世界发展银行-Economic-and-Distributional-Impacts-of-Free-Trade-Agreements-_-The-Case-of-Indonesia_40页_713kb

Summary of "Economic and Distributional Impacts of Free Trade Agreements: The Case of Indonesia"

Core Content

This paper evaluates the economic and distributional impacts of various preferential trade agreements (PTAs) on Indonesia, using a combination of a dynamic global computable general equilibrium (CGE) model and a microsimulation tool (GIDD). The analysis focuses on two major PTAs currently under negotiation by Indonesia: the Regional Comprehensive Economic Partnership (RCEP) and the European Union-Indonesia Comprehensive Economic Partnership Agreement (EU-CEPA). It also considers the potential impacts of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the FTAAP (Free Trade Area of the Asia-Pacific), as well as the TPP-11 and TPP-15 scenarios.

Main Findings

1. Economic Impacts

• The EU-CEPA is expected to yield the largest macroeconomic gains for Indonesia, with an increase in GDP of 2.13% by 2030, compared to:
  • RCEP: 0.18%
  • FTAAP: 0.89%
  • TPP-15: 1.16%
• The EU-CEPA has the highest expected reductions in trade barriers, especially non-tariff measures (NTMs), and a high share of bilateral trade.
• The CPTPP is projected to have negligible impact on Indonesia due to low trade diversion.
• The TPP-15 scenario (which includes Indonesia) is expected to generate a positive impact, with an increase in GDP of 0.58%.

2. Distributional Impacts

• The EU-CEPA leads to the highest income growth at every point of the income distribution.
• However, the gains are regressive, meaning they benefit richer households more than poorer ones.
• This regressive effect is driven by increased skill wage premia in skill-intensive sectors, particularly services.
• The growth incidence curve for the RCEP is U-shaped, indicating greater benefits for lower and higher income groups.
• The FTAAP and TPP-15 show mildly inverted U-shaped growth incidence curves.
• The GIDD microsimulation model distributes macroeconomic results to households based on Indonesia’s 2014 National Socio-Economic Household Survey.

3. Key Economic Sectors

• Wearing apparel and textiles are among the most affected sectors, with output growth of 76% and export growth of 117% under the EU-CEPA.
• Services (especially trade and transport, construction, and financial and business services) are also major beneficiaries.
• Textiles and wearing apparel benefit from high intermediate input usage (70% and 63% respectively), leading to significant price reductions.

4. Trade Barriers in EU Markets

• Indonesia faces relatively high protection in the EU market, even though tariffs are low (0.02%).
• NTMs in the EU market are among the highest in all PTAs, with an average of 15.42%.
• The highest NTMs are found in food processing (40.88%), communication and business services (40.23%), construction (32.47%), trade and transport (32.03%), agriculture (31.81%), and textiles (30.82%).

5. Macroeconomic Modeling Framework

• The LINKAGE model is used to simulate dynamic, multi-country CGE effects of PTAs.
• The model includes 17 production sectors and 35 countries/regions.
• It accounts for changes in savings, investment, and productivity, with productivity growth calibrated to historical trends.
• The GIDD model is used to distribute macroeconomic impacts to household-level welfare, incorporating demographic, migration, and price changes.

Key Assumptions and Methodology

• Baseline scenario is based on GTAP 9 data (2011) and World Bank projections.
• Tariff and NTM reductions are based on published schedules from ITC and MAcMap.
• Only three-quarters of measured NTMs are considered as actual trade barriers, with the rest assumed to be quality-increasing regulations.
• Actionable NTMs are politically feasible reductions, with different thresholds for goods and services.
• NTMs are modeled as iceberg trade costs, which reduce with trade liberalization.

Conclusion

The paper highlights the importance of NTMs in shaping the economic and distributional outcomes of trade agreements. While the EU-CEPA provides the highest economic gains, its regressive distributional effects suggest that poor households may be disproportionately affected. The RCEP and FTAAP show more equitable income distribution, but with lower overall gains. The microsimulation approach allows for a more nuanced understanding of how trade liberalization affects different segments of the population, which is crucial for policy design and poverty reduction strategies.

The study contributes to the understanding of PTAs' impacts in Indonesia, a country with a historically low engagement in deep trade agreements, and emphasizes the need for a multi-country modeling approach to capture global feedback mechanisms.