## Potential Output and High-Income Convergence in Fiji (Sections 1–2)

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### A. Introduction
- Fiji’s National Development Plan and Vision 2050 set objectives to raise living standards, strengthen economic resilience, and achieve high-income status by 2050 through productivity-enhancing reforms, higher investment, and greater resilience to climate-related risks.
- Understanding supply-side growth capacity and the pace of income convergence is essential to gauge feasibility and required policy effort.

### B. Estimating Potential Output: Method and Key Result
- Methodology:
  - Production-function (augmented Cobb–Douglas) growth-accounting approach combining physical capital, labor input (total hours worked), human capital, and total factor productivity (TFP).
  - Historical data primarily from Penn World Table (PWT) version 11.0 through 2023; projections from 2024 onward.
- Key quantitative findings:
  - Baseline potential output growth: 3.4 percent.
  - Scenario range for potential growth: 2.4 –   3.6 percent.
  - Capital is the dominant driver across scenarios; TFP assumptions are the main source of uncertainty.

### C. Framework and Growth Decomposition (formulation)
- Production function:
  - Y_t^* = A_t K_t^α (N_t H_t)^{1−α}
  - Log-differenced growth decomposition: y_t = a_t + α·k_t + (1−α)·(n_t + h_t)
- Data and projection approach:
  - Uses PWT measures of output, capital services, capital stock, human capital, and TFP.
  - Projections aim to capture plausible medium-term trends rather than cyclical movements.

### D. Factor Input Projections and Assumptions
- Capital
  - Capital input measured as capital services; growth of capital services approximated from historical relationship with capital stock growth via OLS over 2000–2023.
  - Capital stock law of motion: rnn_a_t = rnn_a_{t−1}·(1−δ) + I_t with δ = 0.0884 (PWT average 2000–23).
  - Investment projection: I_t = I_{t−1}·(1 + g_r^{GFCF}), baseline real GFCF growth = 3.9 percent (average 2010–23).
  - Recursive mapping ensures consistency between investment path, capital stock, and capital services.
- Labor and Human Capital
  - Labor input N_t = employment × average hours; employment assumed to evolve with working-age population while employment rate held constant at its 2023 level.
  - Working-age population projections from United Nations demographic forecasts.
  - Human capital measured using PWT human capital index (piecewise-linear returns to schooling): returns set at 13.4 percent for first 4 years, 10.1 percent for years 5–8, and 6.8 percent beyond 8 years.
  - From 2024 onward baseline assumes annual increase in years of formal schooling = 0.05 years (average gain 2010–2023).
- TFP
  - Baseline TFP growth set equal to average over 2010–2019 = 0.99 percent.

### E. Results and Scenario Analysis: Contributions to Potential Growth
- Baseline composition (medium term):
  - Potential output growth = 3.4 percent.
  - Contributions (medium term, baseline): Capital accumulation largest, followed by TFP, labor input, and human capital.
- Scenario range and drivers:
  - Potential growth plausible range = 2.4 –   3.6 percent.
  - Main scenarios vary assumptions on investment, TFP, and labor input:
    - Investment: baseline real investment growth = 3.9 percent (2010–2023 average). Slower-investment scenario uses longer-run average over 2000–2023; faster-investment scenario uses higher GFCF growth.
    - Employment: alternative scenario increases growth in the employment rate with respect to working-age population to 0.3 from baseline 0.2.
    - TFP: low-TFP scenario reflects continuation of subdued productivity over 2010–2023; TFP is a major determinant of downside risk.
- Quantitative scenario table (selected values as presented in source):
  - Baseline: GDP 3.4; Capital Service 1.5; Labor 0.6; Human Capital 0.3; TFP 1.0
  - Slower Investment Growth (2.6): GDP 3.2; Capital Service 1.2; Labor 0.6; Human Capital 0.3; TFP 1.0
  - Faster Investment Growth (5.1): GDP 3.6; Capital Service 1.7; Labor 0.6; Human Capital 0.3; TFP 1.0
  - Slower TFP Growth (0.03): GDP 2.4; Capital Service 1.5; Labor 0.6; Human Capital 0.3; TFP 0.0
  - Growth in employment rate (0.3): GDP 3.5; Capital Service 1.5; Labor 0.8; Human Capital 0.3; TFP 1.0
- Interpretation:
  - Capital accumulation consistently accounts for the largest share of potential growth across scenarios.
  - Labor input and human capital contributions are relatively stable and limited in their ability to materially raise potential growth given demographic constraints and migration.
  - Variation in TFP assumptions explains most cross-scenario differences in potential growth.

### F. High-Income Convergence by 2050: Assessment
- Main finding:
  - Under baseline assumptions and absent major shocks, Fiji would make substantial progress toward high-income status by 2050 but would fall marginally short.
- Mechanism and risks:
  - Continued income convergence is feasible; however climate-related shocks and capital losses weigh on long-run growth and can prevent full closure of the income gap.
  - The convergence challenge is characterized as protecting and sustaining growth over time (through resilient and productive investment and continued productivity gains) rather than fundamentally insufficient growth capacity.

### Impact of natural disasters on long-run income and capital (Section 2)
- Even small and persistent output losses associated with natural disasters compound over decades, lowering the level of income relative to the high-income threshold.
- Absent resilience-enhancing action, disaster-related output losses can weigh on long-run growth outcomes through cumulative erosion of the capital stock.
- The projection excludes pandemic-scale global disruptions and assumes future shocks are broadly consistent with historical averages.

### Growth requirements to close the income gap (Section 2)
- Closing the remaining income gap would require policies that raise growth above the baseline path, including through higher and more effective investment.
- A growth rate of 3.6 percent is needed to close the gap, with a particular focus on strengthening resilience to climate-related shocks.

### Role and composition of investment (Section 2)
- Increasing capital expenditure can support growth by accelerating capital accumulation and strengthening productive capacity.
- The composition of this additional investment matters: in a country highly exposed to natural disasters, directing a greater share of investment toward climate-resilient infrastructure can enhance growth resilience by reducing repeated capital losses associated with climate-related shocks.
- The growth benefit from resilience-enhancing investment primarily reflects reduced capital losses from climate-related shocks and the preservation of productive capital, rather than higher total factor productivity growth.

### Scenario analysis and projection (Section 2)
- Under a scenario in which Fiji increases climate-related and resilience-enhancing investment, average growth is estimated to be around 0.2 percentage point higher than in the baseline, which would be sufficient to close the remaining income gap by 2050.

### Key statistics and timeline (Section 2)
- Required growth rate to close gap: 3.6 percent
- Estimated incremental growth from resilience investment: around 0.2 percentage point higher than baseline
- Target horizon for closing the remaining income gap under the scenario: by 2050

*Prepared by Matteo Ghilardi and Sonam Tobgay; IMF Selected Issues Paper SIP/2026/053 (Section 1). Source: sipea2026053 - Section 2, IMF staff estimates and World Bank's World Development Indicators.*

### Section 1

### Potential Output and High-Income Convergence in Fiji (Section 1)

### A. Introduction
- Fiji’s National Development Plan and Vision 2050 set objectives to raise living standards, strengthen economic resilience, and achieve high-income status by 2050 through productivity-enhancing reforms, higher investment, and greater resilience to climate-related risks.
- Understanding supply-side growth capacity and the pace of income convergence is essential to gauge feasibility and required policy effort.

### B. Estimating Potential Output: Method and Key Result
- Methodology:
  - Production-function (augmented Cobb–Douglas) growth-accounting approach combining physical capital, labor input (total hours worked), human capital, and total factor productivity (TFP).
  - Historical data primarily from Penn World Table (PWT) version 11.0 through 2023; projections from 2024 onward.
- Key quantitative findings:
  - Baseline potential output growth: 3.4 percent.
  - Scenario range for potential growth: 2.4 –   3.6 percent.
  - Capital is the dominant driver across scenarios; TFP assumptions are the main source of uncertainty.

### C. Framework and Growth Decomposition (formulation)
- Production function (as in source):
  - Y_t^* = A_t K_t^α (N_t H_t)^{1−α}
  - Log-differenced growth decomposition: y_t = a_t + α·k_t + (1−α)·(n_t + h_t)
- Data and projection approach:
  - Uses PWT measures of output, capital services, capital stock, human capital, and TFP.
  - Projections aim to capture plausible medium-term trends rather than cyclical movements.

### D. Factor Input Projections and Assumptions
- Capital
  - Capital input measured as capital services; growth of capital services approximated from historical relationship with capital stock growth via OLS over 2000–2023.
  - Capital stock law of motion: rnn_a_t = rnn_a_{t−1}·(1−δ) + I_t with δ = 0.0884 (PWT average 2000–23).
  - Investment projection: I_t = I_{t−1}·(1 + g_r^{GFCF}), baseline real GFCF growth = 3.9 percent (average 2010–23).
  - Recursive mapping ensures consistency between investment path, capital stock, and capital services.
- Labor and Human Capital
  - Labor input N_t = employment × average hours; employment assumed to evolve with working-age population while employment rate held constant at its 2023 level.
  - Working-age population projections from United Nations demographic forecasts.
  - Human capital measured using PWT human capital index (piecewise-linear returns to schooling): returns set at 13.4 percent for first 4 years, 10.1 percent for years 5–8, and 6.8 percent beyond 8 years.
  - From 2024 onward baseline assumes annual increase in years of formal schooling = 0.05 years (average gain 2010–2023).
- TFP
  - Baseline TFP growth set equal to average over 2010–2019 = 0.99 percent.

### E. Results and Scenario Analysis: Contributions to Potential Growth
- Baseline composition (medium term):
  - Potential output growth = 3.4 percent.
  - Contributions (medium term, baseline): Capital accumulation largest, followed by TFP, labor input, and human capital.
- Scenario range and drivers:
  - Potential growth plausible range = 2.4 –   3.6 percent.
  - Main scenarios vary assumptions on investment, TFP, and labor input:
    - Investment: baseline real investment growth = 3.9 percent (2010–2023 average). Slower-investment scenario uses longer-run average over 2000–2023; faster-investment scenario uses higher GFCF growth.
    - Employment: alternative scenario increases growth in the employment rate with respect to working-age population to 0.3 from baseline 0.2.
    - TFP: low-TFP scenario reflects continuation of subdued productivity over 2010–2023; TFP is a major determinant of downside risk.
  - Quantitative scenario table (excerpted values shown in source):
    - Baseline: GDP 3.4; Capital Service 1.5; Labor 0.6; Human Capital 0.3; TFP 1.0
    - Slower Investment Growth (2.6): GDP 3.2; Capital Service 1.2; Labor 0.6; Human Capital 0.3; TFP 1.0
    - Faster Investment Growth (5.1): GDP 3.6; Capital Service 1.7; Labor 0.6; Human Capital 0.3; TFP 1.0
    - Slower TFP Growth (0.03): GDP 2.4; Capital Service 1.5; Labor 0.6; Human Capital 0.3; TFP 0.0
    - Growth in employment rate (0.3): GDP 3.5; Capital Service 1.5; Labor 0.8; Human Capital 0.3; TFP 1.0
- Interpretation:
  - Capital accumulation consistently accounts for the largest share of potential growth across scenarios.
  - Labor input and human capital contributions are relatively stable and limited in their ability to materially raise potential growth given demographic constraints and migration.
  - Variation in TFP assumptions explains most cross-scenario differences in potential growth.

### F. High-Income Convergence by 2050: Assessment
- Main finding:
  - Under baseline assumptions and absent major shocks, Fiji would make substantial progress toward high-income status by 2050 but would fall marginally short.
- Mechanism and risks:
  - Continued income convergence is feasible; however climate-related shocks and capital losses weigh on long-run growth and can prevent full closure of the income gap.
  - The convergence challenge is characterized as protecting and sustaining growth over time (through resilient and productive investment and continued productivity gains) rather than fundamentally insufficient growth capacity.

*Prepared by Matteo Ghilardi and Sonam Tobgay; IMF Selected Issues Paper SIP/2026/053 (Section 1).*

### Section 2

### sipea2026053 - Section 2

### Impact of natural disasters on long-run income and capital
- Even small and persistent output losses associated with natural disasters compound over decades, lowering the level of income relative to the high-income threshold.
- Absent resilience-enhancing action, disaster-related output losses can weigh on long-run growth outcomes through cumulative erosion of the capital stock.
- The projection excludes pandemic-scale global disruptions and assumes future shocks are broadly consistent with historical averages.

### Growth requirements to close the income gap
- Closing the remaining income gap would require policies that raise growth above the baseline path, including through higher and more effective investment.
- A growth rate of 3.6 percent is needed to close the gap, with a particular focus on strengthening resilience to climate-related shocks.

### Role and composition of investment
- Increasing capital expenditure can support growth by accelerating capital accumulation and strengthening productive capacity.
- The composition of this additional investment matters: in a country highly exposed to natural disasters, directing a greater share of investment toward climate-resilient infrastructure can enhance growth resilience by reducing repeated capital losses associated with climate-related shocks.
- The growth benefit from resilience-enhancing investment primarily reflects reduced capital losses from climate-related shocks and the preservation of productive capital, rather than higher total factor productivity growth.

### Scenario analysis and projection
- Under a scenario in which Fiji increases climate-related and resilience-enhancing investment, average growth is estimated to be around 0.2 percentage point higher than in the baseline, which would be sufficient to close the remaining income gap by 2050.

### Key statistics and timeline
- Required growth rate to close gap: 3.6 percent
- Estimated incremental growth from resilience investment: around 0.2 percentage point higher than baseline
- Target horizon for closing the remaining income gap under the scenario: by 2050

*Source: sipea2026053 - Section 2, IMF staff estimates and World Bank's World Development Indicators.*

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_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2026/english/sipea2026053.pdf_