## 1. Impacts of Potential Demographics Changes

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### ARUBA’S DEMOGRAPHICS AND POTENTIAL IMPACT ON THE LABOR MARKET AND OUTPUT — Stylized facts
- Fertility and births
  - Birth rate (number of births per 1,000 people) reached a historic low of 759 in 2024.
  - Civil Registry and Population Office: number of births per 1,000 women — 1972: 20.69; 1980: 18.78; 1990: 18.17; 2000: 14.26; 2010: 11.94; currently: 7.59.
  - UN statistics: Total Fertility Rate (TFR) — 1950: 5.83; 1960: 4.57; 1970: 2.97; 1980: 2.20; 1990: 2.35; 2000: 1.85; 2010: 1.86; 2020: 1.66; currently: 1.60.
- Aging population and demographics
  - Cohort aged 65+ increased from approximately 7 percent in 1990 to over 19 percent by end of 2024.
  - Median population age: 23.9 years in 1960; 42 years in 2020.
  - Proportion of women increased from 51 percent to 53 percent over 1960–2024.
- Population growth dynamics
  - Population nearly doubled since 1960; most growth occurred before 2010.
  - 1989–1993: average growth rates over 5 percent; population increased nearly 40 percent from 1990 to 2000.
  - Growth slowed to 13 percent in the early 2000s and to 7.8 percent in the 2010s.
  - Since the pandemic onset, population growth stagnated due to historic low birth rates and diminished inward migration; current primary drivers are immigration and emigration flows.
- Migration and foreign-born population (end-December 2024)
  - 39.5 percent of Aruba’s population was foreign-born.
  - Foreign-born women ≈ 56 percent of the migrant population.
  - Among migrants: working-age females (15–64) 46 percent; working-age males 36 percent; children 5 percent; age 65+ 14 percent.
  - By end-2024, 22 percent of inward migrants either returned to Aruba or came from other Dutch Caribbean islands.
  - Remaining foreign-born primarily from Venezuela 19.7 percent, Colombia 19.6 percent, and the Netherlands 14.6 percent.
  - Average of around 3,000 Arubans emigrated each year in the past decade, with a pandemic peak in 2020.
  - De-registrations in 2024: emigrants born in Aruba or the former Netherlands Antilles accounted for 53.5 percent.
- Population structure and dependency
  - Population pyramid evolved from classic in 1960 to bimodal by 1990s; current pyramid has a narrower base with baby boomers approaching/entering retirement.
  - Total dependency ratio: low of 42.8 in 1997; remained 43–45 percent until mid-2010s; began rising rapidly in 2016; surpassed 50 percent in 2021.
  - Old-age dependency ratio: 10.6 in 2000; record high of 28.9 in 2024.
  - Child dependency ratio declined from 33.3 in 2010 to 22.9 (historic low).
  - Dependency burden is higher for women.
- Regional comparisons and UN projections (UN DESA)
  - Under UN baseline (median fertility) scenario: population expected to peak and then decline during latter half of this decade; potential population decrease of 11 percent by 2100.
  - UN low fertility scenario: decline of 36 percent by 2100.
  - UN high fertility scenario: population growth through century’s end.
  - Baseline scenario dependency projections: total dependency ratio projected to reach 60 percent around 2030, 70 percent by 2038, 80 percent by 2053, and surpass 100 percent around 2077.
- Policy note
  - Low fertility rates are difficult or expensive to reverse (literature referenced in source).

### Labor market stylized facts
- Labor force participation (LFP)
  - Total LFP: 55.0 percent in 1960; 65.4 percent in 2000 (driven by rise in female LFP).
  - Since 2000, male and female LFP moved in parallel; gender gap ≈ 9 percent in 2023.
  - LFP slightly declined post-pandemic.
- Unemployment rate (UR)
  - UR declined steadily 1960–1991, then oscillated around 7.5 percent.
  - Recent historic lows: 4.3 percent in 2022; 4.0 percent in 2023; 4.3 percent in 2024.
  - Gender gap in UR narrowed through 2010 and oscillates around zero thereafter.
- Employment rate (ER) and inactivity
  - ER rose 1972–2000 (female ER increases); slightly declined 2000–2018.
  - 2019: ER increased for both genders to highest levels of past two decades.
  - ER declined significantly during the pandemic; started to recover since 2021; ER gender gap remains over 9 percent.
  - Inactivity: 39.3 percent in 2022; 37.2 percent in 2023; remains above pre-pandemic levels.
- Youth labor market (15–24 years)
  - Youth LFP: 35.0 percent in 2022; 40.2 percent in 2023.
  - NEET rate: 2019: 10.7 percent; 2022: 7.4 percent; 2023: 7.8 percent.
  - Youth UR: 9.9 percent in 2022; 11.1 percent in 2023; pandemic peak 25.9 percent in 2020.
  - Top youth employment sectors: accommodation and food, trade and repairs, administrative support, transport and storage, construction.
  - Top youth occupations: entry-level, clerical, services, sales.
  - Top four reasons for youth UR increase: recent entry to job market 25 percent; inability to work full-time 13 percent; inability to find a job 10 percent; lack of required diplomas 6 percent.
- Gender and age patterns in inactivity
  - 2015–2019 average inactivity: men 32.2 percent; women 40.8 percent.
  - Pandemic peaks in 2020 inactivity: men 40.9 percent; women 48.0 percent.
  - By 2022 inactivity decreased to men 36.5 percent; women 43.3 percent.
  - Highest inactivity: youth (education) and elderly (starting at age 60).
  - Female inactivity higher in ages 35–44 and 50–59; mean child-bearing age increased from 27.4 to 29.5 years over 2010–24; about a fifth of women give birth at 35 or older.

### 2023 Labor Force Survey (LFS) — structure, wages, occupations
- Occupational and sectoral patterns
  - Men dominate as managers; women dominate professional and hospitality roles.
  - "Trade and repairs" is second largest employer for both genders; construction is second for men; "health and social work" is second for women.
  - Service and sales occupations: almost one-third of female employment; over one-fifth of male employment.
  - Elementary occupations rank second overall; craft trades rank second among men.
  - Employment by gender shares (chart context): Managers — Females 55 percent, Males 50 percent; Hotels and restaurants and Professionals — Females 61 percent vs Males 39 percent.
- Formal sector salaries and gender wage gaps (Social Security-SvB, 2024)
  - Monthly minimum wage in 2024:
    - General employees: 1109.61 USD (about 55 percent of average wage).
    - Domestic workers: 517.68 USD (around 25 percent of average wage).
  - Average salary in the formal sector as of 2024q3:
    - Men: 2,623 USD.
    - Women: 2,171 USD.
    - Implied wage gap: about 17 percent.
  - Sectors with wage gaps over 30 percent: Education, IT, electricity and gas.
  - Sectors with best wage parity: Real estate, professional, administrative services.
  - Best paying sectors: Public administration, finance and insurance, utilities, health and social services.

### Population projections and baseline labor projection (medium fertility, policy unchanged)
- UN World Population Prospects scenarios — peak timing (as stated)
  - "No change": total population peak in 2028; working age peak in 2031. Total and working age population begin declining as early as 2029 and 2032, respectively.
  - "Zero net migration": peaks in 2030 (total) and 2034 (working age).
  - "Low" fertility: total (working age) population decline after peaking in 2028 (2034).
  - "Medium" fertility: total (working age) population decline after peaking in 2031 (2034).
  - "High fertility": continued growth in total population; working age population peaks in 2034 and begins to decline between 2035–38; trend reversal in 2039 as those born in 2024 enter labor force.
  - Comparison note: "no change" yields slightly higher totals through 2029; "zero migration" dominates from 2030 onward due to modestly positive net migration.
  - Constant fertility and mortality at 2024 levels would lead to fast population declines.
- Baseline labor market projection assumptions
  - Use "medium fertility" population scenario.
  - LFP rate for each gender unchanged from 2023 levels.
  - Apply UR consistent with macroeconomic framework.
- Baseline LFP rates (held at 2024 levels)
  - Males: 68.3 percent.
  - Females: 59.3 percent.
  - Gender gap: 9.0 percent.
  - Overall LFP: 63.5 percent.
- Baseline employment change over 2024–2030
  - Total employed increases by 1,304 persons (679 males; 625 females).
  - Percentage change: 2.3 percent overall (2.4 percent males; 2.2 percent females).
  - Employment growth would slowly decline, becoming flat by 2030.

### Alternative counterfactual scenarios (employment gains 2024–2030)
- Raise female LFP to 62 percent by 2030 (reduce gap with male LFP by 30 percent)
  - Required annual change: about 0.45 percentage point increase per annum in female LFP.
  - Female employment additional increase: 4.8 percent or 1,295 persons over 2025–2030 (on top of baseline gains).
  - Total employment gain: 2,599 persons or 4.7 percent over 2024–2030.
  - Male employment growth: 2.4 percent.
  - Female employment growth: 6.9 percent.
- Raise male LFP by 0.3 percent per annum to reach 70.1 percent by 2030
  - Male employment increase: 754 persons or 2.7 percentage points over 2024–2030 compared to baseline.
  - Total employed growth: 2,058 persons or 3.7 percent over 2024–2030.
  - Male employment growth: 5.1 percent.
  - Female employment growth: 2.3 percent.
- Combine raising female LFP to 62 percent and male LFP to 70.1 percent
  - Overall LFP: 65.8 percent.
  - Gender gap: 8.1 percent.
  - Total employment increase over 2024–2030: an additional 2,728 people, or 2.7 percentage points relative to baseline.
  - Total employment gain: 2,728 persons or 6.0 percent over 2024–2030.
  - Male employment growth: 5.1 percent.
  - Female employment growth: 6.9 percent.

### Impact of increased LFP on GDP growth (medium-term, relative to baseline)
- Under baseline, labor contributes about 0.4 percent to GDP growth.
- Raising male LFP would increase medium-term growth by an additional 0.14 percentage points of GDP relative to baseline.
- Boosting female LFP could add another 0.24 percent of GDP growth.
- Combined scenario: increase medium-term output by 0.38 percentage points by 2030.

### Policy recommendations to address demographic pressures and labor market frictions
- General strategic focus
  - Encourage labor supply and reduce labor market frictions to increase LFP and facilitate resource allocation.
  - Expand access to formal labor markets via integrated approaches and deepen active labor market policies.
- Specific measures and programs
  - Establish an online job database and a center for job seekers offering personalized job matching services, CV preparation advice, and interview preparation.
  - Job search assistance, vocational training and internship programs.
  - Provide vocational training or other short-cycle programs to address skill mismatches for youth and facilitate quick integration of inactive and unemployed into the labor force.
  - Invest in retraining and reskilling with emphasis on digital skills, entrepreneurship competencies, and financial education embedded in the national education system.
  - Promote youth and female entrepreneurship to support job creation.
  - Improve educational outcomes given relatively low tertiary education participation.
  - Offer flexible work options backed by unemployment insurance to enhance the business and investment climate.
  - Implement programs encouraging older workers to remain in or re-enter the workforce.
- Policies to increase female LFP
  - Improve availability and affordability of childcare and elderly care, within the limits of the fiscal rule.
  - Mitigate asymmetries in parental benefits.
  - Consider flexible parental leave to facilitate participation and formalization of work.
  - Encourage female participation in careers with acute skill shortages (e.g., STEM).
  - Use school curriculum and job centers to promote female entrepreneurship and financial literacy.
- Integration of migrants
  - Allowing young, skilled immigrants to fill vacancies could raise contributions to social security and healthcare and boost the labor force.
  - Ongoing formalization of undocumented migrants will strengthen the labor market.
  - Consider allowing migrants (or returning Arubans) the right to repurchase lost years between ages 15 and the pension age or require a minimum of paid-in taxes to be eligible for the program.

### Attribution
- Prepared by Olga Bespalova, Justin Matz, Toyosi Ojo, and Marco Arena; sources cited include Central Bureau of Statistics, Civil Registry Office, UN DESA, Central Bank of Aruba, Labor Force Survey (2023), and IMF staff calculations.

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### Sea level rise (SLR): significance, projections, impacts, and adaptation

### Key findings: exposure and significance
- SLR poses significant long-term challenges; sea level will continue to rise slowly but steadily for thousands of years, even with strong global mitigation.
- Aruba’s infrastructure, capital, and population concentrated along fragile coastline.
- Approximately 46 percent of all households live in coastal areas.
- Coastal population density: well over 1,000 residents per km2; coastal areas host at least 10,000 tourists per km2.
- The only airport, all major industrial facilities, the major hospital, and the only desalination plant are on the coastline, marginally above sea level.

### Climate baseline and observed change
- Climate baseline
  - Average annual mean temperature: 29.4 °C.
  - Average total annual precipitation: 426 mm.
  - Aruba: tropical semi-arid, hotter, drier, warming faster than most countries.
- Observed changes
  - Average mean annual temperature increased by 1.6 °C relative to pre-industrial levels.
  - No historical change in average total annual rainfall or seasonality in national averages.
  - High-resolution/high-frequency data do not reveal significant historical trends in drought or extreme precipitation for national averages.

### Model projections — temperature and precipitation (medians and ranges)
- Temperature projections
  - Additional increase of 0.6 to 0.9 °C in 2050 (relative to present).
  - Additional increase between 0.8 and 1.9 °C in 2085 (relative to present).
  - Accounting for 1.6 °C of present warming implies warming will exceed 2 °C and potentially approach 4 °C by end of century.
  - 90th percentile of the SSP3-7.0 ensemble implies increase of 2.4 °C at end of century relative to present (or 4 °C higher than pre-industrial).
- Precipitation projections
  - Projected decline by between 6 and 12 percent in 2050.
  - Projected decline by between 9 and 29 percent by the end of the century.
  - Projected percentage reduction in precipitation among the largest in the world; more severe outcomes cannot be excluded.
- Dry periods and extreme heat
  - Models indicate potential additional risks from longer dry periods and extreme heat episodes, especially under high emissions scenarios, with large uncertainty.

### Sea-level rise projections (local medians relative to 2000)
- Local SLR medians under scenarios (summary)
  - Moderate (global): 0.71 m by 2100 (median).
  - Paris (RCP 2.6): could limit SLR to 0.60 m by 2100 (median).
  - Extreme (RCP 8.5): could approach or exceed 0.90 m by 2100; accelerated ice-sheet melt could lead to rise of 1 m or more by end of century.
- Regional/temporal notes
  - With moderate emissions, sea level projected to increase by 0.71 m relative to 2000.
  - Paris-aligned cuts (RCP 2.6) have modest effect until 2050 but could limit SLR to 0.60 m by 2100.
  - Land ice melting inertia implies sea levels will continue rising for thousands of years even if global mean temperature stabilizes.

### CIAM model central estimates — economic costs and adaptation
- Central estimate (no adaptation)
  - Annual average cost of SLR without adaptation: approximately 0.8 percent of GDP from 2020 to 2099.
  - Full scenario range: annual average costs between 0.4 to 1.4 percent of GDP (lowest emission to highest emission).
  - Most cost attributed to permanent inundation and abrupt relocation costs.
  - Losses represent welfare losses and are not directly translated into GDP or fiscal impacts without further modeling.
  - These losses exclude potentially large reductions in tourism revenues from extensive coastal flooding and incremental pluvial flood costs.
- Adaptation via coastal protection (CIAM central estimates)
  - Coastal protection reduces average annual costs from 0.8 to 0.5 percent of GDP from 2020 to 2099.
  - Coastal protection requires annual investment of approximately 0.4 percent of GDP throughout the century.
  - Protection mix: hard barriers and soft solutions (beach nourishment).
  - Coastal protections may exacerbate pluvial flood damages by slowing drainage; potential increased investment needs beyond preliminary estimates.
  - Economic value of wetland areas lost due to protection is incorporated in model estimates.
- Planned retreat
  - Planned retreat can lower average annual cost of SLR to 0.2 percent of GDP from 2020 to 2099.
  - CIAM estimates cost of planned retreat averages 0.2 percent of GDP between 2020 and 2099 — less than half the cost of protection and 80 percent less than inaction.
  - Long-term cost of planned retreat equals opportunity cost of land to which population/assets move plus disutility from relocation and residual storm surge impacts.
- Government financing implications (if government assumes full responsibility)
  - Upper bound government financing needs: 0.2 percent of GDP annually for planned retreat to 0.8 percent annually for inaction.
  - If government fully finances coastal protection, public spending must increase by 0.4 percent of GDP annually on average between 2020 and 2099.
- CIAM country comparison
  - Median country median losses equal 1.13 percent of GDP without adaptation; equal 0.3 percent of GDP with least-cost adaptation.

### CIAM model structure, assumptions, and limitations
- Model and inputs
  - Model: Coastal Impact and Adaptation Model (CIAM) (Diaz, 2016).
  - Global coastline divided into >12,000 segments; Aruba’s coastline (~70 Km) modeled as a single segment.
  - Inputs per segment: capital, population, wetland coverage, local SLR projections (Kopp et al., 2014), baseline erosion, vertical land movement.
  - Increased river flood risk not considered.
- Storm surge treatment
  - Storms treated as periodic inundations on top of SLR; consider 1/10, 1/100, 1/1,000 and 1/10,000 year return periods.
  - Storm surge costs incremental relative to baseline without SLR.
- Valuation and monetization
  - Land values use rental value of agricultural land near coastline (Yohe, 1990).
  - Loss of life monetized using Value of Statistical Life.
  - Wetland loss monetized using willingness-to-pay for biodiversity.
  - Disutility of reactive migration monetized.
- Cost components by scenario
  - No-adaptation: residual value of abandoned capital + demolition + value of inundated land + reactive migration disutility.
  - Protection: cost of seawalls/barriers + expected storm costs; capital and land preserved but periodic storm losses remain.
  - Planned retreat: residual capital cost + value of inundated land + disutility of relocation; avoids protection costs by letting capital depreciate and rebuilding inland.
- Limitations
  - Aruba modeled as single segment; CIAM does not capture fine coastal heterogeneity needed for optimal local strategies.
  - Does not capture interaction with ongoing coastal erosion processes.
  - Nature-based solutions not considered due to need for granular data, despite potential cost-effectiveness.

### Policy implications and recommendations for adaptation
- Planning and coordination
  - Long-term planning and coordination are key for successful implementation of planned retreat and protection strategies (e.g., rebuilding beachfront assets farther inland; redeveloping areas behind beachfront properties; setting coastal buffers with light infrastructure).
- Fiscal implications
  - Inaction and planned retreat imply no direct government costs unless compensations occur.
  - If public finances must cover protection or compensate private losses, additional spending can lead to deficits and increasing debt if revenue and non-climate expenditure shares remain constant.
- Distributional considerations
  - Distributional impacts depend on exposure and policy choices; inaction or retreat shifts costs to private coastal property owners (accelerated capital depreciation, business disruption, land depreciation, disutility).
  - Private adaptation may be inefficient due to coordination failures and externalities; public coordination is justified where large externalities exist.
- Data and governance needs
  - Require more granular hyper-resolution maps of elevation, currents, tides, infrastructure, and population to refine estimates and optimal mixes of protection/retreat.
  - Netherlands’ governance tradition on CBA and flood risk management is a potential source of best practices.
- Role of cost-benefit analysis (CBA)
  - CBA provides a transparent framework to compare social costs/benefits, identify trade-offs, and prioritize adaptation investments; should be complemented by distributional analysis and applied consistently.

### Annex I — Aruba: Sea-Level Rise Projections relative to 2000 level (meters) — selected values (Table A1)
- Global Mean (selected medians and 5th–95th percentile ranges)
  - Paris (RCP2.6): 2030: 0.14 [0.10 , 0.18]; 2050: 0.25 [0.18 , 0.33]; 2070: 0.35 [0.23 , 0.51]; 2100: 0.50 [0.30 , 0.82].
  - Moderate (RCP4.5): 2030: 0.14 [0.10 , 0.18]; 2050: 0.26 [0.18 , 0.35]; 2070: 0.39 [0.26 , 0.56]; 2100: 0.60 [0.35 , 0.94].
  - Extreme (RCP8.5): 2030: 0.14 [0.11 , 0.18]; 2050: 0.29 [0.21 , 0.38]; 2070: 0.47 [0.33 , 0.66]; 2100: 0.77 [0.51 , 1.19].
- Aruba local projections relative to 2000 (selected medians and ranges)
  - Baseline: 2030: 0.02 [-0.03 , 0.07]; 2050: 0.04 [-0.04 , 0.12]; 2070: 0.05 [-0.06 , 0.17]; 2100: 0.08 [-0.09 , 0.24].
  - Paris (RCP2.6): 2030: 0.18 [0.10 , 0.26]; 2050: 0.31 [0.19 , 0.45]; 2070: 0.44 [0.24 , 0.67]; 2100: 0.60 [0.31 , 1.01].
  - Moderate (RCP4.5): 2030: 0.18 [0.10 , 0.25]; 2050: 0.32 [0.19 , 0.46]; 2070: 0.47 [0.27 , 0.71]; 2100: 0.71 [0.35 , 1.15].
  - Extreme (RCP8.5): 2030: 0.18 [0.11 , 0.26]; 2050: 0.35 [0.21 , 0.50]; 2070: 0.56 [0.34 , 0.83]; 2100: 0.90 [0.53 , 1.40].
- Source and notes for Table A1: sea-level rise projections from Kopp et al. (2014) derived from CIAM database (Diaz, 2016); ranges are 5th–95th percentiles; local projections include local vertical land movement.

### CIAM methodology summary
- CIAM divides coasts into segments, values land/wetlands/capital, models SLR plus storm surges, monetizes life/wetland loss/disutility, compares no-adaptation, protection, and planned retreat to identify least-cost strategies subject to data and model limitations.

_Prepared by Olga Bespalova, Justin Matz, Toyosi Ojo, and Marco Arena; Emanuele Massetti (FAD). International Monetary Fund._

### 1.    Impacts of Potential Demographics Changes  ______________________________________ 14

### ARUBA’S DEMOGRAPHICS AND ITS POTENTIAL IMPACT ON THE LABOR MARKET AND OUTPUT

### A. Stylized Facts on Demographics Trends In Aruba
- Historically, Aruba benefited from population growth that expanded the labor force and fostered economic growth (the “demographic dividend”); declining fertility rates and rapid aging are altering the balance between economically active and dependent populations.
- Fertility and births
  - The birth rate, measured as the number of births per 1,000 people, reached a historic low of 759 in 2024.
  - Civil Registry and Population Office: number of births per 1,000 women fell from 20.69 in 1972 to 18.78 in 1980, 18.17 in 1990, 14.26 in 2000, 11.94 in 2010, and is currently 7.59.
  - UN statistics: Total Fertility Rate (TFR) — 1950: 5.83; 1960: 4.57; 1970: 2.97; 1980: 2.20; 1990: 2.35; 2000: 1.85; 2010: 1.86; 2020: 1.66; currently: 1.60.
- Aging population
  - Cohort aged 65 and older increased from approximately 7 percent in 1990 to over 19 percent by end of 2024.
  - Median population age increased from 23.9 years in 1960 to 42 years in 2020.
  - Proportion of women increased from 51 percent to 53 percent over 1960-2024.
- Population growth dynamics
  - Aruba’s population nearly doubled since 1960, with most growth occurring before 2010.
  - 1989–1993: average growth rates over 5 percent; population increased nearly 40 percent from 1990 to 2000.
  - Growth slowed to 13 percent in the early 2000s and to 7.8 percent in the 2010s.
  - Since the pandemic onset, population growth has stagnated due to historic low birth rates and diminished inward migration; current primary drivers of population change are immigration and emigration flows.
- Migration and foreign-born population (end-December 2024)
  - 39.5 percent of Aruba’s population was foreign-born.
  - Foreign-born women represented approximately 56 percent of the migrant population.
  - Among migrants: working-age females (15-64) comprise 46 percent; working-age males 36 percent; children 5 percent; age 65+ 14 percent.
  - By end-2024, 22 percent of inward migrants either returned to Aruba or came from other Dutch Caribbean islands.
  - Remaining foreign-born primarily from Venezuela (19.7 percent), Colombia (19.6 percent), and the Netherlands (14.6 percent).
  - Average of around 3,000 Arubans emigrated each year in the past decade, with a pandemic peak in 2020.
  - De-registrations in 2024: emigrants born in Aruba or the former Netherlands Antilles accounted for 53.5 percent.
- Population structure and dependency
  - Population pyramids evolved from a classic pyramid in 1960 to a bimodal distribution by the 1990s; current pyramid has a narrower base with baby boomers approaching or entering retirement.
  - Total dependency ratio: hit a low of 42.8 in 1997, stayed at 43-45 percent until the mid-2010s, began rapidly rising in 2016, and surpassed 50 percent in 2021.
  - Old-age dependency ratio rose from 10.6 in 2000 to a record high of 28.9 in 2024.
  - Child dependency ratio declined from 33.3 in 2010 to 22.9 (historic low) reflecting lower TFR.
  - The dependency burden is higher for women.
- Regional comparisons and projections
  - Aruba appears on the frontier of overall population aging among selected regional peers; Aruba and Trinidad and Tobago’s dependency ratio is already rising.
  - UN baseline projections (UN DESA): population expected to peak and then decline during the latter half of this decade under all but a “high fertility scenario.”
    - Baseline (median fertility) scenario indicates a potential population decrease of 11 percent by 2100.
    - UN low fertility scenario predicts a decline of 36 percent by 2100.
    - Only the UN high fertility scenario results in population growth through the century’s end.
  - Baseline scenario dependency projections: total dependency ratio projected to reach 60 percent around 2030, 70 percent by 2038, 80 percent by 2053, and surpass 100 percent around 2077.
- Policy note from sources: low fertility rates are difficult or expensive to reverse (references to literature on policy responses to low fertility).

### B. Stylized Facts on Labor Markets in Aruba
- Labor force participation (LFP)
  - Total LFP rose from 55.0 percent in 1960 to 65.4 percent in 2000, driven by a sharp rise in female LFP.
  - Since 2000, male and female LFP have moved in parallel; the gender gap was around 9 percent in 2023.
  - LFP slightly declined post-pandemic.
- Unemployment rate (UR)
  - UR declined steadily from 1960 to 1991, then fluctuated around 7.5 percent.
  - Recent historic lows: 4.3 percent in 2022, 4.0 percent in 2023, and 4.3 percent in 2024.
  - Gender gap in UR narrowed significantly through 2010 and has oscillated around zero.
- Employment rate (ER) and inactivity
  - ER increased from 1972 to 2000 (driven by female ER increases), then slightly declined during 2000-2018.
  - 2019 saw ER increase for both genders to the highest levels of the past two decades.
  - ER declined significantly during the pandemic and started to recover since 2021; ER gender gap remains over 9 percent.
  - Inactivity declined from 39.3 percent in 2022 to 37.2 percent in 2023 but remains above pre-pandemic levels.
- Youth labor market
  - Youth LFP (15-24 years) increased to 40.2 percent in 2023 from 35.0 percent in 2022.
  - NEET rate (Not in Employment, Education, or Training) for 2023 is 7.8 percent (2019: 10.7 percent; 2022: 7.4 percent).
  - Youth UR increased from 9.9 percent in 2022 to 11.1 percent in 2023 (below the pandemic peak of 25.9 percent in 2020).
  - Top sectors for youth employment: accommodation and food, trade and repairs, administrative support, transport and storage, construction.
  - Top occupations for youth: entry-level, clerical, services, and sales positions.
  - Top four reasons for youth UR increase: recent entry to job market (25 percent), inability to work full-time (13 percent), inability to find a job (10 percent), lack of required diplomas (6 percent).
- Gender and age patterns in inactivity and employment status
  - Inactivity rates are consistently higher for females than males across all age groups.
  - 2015-2019 average inactivity: men 32.2 percent, women 40.8 percent.
  - Pandemic peaks in 2020 inactivity: men 40.9 percent, women 48.0 percent.
  - By 2022 inactivity decreased to 36.5 percent for men and 43.3 percent for women.
  - Highest inactivity by age cohorts observed among youth (education) and elderly (starting at age 60).
  - Female inactivity noticeably higher in ages 35-44 and 50-59, potentially due to child and elderly care roles or difficulties re-entering the labor force.
  - Mean child-bearing age increased from 27.4 to 29.5 years over 2010-24; about a fifth of women give birth at 35 or older.

*Prepared by Olga Bespalova, Justin Matz, Toyosi Ojo, and Marco Arena; sources cited include Central Bureau of Statistics, Civil Registry Office, UN DESA, Central Bank of Aruba, Labor Force Survey (2023), and IMF staff calculations.*

### 13.      The 2023 Labor Force Survey (LFS) reveals major economic sectors and occupations.

### 13.      The 2023 Labor Force Survey (LFS) reveals major economic sectors and occupations.

### Labor force structure and occupations (LFS 2023 and 2021 observations)
- Men dominate as managers, while women – in professional and hospitality roles.
- The “trade and repairs” sector is the second largest employer for both genders, followed by construction for men and “health and social work” for women.
- Service and sales occupations comprise almost a third of female and over a fifth of male employment.
- Elementary occupations rank second, followed by craft trades among men.
- Employment by gender (shares, in percent) highlights higher female shares in:
  - Managers: Females 55 percent, Males 50 percent (chart context).
  - Hotels and restaurants and Professionals: Females 61 percent vs Males 39 percent (chart context).
- Employment in hospitality by gender and age (shares, in percent) shown for age groups: All ages, 15-54, 55-64, 65-74 (chart context).

### Formal sector salaries and gender wage gaps (2024, Social Security-SvB data)
- Monthly minimum wage in 2024:
  - General employees: 1109.61 USD (about 55 percent of average wage).
  - Domestic workers: 517.68 USD (around 25 percent of average wage).
- Average salary in the formal sector as of 2024q3:
  - Men: 2,623 USD.
  - Women: 2,171 USD.
  - Implied wage gap: about 17 percent.
- Sectors with wage gaps over 30 percent: Education, IT, electricity and gas.
- Sectors with best wage parity: Real estate, professional, administrative services.
- Best paying sectors: Public administration, finance and insurance, utilities, health and social services.
- Median wages by sex shown across industries (chart context with female and male bars by sector).

### Population projections and scenarios (UN World Population Prospects scenarios)
- Scenarios differ by assumptions about fertility, mortality, and net immigration rate (Table 1).
- Peaks and timing by scenario (as stated):
  - “No change” scenario: total population peak in 2028; working age population peak in 2031. Total and working age population would begin declining as early as 2029 and 2032, respectively.
  - “Zero net migration” scenario: peaks in 2030 (total) and 2034 (working age).
  - “Low” fertility scenario: total (working age) population decline after peaking in 2028 (2034).
  - “Medium” fertility scenario: total (working age) population decline after peaking in 2031 (2034).
  - “High fertility” scenario: continued growth in total population, working age population peaking in 2034 and beginning to decline between 2035-38, with a trend reversal in 2039 as those born in 2024 enter the labor force.
- Comparison notes:
  - Between “no change” and “zero migration”: “no change” results in slightly higher totals through 2029; “zero migration” dominates from 2030 onward due to a modestly positive net migration rate.
  - Constant fertility and mortality rates at 2024 levels would lead to fast population declines.

### Baseline labor market projection (medium fertility scenario, policy unchanged)
- Assumptions:
  - Use “medium fertility” population scenario.
  - LFP rate for each gender unchanged from 2023 levels.
  - Apply UR consistent with the macroeconomic framework.
- Baseline LFP rates (held at 2024 levels in projection):
  - Males: 68.3 percent.
  - Females: 59.3 percent.
  - Gender gap: 9.0 percent.
  - Overall LFP: 63.5 percent.
- Over 2024-2030 baseline employment change:
  - Total number of employed increases by 1,304 persons (679 males and 625 females).
  - Percentage change: 2.3 percent overall (2.4 percent for males and 2.2 percent for females).
  - Employment growth would slowly decline, becoming flat by 2030.

### Alternative counterfactual scenarios and estimated employment gains (2024-2030 unless otherwise stated)
- Scenario: Bring female LFP to 62 percent by 2030 (reduce gap with male LFP by 30 percent).
  - Required annual change: about 0.45 percentage point increase per annum in female LFP.
  - Female employment additional increase: 4.8 percent or 1,295 persons over 2025-2030 (on top of baseline gains).
  - Total employment gain: 2,599 persons or 4.7 percent over 2024-2030.
  - Male employment growth under this scenario: 2.4 percent.
  - Female employment growth under this scenario: 6.9 percent.
- Scenario: Raise male LFP by 0.3 percent per annum to reach 70.1 percent by 2030.
  - Male employment increase: 754 persons or 2.7 percentage points over 2024-2030 compared to baseline.
  - Total number of employed growth: 2,058 persons or 3.7 percent over 2024-2030.
  - Male employment growth: 5.1 percent.
  - Female employment growth: 2.3 percent.
- Scenario: Combine raising female LFP to 62 percent and male LFP to 70.1 percent.
  - Overall LFP would reach 65.8 percent.
  - Gender gap would shrink to 8.1 percent.
  - Total employment increase over 2024-2030: an additional 2,728 people, or 2.7 percentage points relative to the baseline.
  - Total employment gain: 2,728 persons or 6.0 percent over 2024-2030.
  - Male employment growth: 5.1 percent.
  - Female employment growth: 6.9 percent.

### Impact of increased labor force participation on GDP growth (medium-term, relative to baseline)
- Under the baseline scenario, labor contributes about 0.4 percent to GDP growth.
- Raising male LFP (counterfactual) would increase medium-term growth by an additional 0.14 percentage points of GDP relative to the baseline.
- Boosting female LFP could add another 0.24 percent of GDP growth.
- Combined scenario (both genders): would increase medium-term output by 0.38 percentage points by 2030.

### Policy recommendations to address demographic pressures and labor market frictions
- General strategic focus:
  - Encourage labor supply and reduce labor market frictions to increase LFP and facilitate resource allocation.
  - Expand access to formal labor markets via integrated approaches and deepen active labor market policies.
- Specific measures and programs:
  - Establish an online job database and a center for job seekers offering personalized job matching services, CV preparation advice, and interview preparation.
  - Job search assistance (CV advice, interview preparation), vocational training and internship programs.
  - Provide vocational training or other short-cycle programs to address skill mismatches for youth and facilitate quick integration of inactive and unemployed into the labor force.
  - Invest in retraining and reskilling with emphasis on digital skills, entrepreneurship competencies, and financial education embedded in the national education system.
  - Promote youth and female entrepreneurship to support job creation.
  - Improve educational outcomes given relatively low tertiary education participation (CBA, 2024).
  - Offer flexible work options backed by unemployment insurance to enhance the business and investment climate.
  - Implement programs encouraging older workers to remain in or re-enter the workforce.
- Policies to increase female LFP:
  - Improve availability and affordability of childcare and elderly care, within the limits of the fiscal rule.
  - Mitigate asymmetries in parental benefits.
  - Consider flexible parental leave to facilitate participation and formalization of work.
  - Encourage female participation in careers with acute skill shortages (e.g., STEM) to raise returns to education for women.
  - Use school curriculum and job centers to promote female entrepreneurship and financial literacy.
- Integration of migrants:
  - Allowing young, skilled immigrants to fill vacancies could raise contributions to social security and healthcare programs and boost the labor force.
  - Ongoing formalization of undocumented migrants will strengthen the labor market.
  - To encourage formal participation, consider allowing migrants (or returning Arubans) the right to repurchase lost years between ages 15 and the pension age or require a minimum of paid-in taxes to be eligible for the program.

*Sources: National authorities; Labor Force Survey 2021; Social Security-SvB; United Nations World Population Prospects; IMF staff calculations.*

### 2.      Sea level rise (SLR) poses significant long-term challenges to Aruba’s economy and

### 1abwea2025002-source-pdf - 2.      Sea level rise (SLR) poses significant long-term challenges to Aruba’s economy and 

### Key findings: significance and exposure
- Sea level rise (SLR) poses significant long-term challenges to Aruba’s economy and population; sea level will continue to rise slowly but steadily for thousands of years, even with strong global mitigation efforts.
- Aruba’s infrastructure, capital, and population are concentrated along an environmentally fragile coastline, creating macro-critical risks from SLR.
- Approximately 46 percent of all households live in coastal areas.
- Coastal population density is well over 1,000 residents per km2, and coastal areas host at least 10,000 tourists per km2.
- The only airport, all major industrial facilities, the major hospital, and the only desalination plant are all located on the coastline, marginally above sea level.

### Climate trends and projections
- Climate baseline:
  - Average annual mean temperature: 29.4 °C.
  - Average total annual precipitation: 426 mm.
  - Aruba has a tropical semi-arid climate that is hotter, drier, and warming faster than most countries.
- Observed and historical changes:
  - The average mean annual temperature has increased by 1.6 °C with respect to pre-industrial levels.
  - Temperatures have increased uniformly across seasons.
  - Historical data show no changes in either the average total annual rainfall or its seasonality.
  - High-resolution and high-frequency data do not reveal significant historical trends in drought and in extreme precipitation events for Aruba’s national averages, though regional shifts north of the 13th parallel show different trends.
- Model projections (medians and ranges as reported):
  - Temperature:
    - Additional increase of 0.6 to 0.9 °C in 2050 (relative to present levels).
    - Additional increase between 0.8 and 1.9 °C in 2085 (relative to present levels).
    - Accounting for 1.6 °C of present warming implies warming will exceed 2 °C and potentially approach 4 °C by the end of the century.
    - The 90th percentile of the SSP3-7.0 ensemble implies an increase of temperature equal to 2.4 °C at the end of the century relative to present levels (or 4 °C higher than pre-industrial).
  - Precipitation:
    - Projected decline by between 6 and 12 percent in 2050.
    - Projected decline by between 9 and 29 percent by the end of the century.
    - The projected percentage reduction in precipitation is among the largest in the world; more severe outcomes cannot be excluded.
  - Dry periods and extreme heat:
    - Models indicate potential additional risks from longer dry periods and episodes with extreme heat, especially under high emissions scenarios, with large uncertainty.

### Sea-level rise projections
- Local SLR medians (relative to 2000 level) under scenarios (Figure 5):
  - Moderate (global): 0.71 m by 2100 (median).
  - Paris (RCP 2.6): could limit SLR to 0.60 m by 2100 (median).
  - Extreme (high emissions, RCP 8.5): could approach or exceed 0.90 m by 2100; faster SLR due to accelerated Greenland and Antarctic Ice Sheets melting could lead to a rise of 1 m or more by the end of the century.
- Regional/temporal notes:
  - With a moderate emission scenario, sea level is projected to increase by 0.71 m relative to its level in 2000.
  - Strong global emission cuts aligned with the Paris Agreement (RCP 2.6) have a modest effect until 2050 but could limit SLR to 0.60 m by 2100.
  - Due to inertia in land ice melting, sea levels will continue to rise for thousands of years even if global mean temperature stabilizes during this century.
- Comparisons to more recent studies (Box 1):
  - Fox-Kemper et al. (2021) project 0.65 m of SLR in 2100 in Aruba for SSP2-4.5 (comparable to RCP4.5), versus 0.60 m in Kopp et al. (2014).
  - SSP5-8.5 projects 0.86 m in 2100; Kopp et al. (2014) project 0.90 m.
  - The 95th percentiles used for this analysis are five centimeters higher than the most recent projections; new projections consider an extreme low-confidence scenario with potentially higher SLR.

### Impacts, risks, and channels to the macroeconomy
- SLR magnifies flood and storm surge risks and impacts key economic and strategic assets concentrated on the coast, increasing potential for acute macroeconomic shocks.
- Economic channels and risks include:
  - Lower fiscal revenues from production losses.
  - Higher expenditure to replace damaged public assets.
  - Lower capital inflows from reduced tourism.
  - Balance of payments pressures and long-term debt sustainability risks.
- Comparative damage context:
  - Centrale Bank van Aruba (2020) estimates that net foreign assets decline by between 7 and 10 percent for at least four months after major flooding events; persistent SLR risks can cause a persistent decline of foreign assets.
  - Centrale Bank van Aruba (2020, p. 39) estimates damages from major floods amount to between 1.5 and 2.1 percent of GDP per event.
  - The central estimate of SLR costs (see below) implies damages equal to a major flood event every other year.

### Estimated economic costs and adaptation options (CIAM model exercise)
- Central estimate (no adaptation):
  - The annual average cost of SLR without adaptation is estimated at approximately 0.8 percent of GDP from 2020 to 2099.
  - Using the full range of scenarios, annual average costs range between 0.4 to 1.4 percent of GDP (lowest projection for the lowest emission scenario to largest projection for the highest emission scenario).
  - Most cost is attributed to permanent inundation and abrupt relocation costs.
  - These costs represent welfare losses and cannot be translated into GDP losses or fiscal impacts without additional models or assumptions.
  - These losses do not include potentially large reductions in tourism revenues from extensive coastal flooding, nor do they account for incremental pluvial flood costs potentially caused by SLR.
- Adaptation via coastal protection (CIAM model central estimates):
  - Coastal protection is estimated to reduce average annual costs of SLR from 0.8 to 0.5 percent of GDP from 2020 to 2099.
  - Coastal protection required to prevent inundation and minimize storm surge costs requires an annual investment of approximately 0.4 percent of GDP, annually, throughout the century.
  - Coastal protection in the model consists of a mix of hard barriers (e.g., seawall, revetment, groyne, or breakwater) and soft solutions like beach nourishment.
  - Coastal protections may exacerbate damages from pluvial floods by slowing drainage of flooded areas, potentially increasing investment needs beyond those estimated in this preliminary analysis.
  - The economic value of wetland areas lost as a result of coastal protection is incorporated in model estimates.

### Role of adaptation and planning
- Aruba cannot control global SLR but can manage local impacts through adaptation.
- Strategic long-term planning and targeted investments can significantly reduce present vulnerabilities, mitigate future damages, and minimize macroeconomic risks.
- Aruba has prior adaptation experience (e.g., desalination investments) and coastal modifications for tourism and infrastructure, but adaptations to historical conditions must be gradually updated to address new environmental conditions.
- The CIAM model exercise is presented as a preliminary stylized blueprint for more detailed hyper-resolution assessments to refine risk and cost estimates.

*Prepared by Emanuele Massetti (FAD). International Monetary Fund.*

### 0.1 percent of GDP annually. The combined investment needs for coastal protection and the

### Estimating the Cost of Sea-Level Rise and Adaptation

### Key findings on costs and scenarios
- Combined investment needs for coastal protection and the negative externality associated with wetland loss amount to 0.5 percent of GDP annually in the discussed adaptation scenario.
- Adaptation reduces costs by approximately 40 percent compared to inaction.
- Planned retreat can lower the average annual cost of SLR to 0.2 percent of GDP from 2020 to 2099.
  - CIAM estimates the cost of planned retreat averages 0.2 percent of GDP between 2020 and 2099, which is less than half the cost of protection and 80 percent less than the cost of inaction.
  - The long-term cost of planned retreat equals the opportunity cost of land to which population and assets move, plus any disutility from relocation and residual storm surge impacts.
- If the government assumes full responsibility for all losses:
  - Upper bound government financing needs range from 0.2 percent of GDP annually for planned retreat to 0.8 percent annually for inaction.
  - If the government is fully responsible for coastal protection costs, public spending must increase by 0.4 percent of GDP annually, on average, between 2020 and 2099.
- In CIAM country comparisons: the median country suffers losses equal to 1.13 percent of GDP without adaptation, and equal to 0.3 percent of GDP with the least-cost adaptation strategy.

### CIAM model structure and assumptions (methodology)
- Model: Coastal Impact and Adaptation Model (CIAM) (Diaz, 2016).
- Global coastline division:
  - More than 12,000 segments grouped by country.
  - Aruba’s coastline (approximately 70 Km) is considered as a single coastal segment in CIAM.
- Data inputs per coastal segment:
  - Capital, population, and wetland coverage at different elevations.
  - Projections of local sea-level rise from Kopp et al. (2014).
  - Baseline erosion and vertical land movement are considered; increased river flood risk is not considered.
- Storm surge treatment:
  - Storms cause periodic inundations on top of sea-level rise.
  - The model considers storm events of 1/10, 1/100, 1/1,000 and 1/10,000 year return periods in protection/retreat variants.
  - Storm surge costs are incremental with respect to a baseline in which storms occur without SLR.
- Valuation and monetization:
  - Land values for inundation cost use the rental value of agricultural land in proximity to the coastline, following Yohe (1990).
  - Loss of life is monetized using the Value of Statistical Life.
  - Loss of wetland due to SLR or protection is monetized using willingness-to-pay estimates for biodiversity preservation.
  - Disutility cost of reactive migration is monetized.
- Cost components by scenario:
  - No-adaptation: sum of residual value of abandoned capital, demolition costs, and value of inundated land; reactive migration disutility monetized.
  - Protection: cost of seawalls and barriers plus expected value of storm costs; capital and land preserved but storms may still cause periodic losses.
  - Planned retreat: residual cost of capital, value of inundated land, and disutility cost of relocation; avoids protection costs by letting capital depreciate and rebuilding inland over time.
- Parameter sources: costs of building and maintaining seawalls and other key parameters are drawn from the literature.

### Model outputs and interpretation
- For each coastal segment CIAM calculates net present value of SLR costs across adaptation strategies and identifies least-cost strategies.
- Typical pattern:
  - Coastal protection is usually least cost where existing capital and population density are large.
  - Planned retreat is usually least cost where capital and population density are low.
- CIAM limitations noted:
  - Aruba modeled as a single coastal segment; CIAM does not capture fine coastal characteristics that determine inundation or optimal local strategy.
  - Does not capture interaction of SLR with ongoing coastal erosion processes in Aruba.
  - Nature-based solutions (e.g., mangroves, coastal buffer zones) are not considered by CIAM due to need for more granular data, despite potential cost-effectiveness and positive externalities.

### Policy implications and recommendations
- Long-term planning and coordination are key for successful implementation of planned retreat and protection strategies (e.g., rebuilding beachfront assets slightly farther inland; redeveloping areas behind beachfront properties; setting coastal buffers with light infrastructure).
- Fiscal implications:
  - Inaction and planned retreat imply no direct government costs unless government compensates losses.
  - If public finances must cover protection or compensate private losses, additional spending can lead to deficits and increasing debt if revenue and non-climate expenditure shares of GDP remain constant.
- Distributional considerations:
  - Distributional impacts depend on exposure and policy choices; inaction or retreat shifts substantial costs to private coastal property owners (accelerated capital depreciation, business disruption, land depreciation, disutility).
  - Private adaptation may be inefficient due to coordination failures and negative externalities; public coordination is justified where large externalities exist.
- Data and governance needs:
  - More granular hyper-resolution maps of elevation, currents, tides, infrastructure, and population are necessary for precise estimates and to determine optimal mixes of protection and retreat.
  - The strong governance tradition in the Netherlands on CBA and flood risk management is a potential source of best practices for Aruba.
- Role of cost-benefit analysis (CBA):
  - CBA provides a transparent, systematic framework to compare social costs and benefits, identify trade-offs, and prioritize adaptation investments.
  - CBA should be complemented by analysis and correction of distributional impacts and applied consistently across adaptation and other development programs.

### Additional empirical and literature points
- PESETA IV (large EU-funded project) finds coastal protection can reduce SLR damages in the EU by approximately 90 percent (Vousdoukas et al. 2020, Table 6).
- Model caveats emphasize uncertainty around exact extent and cost of damages and protection costs, but consensus that long-term planning of adaptation is highly effective and requires strong governance.

_International Monetary Fund — Box 2 and accompanying analysis on estimating the cost of sea-level rise and adaptation, and lessons for efficient adaptation spending_

### 20210809. PO.DAAC, CA, USA. Dataset accessed 2024-11-01 at Sea Level Projection Tool – NASA

### 1abwea2025002-source-pdf - 20210809. PO.DAAC, CA, USA. Dataset accessed 2024-11-01 at Sea Level Projection Tool – NASA

### References and Datasets
- 20210809. PO.DAAC, CA, USA. Dataset accessed 2024-11-01 at Sea Level Projection Tool – NASA Sea Level Change Portal.
- Gonguet, Fabien, Wendling, Claude, Aydın, Ö., & Battersby, B. (2021). Climate-Sensitive Management of Public Finances—. Green PFM””. IMF Staff Climate Notes, (2021/002).
- Hinkel, Jochen, Jeroen CJH Aerts, Sally Brown, Jose A. Jiménez, Daniel Lincke, Robert J. Nicholls, Paolo Scussolini, Agustín Sanchez-Arcilla, Athanasios Vafeidis, and Kwasi Appeaning Addo. “The ability of societies to adapt to twenty-first-century sea-level rise.” Nature Climate Change 8, no. 7 (2018): 570-578.
- IPCC, Climate Change (2021). The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. 2021.
- Kopp RE, Horton R, Little C, Mitrovica JX, Oppenheimer M, Rasmussen DJ, Strauss BH, Tebaldi, C (2014) Probabilistic 21st and 22nd century sea-level projections at a global network of tide gauge sites. Earth's Future pp 383{406}.
- Peterson, Ryan R. 2024. “The Heat is On.” Climate Aruba Digest Semiannual Report, June 2024.
- Sakrak, Ozlem Aydin, Bryn Battersby, Fabien Gonguet, Claude Wendling, Jacques Charaoui, Murray Petrie, and Suphachol Suphachalasai (2022). How to Make the Management of Public Finances Climate-Sensitive—“Green PFM” Fiscal Affairs Department, How to Note, Washington, DC : International Monetary Fund, 2022.
- Tinbergen, J., 1954, De economische balans van het Deltaplan, bijlage bij het rapport van de Deltacommissie.
- Yohe, Gary. "The cost of not holding back the sea: Toward a national sample of economic vulnerability." Coastal Management 18, no. 4 (1990): 403-431.

### Annex I — Maps of Simulated Changes (Droughts and Precipitation)
- Annex I. Figure A1. Maps of Simulated Changes for Indicators of Droughts and Intense Precipitations
  - Indicators shown: Consecutive Dry Days; Intense Rainfall (RX1Day).
  - Source: FADCP Climate Dataset (Massetti and Tagklis, 2024), using CMIP6 data (Copernicus Climate Change Service, Climate Data Store, 2021: CMIP6 climate projections).
  - Notes:
    - Crosses indicate areas in which models disagreen on both the magnitude and sign of precipitation changes.
    - Dots indicate areas in which most models agree on the direction of change, but it cannot be excluded with high confidence that no change or change of a different sign is possible.

- Annex I. Figure A2. Maps of Simulated Changes for Total Annual Precipitation
  - Source: FADCP Climate Dataset (Massetti and Tagklis, 2024), using CMIP6 data (Copernicus Climate Change Service, Climate Data Store, 2021: CMIP6 climate projections).
  - Notes:
    - Crosses indicate areas in which models disagreen on both the magnitude and sign of precipitation changes.
    - Dots indicate areas in which most models agree on the direction of change, but it cannot be excluded with high confidence that no change or change of a different sign is possible.

### Annex I. Table A1 — Aruba: Sea-Level Rise Projections Relative to 2000 level (meters)
- Table structure: Projections for years 2030, 2050, 2070, 2100 under three emission scenarios (Paris - RCP2.6; Moderate - RCP4.5; Extreme - RCP8.5). Ranges in brackets represent the 5th and 95th percentiles.

- Global Mean
  - Paris (RCP2.6)
    - 2030: 0.14 [0.10 , 0.18]
    - 2050: 0.25 [0.18 , 0.33]
    - 2070: 0.35 [0.23 , 0.51]
    - 2100: 0.50 [0.30 , 0.82]
  - Moderate (RCP4.5)
    - 2030: 0.14 [0.10 , 0.18]
    - 2050: 0.26 [0.18 , 0.35]
    - 2070: 0.39 [0.26 , 0.56]
    - 2100: 0.60 [0.35 , 0.94]
  - Extreme (RCP8.5)
    - 2030: 0.14 [0.11 , 0.18]
    - 2050: 0.29 [0.21 , 0.38]
    - 2070: 0.47 [0.33 , 0.66]
    - 2100: 0.77 [0.51 , 1.19]

- Aruba (local projections relative to 2000)
  - Baseline
    - 2030: 0.02 [-0.03 , 0.07]
    - 2050: 0.04 [-0.04 , 0.12]
    - 2070: 0.05 [-0.06 , 0.17]
    - 2100: 0.08 [-0.09 , 0.24]
  - Paris (RCP2.6)
    - 2030: 0.18 [0.10 , 0.26]
    - 2050: 0.31 [0.19 , 0.45]
    - 2070: 0.44 [0.24 , 0.67]
    - 2100: 0.60 [0.31 , 1.01]
  - Moderate (RCP4.5)
    - 2030: 0.18 [0.10 , 0.25]
    - 2050: 0.32 [0.19 , 0.46]
    - 2070: 0.47 [0.27 , 0.71]
    - 2100: 0.71 [0.35 , 1.15]
  - Extreme (RCP8.5)
    - 2030: 0.18 [0.11 , 0.26]
    - 2050: 0.35 [0.21 , 0.50]
    - 2070: 0.56 [0.34 , 0.83]
    - 2100: 0.90 [0.53 , 1.40]

- Source and notes for Table A1:
  - Source: Sea-level rise projections from Kopp et al. (2014) derived from the CIAM model database (Diaz, 2016).
  - Notes: Global and Local Sea-Level Rise (SLR) probabilistic projections until 2100 under three emission scenarios (Paris - RCP 2.6; Moderate - RCP 4.5; Extreme - RCP 8.5). The range in brackets represents the 5th and 95th percentiles of the distribution of SLR for each emission scenario. Local SLR projections include information on local climate change induced SLR rates and a baseline projections of local vertical land movement (subsidence or uplifting) not caused by climate change.

*Source: Sea-level rise projections from Kopp et al. (2014) derived from the CIAM model database (Diaz, 2016).*

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_Source: https://www.imf.org/-/media/files/publications/cr/2025/english/1abwea2025002-source-pdf.pdf_