruguay-specific patterns
- Cross-region findings:
  - Advanced economies: exporters exhibit lower TFP compared to non-exporters (negative and statistically significant).
  - Emerging and developing economies: exporters show a positive TFP effect.
  - LAC and Uruguay: no statistically significant differences in TFP between exporters and non-exporters.
  - Across regions, exporters are larger and more likely to be foreign owned; proportion of skilled workers among exporters is lower in some regions.
- Uruguay-specific values (Annex III Table 1):
  - TFP (output method): -0.234
  - TFP (VA method): -0.288
  - Age of the establishment (years): 9.670*** (Uruguay)
  - Ownership type (foreign vs. domestic): 0.141*** (Uruguay)
  - Number of workers: 78.91*** (Uruguay)
  - Proportion of skilled workers, out of: -3.310 (Uruguay)
  - Real annual sales growth (%): -1.728 (Uruguay)
  - Annual employment growth (%): -0.439 (Uruguay)
  - Real annual labor productivity growth: -1.111 (Uruguay)
  - Capacity utilization (%)*: 3.152* (Uruguay)
  - Sales per worker (in USD 2009): 91,431*** (Uruguay)
  - Percent of firms having their own web: 26.42*** (Uruguay)
- Interpretation:
  - Exporters in Uruguay do not show statistically significant TFP advantages in the short term; they are significantly older, employ more workers, and are more likely to be foreign owned.

### Annex IV—Index of Bureaucracy Costs (selected Uruguay figures)
- Average times (cross-country):
  - It takes an average of 3.7 months, or 154 working days, to start a business.
  - An additional 112 working days per year are dedicated to compliance to keep the business operational.
- Uruguay — Opening Procedures (annual hours):
  - Total (annual hours): 1,080
  - Identification and registration: 840
  - Registration with national entities: 72
  - Request for utilities: 168
  - Specific by productive activity: 5
- Uruguay — Operating Procedures (annual hours):
  - Total (annual hours): 414
  - Employment Administration: 201
  - Wages, taxes, and contrib.: 174
  - Hiring and firing: 27
  - Operations Management: 156
  - Taxation: 136
  - Waste Management: 12
  - Vehicle: 5
  - Occupational health/safety: 3
  - Other: 57
  - Regulatory update: 10
  - Specific by activity: 47

### Policy implications and prioritized reform agenda
- Overarching goal:
  - Implement a comprehensive structural reform agenda to boost productivity, foster innovation, and ensure sustained long-term growth to achieve higher income per capita and accelerate convergence with the world's most developed economies.
- Priority reform areas and specific recommendations:
  - Strengthening Financial Market Development:
    - Adapt regulations to facilitate a level playing field in financial markets.
    - Reduce frictions in credit markets by enhancing credit information.
    - Develop capital markets to provide additional and well-diversified funding options for firms.
  - Enhancing Competition and Business Regulation:
    - Streamline bureaucratic procedures by simplifying administrative requirements for firm registration, operating licenses, and taxation.
    - Enhance competition and adapt the regulatory framework to foster innovation, particularly in high-impact industries.
  - Harnessing AI adoption to provide equitable growth:
    - Prioritize investment in digital infrastructure and human capital to enhance AI preparedness.
    - Promote equitable and ethical integration of AI-driven innovation into education, industry and the public sector.
    - Offer retraining programs for vulnerable workers to limit labor force disruptions caused by AI adoption.
  - Labor market reforms to improve flexibility and skill development:
    - Modernize wage bargaining frameworks and account for firm heterogeneity to increase labor market flexibility.
    - Promote active labor market policies such as retraining programs, job-matching services, and targeted incentives for hiring young workers.
    - Boost education quality, encourage school completion, and align education and training programs with evolving labor market needs.
  - Deepening trade liberalization and export competitiveness:
    - Reduce trade-related costs by simplifying customs procedures and enhancing logistics efficiency.
    - Diversify export markets via new trade agreements and leverage regional trade blocs.
    - Prioritize competitiveness of services exports (IT, consulting, business services); attract skilled labor and reduce regulatory barriers.
- Summary implications:
  - Pursuing the outlined structural reforms could increase productivity, attract investment, and enhance competitiveness.
  - Reforms can help close structural gaps, create new economic opportunities, and position Uruguay as a leader in Latin America in economic resilience and innovation.
  - Long-run strategy: expand strategic investments, infuse global knowledge via trade integration and FDI, and prioritize domestic innovation, targeted R&D, support for high-tech sectors, and institutional reforms to avoid the “middle income trap.”

*Source: IMF staff chapter "1. Demographics of AI Exposure" from SIPEA2025 (sipea2025142).*

### 1. Demographics of AI Exposure ______________________________________________________ 11

### 1. Demographics of AI Exposure

### Key findings and headline projections
- Well-targeted reforms in credit markets, trade facilitation, and labor market flexibility could increase output growth by up to 1.5 percentage points over the medium term.
- Advancements in artificial intelligence (AI) could raise GDP by as much as 10 percent over the next 30 years.
- Over the last 60 years Uruguay’s average growth has been 2.2 percent; this is lower than the 3.9 percent of its other LA7 peers.
- Average growth over the last 10 years has been 1.2 percent per year.
- Potential growth estimates using filtering methods yield a range of 1.9-2.5 percent; the median estimate for recent years is around 2.2 percent.

### Growth accounting and outlook
- Historical drivers:
  - Since 1990, capital accumulation has been a consistent growth component.
  - Total factor productivity (TFP) has been important but volatile.
  - Labor’s contribution has declined as employment rose while average hours worked fell.
- Baseline assumptions for a potential growth scenario around 2.2 percent:
  - Real investment grows around 3-4 percent per year (the average of the last 25 years).
  - Labor force grows at 0.2 percent (migration flows and population growth according to the historical trend of the last 10 years).
  - Contribution from TFP growth is around 1 percent (close to the average over the last 20 years, excluding 2020-2021).

### Structural gaps and competitiveness
- Uruguay performs strongly on many socio-economic indicators relative to LAC peers, including human capital, poverty reduction, macroeconomic stability, and governance.
- Noted persistent gaps:
  - Investment and total factor productivity growth remain disappointing.
  - High youth unemployment and regional disparities (notably in northern departments).
  - Limited global integration and participation in global value chains due to regulatory barriers and low competitiveness.
  - Dominance of state-owned enterprises (SOEs) in key sectors contributes to inefficiencies.
  - Infrastructure bottlenecks in logistics and transport hinder competitiveness.
  - Financial sector is stable but heavily dollarized, restricting credit access for micro, small, and medium-sized enterprises.
  - Firms perceive labor market practices as rigid; education faces challenges in quality, completion rates, and early learning outcomes.
- Structural gaps relative to global frontiers (2000–2022 normalization):
  - Compared to Advanced Economies and LA7 frontiers, gaps are present in business regulation, the credit market, external sector, governance, and the labor market.
  - Economic Complexity Index (ECI) analysis shows widening trade gaps between 1998–2010 and 2011–2023; technology gap has narrowed somewhat but trade-related complexity lagged.

### AI exposure and preparedness
- Uruguay ranks highly in AI preparedness among Emerging Market economies according to the IMF’s AI Preparedness Index (AIPI), particularly high for regulation and ethics, placing the country in the top 5 for that category.
- Uruguay’s labor force AI exposure:
  - Overall exposure to AI in Uruguay is similar to the average Emerging Market economy.
  - The share of the workforce in occupations highly complementary with AI is similar to the Emerging Market average.
  - Uruguay’s AI exposure is below Chile and Mexico and similar to Brazil and Colombia (Bakker et al., 2024).
- AI technologies noted:
  - Generative AI (large language learning models) broaden application and accessibility.
  - AI models can also be specialized for discrete tasks such as pattern identification or repetition, enabling automation of repetitive tasks and reshaping job functions.
- Institutional and policy context:
  - Uruguay has agreed to adhere to the OECD AI Principles, promoting trustworthy and responsible AI systems.
  - Uruguay ranks third in Latin America in the Latin American Artificial Intelligence Index by CEPAL and scores highly in the Government AI Readiness Index by Oxford Insights (2024) across Government, Technology Sector, and Data & Infrastructure pillars.

### Policy implications and reform priorities emphasized
- Prioritize structural reforms to close gaps in competitiveness and harness AI benefits; key areas highlighted:
  - Financial deepening to improve credit access, especially for micro, small, and medium-sized enterprises.
  - Trade facilitation and reductions in regulatory barriers to enhance integration into global value chains and export sophistication.
  - Labor market reforms to increase flexibility and reduce perceived rigidity (including wage-setting and hiring practices).
  - Measures to boost innovation, research capabilities, and economic complexity to narrow trade and technology gaps.
  - Address infrastructure bottlenecks in logistics and transport to raise competitiveness.
  - Strengthen education outcomes (quality, completion rates, early learning) to support human capital for technological adoption.
  - Leverage strong regulation and ethics frameworks to promote trustworthy AI adoption while managing reallocation risks between labor and capital.

*Source: IMF staff chapter "1. Demographics of AI Exposure" from SIPEA2025 (sipea2025142).*

### 15.  AI exposure measures the ability of an AI to replicate human abilities. To study the

### sipea2025142 - 15.  AI exposure measures the ability of an AI to replicate human abilities. To study the

### Concept and measurement
- AI Occupational Exposure (AIOE) index from Felten et al. (2021) is used to measure overlap between AI applications and human abilities required by occupations.
- AI Complementarity index (see Annex) measures an occupation’s shielding and complementary potential to AI; paired with high exposure, it indicates AI complementarity potential.
- Uruguayan occupations at the 4-digit ISCO 08 level in the Encuesta de Continua de Hogares (ECH, INE) are matched to Felten et al. (2021) exposure scores.

### Demographics and distribution of AI exposure and complementarity
- Age:
  - As Uruguayan workers age, they are less likely to be in high-exposure low-complementarity occupations; the share of high-exposure high-complementarity workers increases especially between ages 20-40 and stabilizes in the 40s and 50s.
- Education:
  - More years of education are associated with a greater share of employment in high-exposure occupations, particularly pronounced for high-complementarity occupations.
- Gender:
  - Females tend to work in occupations more exposed to AI than males.
  - A larger share of female workers are also in occupations highly complementary to AI compared to males, implying both greater risks and greater opportunities for women.

### Occupation- and sector-level patterns
- Occupational composition:
  - Majority of Uruguayans work as service and sales workers, which have varied exposure to AI.
  - Examples: cooks, waiters, and hairdressers have low AI exposure; sales assistants are highly exposed with low complementarity; teachers’ aides and childcare workers face high exposure with high complementarity.
  - Managerial and professional occupations account for above 15 percent of the employed population and exhibit high exposure and high complementarity.
  - Clerical support workers and technician occupations generally present high exposure and low complementarity.
  - Skilled agricultural workers, craftsmen, plant operators and workers in elementary occupations have low exposure to AI.
- Sectoral patterns (by ISIC Rev. 4):
  - Wholesale, retail trade, and services contain a mix of occupations with varying exposure and complementarity.
  - Education: many roles both highly exposed and highly complementary to AI.
  - Health sector: benefits from AI in diagnostics, patient care, and efficiency.
  - Agriculture, construction, and manufacturing: less exposed due to manual labor, though innovation initiatives exist (e.g., INIA).

### Regional and labor-status distribution
- Geography:
  - Montevideo: 40 percent of the population live in Montevideo; workforce comprises a mix of high-exposure/high-complementarity, high-exposure/low-complementarity, and no-exposure occupations.
  - Canelones: majority share of workers not directly exposed to AI.
- Informality:
  - Uruguay’s informality rate is around 20-25 percent.
  - Within the informal workforce, the majority of workers are in low-exposure occupations.
  - The formal workforce (around 75 percent of workers) displays a mixture of high exposure and low exposure workers.
  - Risk: AI adoption could displace formal-sector jobs (notably high-exposure, low-complementarity) and potentially incentivize informality if not managed, given the labor tax wedge cost of formality.

### Distributional and income-related patterns
- Income distribution:
  - AI exposure is spread along the labor income distribution.
  - Employment in occupations at risk of displacement (high-exposure, low complementarity) is increasing across income quantiles (mildly positive trend).
  - High-exposure, high-complementarity jobs are more concentrated in upper-income quantiles, especially the top 20 percent of earners.
  - Positive correlation exists between high income workers and high complementarity to AI.

### Macroeconomic impacts, reform dividends, and calibration
- Structural reform dividends (empirical local projection estimates from Budina et al. 2023 applied to Uruguay):
  - Reforms closing the gap to the most advanced country in governance, business regulation, or external orientation could increase Uruguay’s annual growth by up to 1 percentage points.
  - Achieving the same score as the global frontier in credit market could increase growth by up to 1.2 percentage points.
  - Labor market reforms enhancing flexibility could dynamize the economy (weaker statistical significance reported).
- Calibration and modeling inputs:
  - Model calibrated using ECH household income distribution and AI exposure, EFHU-2 (2014 Uruguayan Household Finance Survey) for investor behavior, and Penn World Table data for capital and labor shares.
- Estimated long-term output gains from AI adoption:
  - Uruguayan output due to AI adoption is expected to increase between 8.5 percent and 9 percent over 30 years (estimated output gain between 2014-2048).
  - Productivity rise: Acemoglu (2025) forecasts a 1.5 percent increase due to AI adoption (annual rise in TFP of 0.05 percent, taken as 1.5 percent over 30 years).
  - Results are shown as ranges from calibrations varying the share of investors, capital risk, and assumed degree of complementarity.

### Inequality effects
- Baseline inequality:
  - Uruguay’s Gini index is around 40.
- Projected effects of AI adoption on inequality (estimated change between 2014-2048):
  - Wage inequality is expected to decrease in most scenarios.
  - Wealth inequality is expected to rise, with gains accruing predominantly to top earners via capital income.
  - Increased wealth concentration is particularly pronounced for the top 5 percentile and even stronger for the top 0.1 percentile.

*Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2025/english/sipea2025142.pdf*

### Box 2. Sectoral Exposure to AI and Contribution to Growth

### Box 2. Sectoral Exposure to AI and Contribution to Growth

### Relationship between sectoral value added and AI exposure
- Data: industry data from INE for 2017, aggregating to the 2-digit industry classification covering 108 industries.
- Finding: A compelling positive correlation exists between sectors that significantly contribute to GDP growth and those that exhibit higher exposure to AI.
- Visualization (described): "Value Added of Industry per Employee and by AI Exposure" (Value Added of Industry per Employee in Millions of Uruguayan Pesos; categories: Low Exposure to AI, High Exposure to AI).
- Sources: ECH 2023; and IMF staff calculations.

### Implications for growth and volatility
- Sectors driving economic expansion are often those integrating advanced technologies into their operations.
- As these sectors become increasingly sensitive to technological changes, both opportunities and challenges arise for their performance.
- Consequence: These sectors may experience sudden shifts in operational capacities due to dependence on technological progress, leading to instability in output.
- Summary statement: AI’s potential to boost economic performance is not without risks.

### Distributional effects from the model and firm/worker outcomes
- Model calibration and outcome:
  - Wages increase due to the aggregate productivity gain from AI.
  - Some workers see a negative impact on their wages due to job displacement because they are highly exposed to AI.
  - High-wage earners who are displaced are more likely to have capital income, which increases due to higher capital returns.
  - Despite labor displacement, the baseline calibration of the model indicates that the overall impact on income for all earners is positive compared to a no-AI world.
- Numeric indicators shown (figure captioned "Total Income Growth from AI over the Income Distribution"):
  - Percent change values shown: 5.4, 5.6, 5.8, 6, 6.2, 6.4.
  - Exposure to AI axis annotated from 0 5 10 15 20 25 (as shown in source figure).

### Uncertainty, model assumptions, and omitted channels
- Overall impact of AI is characterized as highly uncertain.
- Descriptive analysis (Section C) provides a snapshot of which professions and sectors in Uruguay are most exposed to AI.
- Model assumption: the distribution of workers in AI-exposed professions does not change over the forecast period.
- Caveat: Over longer horizons, workers will likely migrate across sectors and roles, acquire new skills, and jobs will evolve.
- Additional abstractions in analysis:
  - The analysis abstracts from global benefits of AI, which could occur through trade linkages and cross-border spillovers of AI exposure.
  - The analysis abstracts from the unpredictable pace of AI adoption.

*Source: Box 2, "Sectoral Exposure to AI and Contribution to Growth," sipea2025142 (ECH 2023; IMF staff calculations).*

### 36. There is evidence that reducing individual-level trade-related frictions or reducing

### 36. There is evidence that reducing individual-level trade-related frictions or reducing

### Exporter determinants (firm- and country-level findings)
- Firm-level probit regression results (Table 3, column 1) indicate:
  - Longer import clearance times, as reported at the firm-level, negatively impact export probability.
  - Time required to clear exports does not have a significant effect.
  - Foreign-owned firms are consistently more likely to export.
  - Firm size (measured by the log of workers) has a positive and significant effect on the decision to export.
- Probit regression results in columns 2–7 show various country-level economic freedom indicators significantly affect export probability:
  - Government efficiency coefficient: -0.0768*** (standard error 0.0161)
  - Legal/property rights coefficient: 0.217*** (standard error 0.0201)
  - Sound macroeconomics coefficient: 0.104*** (standard error 0.00775)
  - Free trade coefficient: 0.170*** (standard error 0.0119)
  - Regulation coefficient: 0.189*** (standard error 0.0185)
- Selected firm-level coefficients reported across specifications:
  - Exporter coefficients (three reported): 0.141*** (0.0423), 0.125*** (0.0429), 0.224*** (0.0502)
  - Foreign ownership coefficients across eight columns: 0.151*** (0.0428), 0.0723* (0.0434), 0.108** (0.0501), 0.207*** (0.0408), 0.198*** (0.0409), 0.169*** (0.0413), 0.170*** (0.0412), 0.187*** (0.0412)
  - Age of the establishment coefficients (eight columns): -0.000174 (0.000695), -0.000806 (0.000703), 0.00161* (0.000855), 8.87e-05 (0.000676), -0.000176 (0.000677), 0.000313 (0.000676), 0.000593 (0.000677), 0.000536 (0.000676)
  - Number workers (logs) coefficients (eight columns): 0.192*** (0.0259), 0.143*** (0.0259), 0.221*** (0.0312), 0.210*** (0.0247), 0.210*** (0.0248), 0.204*** (0.0249), 0.196*** (0.0249), 0.198*** (0.0248)
- Sample and controls:
  - Observations reported across specifications: 11,955; 11,942; 6,562; 11,955; 11,955; 11,955; 11,955; 11,955
  - Other firm variables: YES (all columns)
  - Year fixed effects: YES (all columns)
  - Country fixed effects: YES for first three columns; NOT for remaining columns
  - Robust standard errors reported in parentheses

### Exporters and innovation
- Empirical evidence (Table 4, columns 1–3) shows firm characteristics drive innovation:
  - Export firms are more likely to introduce new products and process innovations, and to invest in R&D, with statistically significant and positive coefficients in three out of four specifications.
  - Export status does not significantly influence whether a product is entirely new to the main market.
  - Foreign-owned firms show a strong positive relationship with product innovation and market novelty.
  - Larger firms (log of workers) are significantly more likely to innovate.
- Table 7 (columns 4–8) indicates various aspects of economic freedom significantly influence a firm's likelihood of introducing a new product or service.

### Policy conclusions and prioritized reform areas (G. Conclusion)
- Overarching goal:
  - Implement a comprehensive structural reform agenda aimed at boosting productivity, fostering innovation, and ensuring sustained long-term growth to achieve higher income per capita and accelerate convergence with the world's most developed economies.
- Key reform areas and specific recommendations:
  - Strengthening Financial Market Development:
    - a. Adapting regulations to facilitate a level playing field in the financial markets to contribute to financial development.
    - b. Reducing frictions in the credit markets by enhancing credit information.
    - c. Developing capital markets to provide additional and well-diversified funding options for firms by adapting regulations to the size and capacity of firms.
  - Enhancing Competition and Business Regulation:
    - a. Streamlining bureaucratic procedures by simplifying administrative requirements for firm registration, operating licenses, and taxation.
    - b. Enhancing competition.
    - c. Adapting the regulatory framework to foster innovation, particularly in high-impact industries.
  - Harnessing AI adoption to provide equitable growth:
    - a. Prioritize investment in digital infrastructure and human capital to enhance AI preparedness.
    - b. Promote equitable and ethical integration of AI-driven innovation into education, industry and the public sector.
    - c. Offer retraining programs for vulnerable workers to limit labor force disruptions caused by AI adoption.
  - Labor market reforms to improve flexibility and skill development:
    - a. Modernize wage bargaining frameworks and account for firm heterogeneity to increase labor market flexibility.
    - b. Promote active labor market policies such as retraining programs, job-matching services, and targeted incentives for hiring young workers.
    - c. Boost education quality, encourage school completion, and align education and training programs with evolving labor market needs.
  - Deepening trade liberalization and export competitiveness:
    - a. Reduce trade-related costs by simplifying customs procedures or enhancing logistics efficiency. Diversify export markets by negotiating new trade agreements and leveraging regional trade blocs to expand market access.
    - b. Prioritize policies that enhance competitiveness of services export industry, particularly in high-value sectors like IT, consulting, and business services; attract skilled labor, foster talent development, and reduce regulatory barriers.

### Summary implications
- Pursuing the outlined structural reforms could:
  - Increase productivity, attract investment, and enhance competitiveness.
  - Help close structural gaps, create new economic opportunities, and position Uruguay as a leader in Latin America in economic resilience and innovation.
- Long-run strategy for a middle-income country (Uruguay):
  - Build on institutional foundations by expanding strategic investments in crucial areas to strengthen competitiveness.
  - Later stages: infuse global knowledge and technologies via trade integration, foreign direct investment, and innovation partnerships.
  - Ultimately prioritize domestic innovation, targeted R&D, support for high-tech sectors, and institutional reforms promoting competition and skill development to avoid the “middle income trap.”

### Annex I—Structural indicators and impact of reforms (methodology highlights)
- Empirical framework: local projection approach in a panel data setting estimating impact of reforms on output:
  - Dependent variable: log of real GDP (PPP) for country i in year t+k.
  - Controls: country and year fixed effects (αi and λt), change in average structural reform score (ΔReform_i,t), and time-varying controls X′i,t including lags of dependent variable and past reforms.
  - Note: local projection approach is less robust to addressing endogeneity; large coefficients should be interpreted with caution.
  - Empirical estimates of βk from Budina and others (2023) are applied to Uruguay gap measures in Figure 2.
- Annex I. Table 1: Definitions of Structural Indicators (sources and definitions preserved):
  - Governance — World Bank, Worldwide Governance Indicators: perception of quality of governance along six dimensions.
  - External sector — Fraser Institute, Index of Economic Freedom: degree of trade and financial openness along four dimensions.
  - Business regulation — Fraser Institute, Index of Economic Freedom: degree to which regulation and bureaucratic procedures restrain entry and reduce competition.
  - Credit market regulation — Fraser Institute, Index of Economic Freedom: extent credit is supplied to private sector, interest rate controls, banking industry private ownership.
  - Labor market regulation — Fraser Institute, Index of Economic Freedom: degree of flexibility of hiring and firing regulation, and centralized collective bargaining.
- Footnotes:
  - 1 See Jordà and Taylor (2025) for a survey on the literature.
  - 2 Each reform area includes multiple sub-indicators, with 1 representing the highest score.

### Annex II—Selected WBES indicators evolution for Uruguay (key values across survey years)
- Regulation/licensing:
  - Days to obtain an import license: 13.6 (URY 2006), 10.0 (URY 2010), 14.1 (URY 2017), 37.4 (URY 2024)
  - Days to obtain an operating license: 133.8 (URY 2006), 108.0 (URY 2010), 176.0 (URY 2017), 167.0 (URY 2024)
  - Percent of firms identifying business licensing/permits as a major constraint: 11.1, 8.4, 11.1, 25.1
  - Days to clear direct exports through customs: 2.7, 6.9, 2.1, 2.9
  - Days to clear imports from customs: 6.7, 9.2, 11.4, 3.4
  - Percent of firms identifying customs and trade regulations as a major constraint: 15.5, 8.4, 17.9, 28.4
- Labor market:
  - Percent of firms identifying labor regulations as a major constraint: 22.7, 25.5, 24.0, 22.5
  - Percent of firms identifying an inadequately educated workforce as a major constraint: 24.5, 30.8, 37.0, 41.7
  - Percent of firms choosing practices of the informal sector as their biggest obstacle: 32.4, 23.2, 16.2, 18.7
- Tax policy and administration:
  - Percent of firms identifying tax rates as a major constraint: 66.3, 45.5, 50.7, 52.7
  - Percent of firms identifying tax administration as a major constraint: 31.1, 27.1, 30.5, 30.4
- Innovation:
  - Percent of firms that introduced a new product/service over last 3 years: 63.3, 58.5, 71.4, 72.6
  - Percent of firms whose new product/service is also new to the main market: 0.0, 46.3, 66.9, 59.4
  - Percent of firms that introduced a process innovation over last 3 years: 57.5, 42.5, 66.6, 53.7
  - Percent of firms that spend on R&D in the last fiscal years: 42.7, 55.5, 18.2, 29.8
  - Percent of firms using technology licensed from foreign companies*: 4.7, 4.2, 23.0, 15.7
  - Percent of firms having their own web site: 33.3, 50.9, 69.1, 80.6
- Credit access and demand:
  - Percent of firms with a bank loan/line of credit: 45.0, 48.6, 54.1, 64.1
  - Percent of firms identifying access to finance as a major constraint: 31.3, 16.0, 20.6, 18.6
  - Percent of firms not needing a loan: 40.2, 44.7, 48.1, 54.2
  - Percent of firms whose recent loan application was rejected: 0.0, 0.0, 4.2, 2.6
  - Percent of firms that are fully credit constrained: 0.0, 0.0, 2.4, 2.6
  - Percent of firms that are partially credit constrained: 0.0, 0.0, 14.2, 10.0
  - Percent of firms that are credit unconstrained: 0.0, 0.0, 83.4, 87.4
  - Percent of firms with a checking or savings account: 88.0, 90.8, 96.9, 98.9
- Investment financing shares (selected):
  - Percent investment financed internally: 82.0, 69.6, 69.9, 69.0
  - Percent investment financed by banks: 8.4, 9.1, 18.8, 18.5
  - Percent investment financed by supplier/customer credit: 4.8, 6.6, 5.2, 9.0
  - Percent investment financed by equity or stock sales: 0.2, 6.6, 3.1, 2.2
  - Percent investment financed by other financing: 4.5, 8.0, 3.2, 1.4
  - Percent loans requiring collateral: 78.3, 55.1, 40.4, 35.7
  - Percent of firms using banks to finance investments: 6.9, 13.7, 31.8, 28.8
- Working capital financing shares (selected):
  - Percent working capital financed internally: 72.3, 69.9, 62.4, 60.3
  - Percent working capital financed by banks: 7.0, 7.6, 11.7, 18.0
  - Percent working capital financed by supplier/customer credit: 15.0, 18.6, 23.3, 20.2
  - Percent of firms using banks to finance working capital: 20.6, 26.4, 39.8, 46.1
- Other firm perceptions:
  - Percent of firms choosing political instability as their biggest obstacle: 5.7, 0.4, 2.0, 2.0
  - Percent of firms choosing crime, theft and disorder as their biggest obstacle: 5.1, 7.2, 1.3, 3.4
  - Percent of firms identifying corruption as a major constraint: 43.0, 21.1, 17.5, 24.2
  - Percent of firms believing the court system is fair, impartial and uncorrupted: 55.0, 50.0, 55.1, 0.0

### Annex III—The “Exporter Premium” (regional patterns and Uruguay-specific findings)
- Cross-region findings:
  - Advanced economies (OECD and AE): exporters exhibit lower TFP compared to non-exporters with negative and statistically significant coefficients for both TFP estimates.
  - Emerging and developing economies: exporters show a positive TFP effect.
  - LAC and Uruguay: no statistically significant differences in TFP between exporters and non-exporters.
  - Across all regions, exporters are typically larger (employ significantly more workers) and more likely to be foreign owned.
  - Proportion of skilled workers among exporters is consistently lower across regions, particularly in advanced economies and the OECD.
  - Real annual sales and productivity growth are significantly higher in emerging and advanced economies for exporters; impact in LAC is weaker.
  - Exporter firms are more likely to have a website across regions.
- Uruguay-specific patterns (Annex III Table 1):
  - TFP (output method): -0.234 (Uruguay)
  - TFP (VA method): -0.288 (Uruguay)
  - Age of the establishment (years): 9.670*** (Uruguay)
  - Ownership type (foreign vs. domestic): 0.141*** (Uruguay)
  - Number of workers: 78.91*** (Uruguay)
  - Proportion of skilled workers, out of: -3.310 (Uruguay)
  - Real annual sales growth (%): -1.728 (Uruguay)
  - Annual employment growth (%): -0.439 (Uruguay)
  - Real annual labor productivity growth: -1.111 (Uruguay)
  - Capacity utilization (%)*: 3.152* (Uruguay)
  - Sales per worker (in USD 2009): 91,431*** (Uruguay)
  - Percent of firms having their own web: 26.42*** (Uruguay)
- Interpretation for Uruguay:
  - Exporters in Uruguay do not show statistically significant TFP advantages in the short term; coefficients for sales and productivity growth are close to zero or negative.
  - Exporting firms in Uruguay are significantly older, employ more workers, and are more likely to be foreign owned.
  - Uruguay has the highest website adoption rate among exporters compared with LAC and OECD in the presented comparisons, suggesting greater digital integration among exporters.

*Source: Excerpt from sipea2025142 (Uruguay), INTERNATIONAL MONETARY FUND.*

### Annex IV. Index of Bureaucracy Costs

### Annex IV. Index of Bureaucracy Costs

### Overview
- The 2024 Index of Bureaucracy, published by the Adam Smith Center for Economic Freedom, examines administrative burdens faced by small businesses in Latin America and three European countries.
- The report covers 16 countries in Latin America and three in Europe and highlights intricate processes, pervasive corruption, and cumbersome administrative procedures that hinder innovation and economic growth.
- The findings aim to offer policymakers actionable recommendations to streamline these processes, thereby fostering more dynamic and resilient economies.

### Methodology
- Administrative burden is calculated based on time required to comply with national bureaucratic procedures for starting and operating small businesses.
- Methodological steps include:
  - Identifying and categorizing essential administrative tasks (tax compliance, labor regulations, mandatory reporting).
  - Structured interviews with business owners, legal experts, and policymakers to estimate time needed for each task.
  - Quantifying total bureaucratic burden in terms of working days per year for cross-country comparisons.
  - Incorporating qualitative assessments to capture inefficiencies such as redundant paperwork, digitalization levels, and prevalence of corruption.

### Key findings and statistics
- Average times reported:
  - It takes an average of 3.7 months, or 154 working days, to start a business.
  - An additional 112 working days per year are dedicated to compliance to keep the business operational.
- Cross-country disparities:
  - Brazil, Costa Rica, and Mexico rank among the least bureaucratic countries in the sample for starting and operating a business.
  - Venezuela and Bolivia face challenges in both starting and operating dimensions.
  - Some countries perform well in one area but not the other.
  - Uruguay shows particular inefficiencies in business opening procedures, notably identification and registration processes.
- Policy emphasis:
  - The findings underscore the need for targeted reforms to reduce administrative hurdles, promote transparency, and enhance efficiency within public bureaucracies to support small business growth and economic development.

### Uruguay — Opening Procedures (Annex IV. Table 1)
- Note: LA7 includes Brazil, Chile, Colombia, Mexico, Paraguay, Peru and Uruguay.
- Source: 2024 Index of Bureaucracy, Adam Smith Center for Economic Freedom.
- Uruguay figures (as reported):
  - Total (annual hours): 1,080
  - Identification and registration: 840
  - Registration with national entities: 72
  - Municipal Registration & Permits: -
  - Request for utilities: 168
  - Specific by productive activity: 5

### Uruguay — Operating Procedures (Annex IV. Table 2)
- Note: LA7 includes Brazil, Chile, Colombia, Mexico, Paraguay, Peru and Uruguay.
- Source: 2024 Index of Bureaucracy, Adam Smith Center for Economic Freedom.
- Uruguay figures (as reported):
  - Total (annual hours): 414
  - Employment Administration: 201
  - Wages, taxes, and contrib.: 174
  - Hiring and firing: 27
  - Operations Management: 156
  - Taxation: 136
  - Waste Management: 12
  - Vehicle: 5
  - Occupational health/safety: 3
  - Other: 57
  - Regulatory update: 10
  - Specific by activity: 47

*Source: Annex IV. Index of Bureaucracy Costs, 2024 Index of Bureaucracy, Adam Smith Center for Economic Freedom (as presented in the source PDF).*

### 236. Chicago: University of Chicago Press.

### 236. Chicago: University of Chicago Press.

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*Source: sipea2025142 - 236. Chicago: University of Chicago Press.*

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_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2025/english/sipea2025142.pdf_