1. “Learning by Exporting and High- tech Capital Deepening in Singapore Manufacturing Industries, 1974-2006 ” โดย ดร. เอกพล จงวิลัยวรรณ วันพุธที่ 23 กรกฎาคม 2551 เวลา 14.00 น.

2. Structural Characteristics Figure 1: Exports and High-tech Capital Formation in Singapore Manufacturing Industries, 1974-2006.

3. Figure 2: The Ratios of Export Intensity (XI) and High-tech Capital Deepening (T) in Singapore Manufacturing Industries, 1974-2006. Structural Characteristics (continued)

4. Figure 3: Scatter Plot of the Ratios of Export Intensity (XI) and High-tech Capital Deepening (T) across Singapore Manufacturing Industries.

5. Learning by Exporting Learning-by-exporting hypothesis refers to the mechanism through which exporting activities generate externalities via technological and informational spillovers and lead to productivity growth. However, the empirical evidence is rather mixed and less clear-cut. Clerides, et al. (1998), Bernard and Jensen (1999), Aw, et al. (2000) for South Korea, and Greenaway and Kneller (2004) find weak evidence of the learning effect. This is in contrast with the studies by Aw, et al. (2000) for Taiwan, Baldwin and Gu (2003), and De Loecker (2007).

6. High-tech Capital Accumulation High-tech capital deepening has been a source of debate that it results in higher productivity performance, thereby enhancing productivity growth and profitability. As is the learning effect, its impacts on the productivity growth are unclear. See Morrison and Berndt (1991), Oliner and Siechel (1994), Siegel and Griliches (1992), Siegel (1997), and Oliner and Siechel (2000).

7. Data Sources and Measurements The two-digit SIC manufacturing industries data are retrieved from the Census of Manufacturing Activities (CMA) from 1974 to 2006, provided by Singapore Economic Development Board (EDB) Various price deflators are gathered from Monthly Digest of Statistics Singapore (MDSS), provided by Singapore Department of Statistics.

8. Data Sources and Measurements (continued)

9. TFPG Measurements Following Feenstra and Hanson (1997), I assume the translog real value added function.,where and B is a 4x4 matrix of parameters. Definition: With the assumption of linear homogeneity, it is straightforward to show that

10. Econometric Models Specification I: Specification II:

11. Econometric Estimations and Discussions To control for unobservable industry heterogeneity, I employ the Generalized Least Squares (GLS) estimation based on the Random Effects model. I employ the Breusch-Pagan test to investigate whether the industry- specific effects exist. I employ the Hausman’s Specification Test to inspect whether GLS estimates are consistent. It is not appropriate to impose parameter restrictions at priori. I obtain unrestricted estimates and test whether they well behave.

12. Empirical Results: Specification I

13. Empirical Results: Specification II

14. Main Findings and Policy Implications The learning effect and high-teach capital deepening affect TFPG via non-neutral (factor-biased) technological progress. Some (Factors) May Lose! A policymaker should also take their income distribution effects into consideration – Not everyone gains from overall productivity growth, Some (Factors) May Lose!

15. Structural Changes

16. Main Findings and Policy Implications The factor-biased technological progress resulting from the export-led development and the high-tech capital accumulation is period-specific. It may be interesting to further investigate the factors that bring about the structural changes.

17. Contributions to TFPG By using the GLS estimates, I evaluate the followings at mean values.

18. Contributions to TFPG (continued)

19. Main Findings and Policy Implications The learning effect can explain TFPG more largely than a high-tech capital accumulation. A policymaker should be aware that only some industries gain from the export-led development and high-tech capital deepening. Some (Industries) May Lose!

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