2014年-世界发展银行全球_CGE_Analysis_of_the_Impact_of_Foreign_Direct_Investment_and_Tariff_Reform_on_Female_and_Male_Wages_54页_1mb

Summary of "CGE Analysis of the Impact of Foreign Direct Investment and Tariff Reform on Female and Male Wages"

Core Content

This paper presents a computable general equilibrium (CGE) analysis of the impact of foreign direct investment (FDI) and tariff reform on female and male wages in Tanzania. It is the first study to incorporate modern trade theory into a CGE framework to assess gender implications of trade reform.

Main Viewpoints

• Tariff Reform and FDI Effects: The study finds that tariff reform and reduction of regulatory barriers lead to real wage increases across all worker categories. However, male wages increase more than female wages due to the greater use of males in business services.

• Skill Intensity and Wages: The most skilled workers (both male and female) benefit more from the real wage increases, as they are more intensively used in business services. The analysis suggests that business services demand more educated workers, which could lead to wider wage gaps if female education is not improved.

• Model Innovations: The model extends previous CGE analyses by:

  • Incorporating FDI and multinational enterprises (MNEs) in advanced service sectors.
  • Using the Dixit-Stiglitz-Ethier framework to capture productivity gains from increased product variety.
  • Including a three-dimensional (sex-sector-skill) data set for 52 sectors and four skill levels.
  • Accounting for different cost structures between foreign and domestic firms in business services.

• Contradiction with Stolper-Samuelson Theorem: The results contradict the predictions of the Stolper-Samuelson Theorem, which suggests that trade liberalization should benefit labor-abundant countries. Instead, the model shows wage increases due to productivity gains from variety and FDI, not just labor abundance.

• Gender Wage Gap: The study identifies a significant gender wage gap in Tanzania, even after accounting for worker characteristics. It emphasizes that gender discrimination may exist, but precise measurement is difficult. The wage gap could also be attributed to sectoral differences in labor demand and occupational segregation.

• Policy Implication: The paper concludes that investing in female education is crucial to increase their human capital and enhance their competitiveness in business services and other modern occupations. Without such investment, the wage gap between males and females is likely to widen further.

Key Information

• Data Source: The study uses data from the Integrated Labour Force Surveys (ILFS) of 2001, provided by the National Bureau of Statistics (NBS) of Tanzania.

• Skill Levels: Four skill levels are considered: Unskilled, Laborers, Technicians, and Professionals. The data set distinguishes labor and wages by gender across these skill levels.

• Sectoral Analysis: The analysis covers four broad sectors:

  1. Business services (where FDI is allowed and regulatory barriers are reduced).
  2. Dixit-Stiglitz goods (produced under monopolistic competition with increasing returns to scale).
  3. Agriculture.
  4. Other CRTS sectors (constant returns to scale sectors).

• Factor Intensity: There are significant differences in factor intensity across sectors:

  • Business services are capital-intensive.
  • Agriculture heavily uses female laborers.
  • Dixit-Stiglitz goods are more capital-intensive than other sectors.
  • Technicians are the most intensively used labor category in both business services and other CRTS sectors.

• Model Structure:

  • Tanzania is modeled as a small open economy.
  • The model includes 12 household types in its original version, but is simplified to a single representative agent for this study.
  • It incorporates imperfect competition and increasing returns to scale in certain sectors.
  • The model uses ad valorem equivalents to represent regulatory barriers to FDI and domestic firms.

• Wage Reallocation: The study explores how labor is reallocated across sectors due to trade and FDI liberalization, and how this affects wage levels.

• Limitations: The study acknowledges that gender wage gaps may be influenced by non-discriminatory factors such as education levels, occupational choices, and sectoral demand. It also notes that detailed data on individual attributes (e.g., marital status, child care responsibilities) are not available, which limits the ability to precisely assess discrimination.

Conclusion

The CGE model highlights the importance of FDI and trade liberalization in driving productivity and wage increases, particularly in business services. It underscores the gendered impact of these reforms, with male workers benefiting more due to their higher skill intensity in these sectors. The paper calls for targeted investment in female education to reduce the gender wage gap and enhance their participation in high-skilled, modern sectors.