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Does Openness to Trade Affect Total Factor Productivity Growth: Evidence from 45 Japanese Manufacturing Industries Insang Hwang1 International Christian University Eric C. Wang 2 National Chung Cheng University Received 17 August 2004; accepted 20 October 2004
Abstract
This study examines the effects of openness to trade on total factor productivity (TFP) growth using the data from 45 Japanese manufacturing industries. The extreme bound analysis (EBA) test is employed to perform a sensitivity analysis. The results of EBA test of Japanese manufacturing industries do not support the hypothesis of “the virtuous cycle of trade and growth.” First, among several measures of growth, output growth appears to be mostly robust and positive in explaining the TFP growth of 45 Japanese manufacturing industries. Second, most of the variables of openness to trade do not show a robust and positive relationship with TFP growth. Considering these findings that trade expansion has an insignificant and ambiguous effect on TFP growth, a scale effect of output growth may have affected the growth of Japanese manufacturing industries for the period of 1973-1998. 1
Insang Hwang, Division of Social Science, International Christian University, 310-2 Osawa, Mitaka, Tokyo, Japan, (e-mail)
[email protected]. 2 Eric C. Wang, Department of Economics, National Chung Cheng University, Chia-yi 621, Taiwan, ROC. The authors would like to thank two anonymous referees for their helpful comments. We wish to thank Tsutomu Miyagawa and Ron Netsu for their help with the data. The first draft of this study was presented at the International Symposium on Foreign Trade, FDI, and Industrial Development held at National Chung Cheng University, Taiwan, ROC, March 27, 2004.
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Keywords: Openness to Trade; Export-led Growth Hypothesis; Extreme Bound Analysis; Japanese Manufacturing Growth; Total Factor Productivity growth JEL classification: H43; O47; L60
1
Introduction
There have been unending questions and researches about the sources of the total factor productivity (TFP) growth by many researchers. The reason is that TFP growth has been representing a measure of technological progress, which has been postulated as an essential factor of economic growth by the neoclassical growth theory: an economic growth without technological growth cannot be sustained and would be stopped in the end (Solow (1956)). Among various sources of TFP growth, openness to international trade (or export-led growth strategy) has been an important factor for a possible source of TFP growth, especially for rapidly growing economies such as Japan and other High Performance Asian Economies (HPAE) during the 1950s and the 1990s.3 In order to expand the shares of manufacturing exports to the world economy, it has been well known that these countries adopted a variety of measures to promote foreign trade for the era of high economic growth. Therefore, a hypothetical relationship can be constructed between TFP growth (or output growth) and openness to foreign trade, which is termed as the virtuous cycle of trade and economic growth. A number of channels have been identified by the literature through which exports can be growth enhancing. The first of these is that the export sector may generate positive externalities on non-export sectors through more efficient management styles and improved techniques (Feder (1982)). The second is related to the fact that export expansion may increase productivity by offering greater economies of scale (Helpman and Krugman (1985)). Third, exports are likely to alleviate foreign exchange con3 For details, see Nishimizu and Hulten (1978) for Japanese economic growth for the period 1955-1971. And, World Bank (1993) and Page (1994) for HPAE’s economic growth for the period 1960-90.
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straints and can thereby provide greater access to international markets (Esfahani (1991)). In addition, the above arguments have recently been supplemented by endogenous growth theories, which emphasize that international trade seems to facilitate long-term growth by increasing returns to scale, productivity spillovers, a higher rate of technological innovations, and a dynamic learning from abroad (Romer (1986), Lucas (1988), Rivera-Batiz and Romer (1991), and Grossman and Helpman (1991)). Despite the popularity of the effects of openness to trade on economic growth, empirical evidence is inconclusive. The inconclusive results regarding openness to trade and growth nexus have been partly attributed to different methodologies and model specification, as well as to diversities in the countries analyzed. While a substantial amount of literature utilizing a range of cross-section type methodologies support a significant and positive association between exports and growth, the time series evidence fails to provide uniform support for this hypothesis. The empirical difficulties in finding a link between export and growth in the time series work might be related to country-specific factors, which are not taken into account by cross-country analyses, given that these studies implicitly assume a common economic structure across different countries. In the case of cross-section (or panel) analysis, Harrison (1996) applies the extreme bound analysis (EBA) approach to the panel data of a large number of LDCs. A positive relationship between GDP growth and openness to trade is confirmed. Edwards (1998) uses the panel data of 93 countries for the years of 1960-90 and finds robust and positive results of openness on TFP growth. In a study regarding the APEC countries over the period of 1980 to 1987, Wu (2000) decomposes the TFP growth into technological progress (TP), technical efficiency (TE), and scale efficiency (SE). He finds that openness to trade has a positive impact on both TP and TE. Miller and Upadhyay (2000) employ a pooled time-series and cross-section data with EBA robustness analysis in order to investigate the link between trade orientation and TFP growth. They find that greater openness benefits TFP growth. Using the real openness measure, Alcal´a and Ciccone (2004) show that the causal effect of foreign trade on productivity across countries is statistically and economically significant as well as robust. Their findings imply that the channels which international trade and scale affect aver-
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Does Openness to Trade Affect Total Factor Productivity Growth
age labor productivity work through total factor productivity. In the case of time series analysis, Hwang (2002) found among the trade sectors that trade growth variables have a robust relationship with technological progress by using EBA method. Among the times series analysis using the causality test, the direction of causality between GDP growth and trade growth is claimed to be mixed or uncertain. The results of Marlin (1992), Serletis (1992), and Bahmani Oskooee et al (1991) appear to favor the export-led growth (ELG) hypothesis. Kunst and Marlin (1989), and Henriques and Sadorsky (1996) show evidence of unidirectional causality from output to export (i.e., growth driven exports). Chow (1987) and Ahmad and Harnhirun (1995) seem to support bi-directional causality. However, Jung and Marshall (1985), and Sharma and Dhakal (1994) reveal no consistent causal pattern and also cast doubt on the validity of ELG hypothesis. In addition to the studies using aggregate or industrial data, recent studies began to use firm/plant level data. Most of them show that there is a positive link between trade openness and production efficiency. That link is significant in their decision of export participation (Aw et al (2000), Bernard and Jensen (1999), Hay (2001), and Wacziarg (2001)).4 Together with the inconclusive evidences for evaluating openness to trade and growth nexus, authors such as Krugman (1994) and Rodrik (1995) are skeptical of the trade liberalization-TFP growth nexus. In addition, Rodrik and Rodriguez (2000) consider that the empirical studies do not provide convincing evidence. They further argue that the effect of openness on growth is, at best, very tenuous, and at worst, doubtful. Chen and Tang (1990), using the TFP of Taiwan’s manufacturing industries, indicate that although the scale economies has a reliable factor in explaining the productivity growth for the majority of industries, the export expansion has an ambiguous effect on productivity. Now, the main issue of this study is the effectiveness of trade openness on TFP growth for the Japanese economy. As the literature discussed above, the mixed and uncertain relationship between GDP growth and trade openness is also found in the Japanese economy based upon several times series analyses. Testing a simple simulta4 Aw et al (2000) for Taiwan and South Korea firms, Bernard and Jensen (1999) for U.S. manufacturing plants, Hay (2001) for Brazilian firms, and Wacziarg (2001) for 57 countries including developing and developed countries.
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neous equation model for several Asian economies, Chen (1979) concludes that Japan does not fit the export-led growth hypothesis in the period of 1950-70. Afxentiou and Seretis (1991) report that Japanese GDP growth is Granger-caused by export growth during the period of 1950-85. But, Marlin (1992) finds that export growth Granger causes productivity growth, while the reverse link is only significant at the 10% confidence significant level. In using five very different tests including the Granger causality test, Boltho (1996) suggests that domestic forces rather than foreign demand propels a longer growth in the Japanese economy during 1913-1990. The main objective of this study is to test the robustness of the effects of trade expansion on TFP growth by probing into 45 manufacturing industries of Japan during the period of 1973-1998. More precisely, we follow a two-stage method by first estimating the TFP growth of each industry and then performs a sensitivity test. We apply EBA methodology, a variant of Leamens regression technique, to examine the impacts of changes in export, total trade, and import penetration ratio on the sectional TFP growth of Japanese manufacturing. We use the EBA method for several reasons. First, the EBA method can be applied to construct a legitimate theoretical model, while the Granger causality method is not usually supported by a proper economic theory. We employ the TFP growth model of Kwon (1986) for building a basic regression equation for the EBA test. Second, the EBA test seems to be powerful in selecting unique robust relationships among several variables. In this context, our main interest is to test the robustness of targeting variables and not to find the degree of impact between dependent and independent variables. Third, the EBA method can evaluate the model of TFP growth more properly than that of the Granger causality analysis. Because TFP growth appears as residuals, and those can be explained by various factors from outside of the model, such as macroeconomic factors. Thus, to include macro-economic variables affecting TFP growth into the regression framework seems to be adequate. The rest of this study is constructed as follows. Section 2 reviews briefly on the growth and trade of Japanese manufacturing. Section 3 introduces the model of TFP growth and EBA methodology. Section 4 reports the empirical results. The last section is the conclusion.
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2
Does Openness to Trade Affect Total Factor Productivity Growth
A Brief View on Japanese Manufacturing Growth
The data used in this study were gathered from JIP (Japan Industry Productivity) database of Hukao et al (2003). All the growth rates including TFP growth were calculated by using the data from JIP database. JIP database contains annual information on 84 sectors, including 49 non-manufacturing sectors, from 1970 to 1998 based on the 1990 constant price. It includes detailed information on factor inputs, annual input-output tables, relatively reliable deflators, and some additional data R&D stock, Japan’s trade data with trading partners, and other factors.
2.1
Economic Growth and Trade Shares
Since the 1970s, the growth rate had slowed down rapidly and became stagnant during the 1990s. Table 1 reports the factor decomposition of economic growth during 1973-1998. The average annual growth rates of real gross output in aggregate economy (GGDP in Table 1) achieved during 1973-1979, 1980-1989, and 1990-1998 were 2.91, 3.34, and 0.98 percents, respectively, and obviously indicated a declining trend. In addition, during 1973-1979, 1980-1989, and 1990-1998 manufacturing economy grew by average annual growth rates of 2.19, and 3.69, and -0.03 percents, respectively. It was acknowledged that TFP growth contributed greatly to the economic growth during the period 1950s and 1960s. But, TFP growth was no more an important factor since the 1970s, showing average annual growth rates of 0.52 percents with its contribution rate of 8 percents in manufacturing output production during 1973-1998.5 The effects of factor inputs, especially the growths of capital stock and intermediate inputs, could explain most of the Japanese economic 5
For the period 1953-1971, Denison and Chung (1976) reported that TFP grew by an average annual rate of 5.88 percents with its contribution rate of 58.37 percents to output growth. For the period 1955-1971, Nishimizu and Hulten (1978) reported that TFP grew by 2.88 percent with its contribution rate of 25.15 percent to output growth.
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Table 1: Decomposition of Japanese Economic Growth Sectors/Year GGDP GLAB GCAP GINT GTFP GMGDP GMLAB GMCAP GMINT GMTFP
1973-79 2.91 1.84 (0.16) 7.37 (0.63) 2.72 (0.23) -0.17(-0.01) 2.19 0.25 (0.04) 3.78 (0.59) 1.94 (0.30) 0.46 (0.07)
1980-89 3.34 2.21 (0.21) 4.75 (0.46) 3.26 (0.31) 0.14 (0.01) 3.69 0.89 (0.09) 4.78 (0.49) 3.49 (0.36) 0.58 (0.06)
(average annual growth rate, %) 1990-98 1973-1998 0.98 2.71 0.64 (0.16) 1.79 (0.19) 2.89 (0.74) 5.29 (0.56) 0.44 (0.11) 2.34 (0.24) -0.03(-0.01) 0.02 (0.00) -0.03 2.39 -0.68 (-0.37) 0.44 (0.06) 2.77 (1.51) 4.27 (0.59) -0.28 (-0.15) 1.97 (0.27) 0.02 (0.01) 0.52 (0.08)
Notes: i) All growth rates were calculated by increasing rate. The values in the parentheses are the contribution rates for the output growth. ii) GGDP and GMGDP represent the growth rates of aggregate Japanese GDP and manufacturing GDP, respectively. GLAB, GCAP, GINT, and GTFP are the growth rates of labor, capital stock, intermediate input, and TFP for the aggregate economy, respectively. GMLAB, GMCAP, GMINT, and GMTFP are the growth rates of labor, capital stock, intermediate input, and TFP for the aggregate manufacturing, respectively. iii) All data were calculated from JIP Database [Hukao et al (2003)].
rise. The average annual growth of capital stock was 5.29 percent in the case of aggregate economy and 4.27 percent in the case of manufacturing, respectively. The down slide of Japan’s economic growth rate in the 1990s was triggered by the burst of Japan’s financial bubble in late 1989 due to the raising of interest rates by the Japanese banks. Its economic hardship after 1989 intensified further because of the outbreak of the financial crisis in late 1997. In 2001 Japan’s economy suffered another negative growth rate of -0.12 percent.6
6
Hayashi and Prescott (2002) explained the stagnant growth Japanese economy in 1990s by two factors; declined TFP growth and working hours.
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Table 2: Trade Shares of Japanese 45 Manufacturing Sectors: 1973-1998 Sector 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
Livestock products Processed marine products Rice polishing, flour milling Other foods Beverages Tobacco Silk Spinning Fabrics and other textile products Apparel and accessories Lumber and wood product Furniture Pulp, paper, paper products Printing and publishing Leather and Leather products Rubber products Basic materials Chemical fibers Other chemicals Petroleum products Coal products Stone, clay & glass products Steel products Other steel Non-ferrous metals Metal products Machinery General machinery equipment Household equipment Other electrical machinery Motor vehicles Ship buildings Other transportation equipment Precision machinery & equipment Other manufacturing
(percentage per year, %) Export Shares Import Shares 1973 1998 1973 1998 0.3 0.3 21.4 22.3 0.7 1.7 14.4 31.1 0.7 0.3 0.5 0.8 0.7 0.5 6.4 0.7 0.3 0.2 2.3 5.2 0.6 0.8 0.7 9.2 –17.6 6.7 –134.8 40.7 4.8 7.1 1.3 38.3 9.3 13.7 5.7 26.3 2.7 0.7 4.1 27.8 1.1 0.2 8.0 32.3 0.5 9.0 0.7 9.2 2.7 –5.8 4.3 –10.8 0.5 0.2 0.8 0.6 6.4 2.7 6.6 71.5 10.0 16.5 2.1 9.1 6.8 8.8 4.5 5.0 23.2 28.1 1.0 4.2 6.1 7.5 9.4 7.6 1.5 1.9 6.9 93.5 0.2 –0.6 0.1 –0.2 3.2 2.2 0.7 10.1 0.2 3.6 3.3 6.5 13.1 7.9 0.1 1.1 3.8 19.8 15.4 41.4 6.2 2.3 0.7 1.4 10.2 20.4 3.8 4.2 8.2 26.2 1.9 10.0 21.1 18.9 1.8 7.4 7.3 28.4 3.5 13.0 11.2 22.2 0.5 2.5 45.4 70.5 0.9 2.4 20.8 31.1 5.7 27.2 26.6 28.4 13.4 23.8 6.4 5.5 4.4 9.1
Note: i) Each share is the share of exports (or imports) in real gross output. ii) JIP Database [Hukao et al (2003)] was used for calculation.
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Table 2 numerates the trade shares of each industry to its own gross output. When this ratio increases, an industry seems to export its goods to foreign markets more than before and appeared to be adopting an export oriented industrial strategy. Furthermore, the changes in the trade shares can be interpreted as the alterations in the comparative advantage of Japanese manufacturing industry. In the 1990s, Japan exports mainly technology intensive and high value added goods to overseas market, while she imports labor intensive and low value added goods from developing countries. Thus, light industry goods such as silk, spinning, and apparels were supplied by imports. In particular, the import shares in gross output of spinning increased from 1.32 percent in 1973 to 38.4 percent in 1998. In the case of export shares in Japanese heavy industry output, the export shares of nonferrous metal, general electrical machinery, electrical machinery, motor vehicles, and shipbuilding increased greatly. In particular, those of electrical machinery increased from 8.18 percent in 1973 to 26.3 percent in 1998. Also, it was observed that machinery manufacturing was the industry group with the largest shares in both aggregate manufacturing of exports and imports. For the period 1992-2001, while average machinery import shares in aggregate manufacturing imports were about 40 percent, the average export shares of machinery manufactures were almost 70 percent in 1998.7 Among machinery manufacturing, general machinery, other electrical machinery and motor vehicles were the major items in contributing to export shares.8
2.2
TFP, Output, R&D Stock, and Exports
We introduce the growth pattern of TFP, output, R&D stock, and exports in 45 sub-manufacturing industries. Table 3 reports the growth rates of these variables. With regard to aggregate manufacturing, TFP grew by an average annual growth rate of 0.52 percent, while output 7 The source is East Asian Economic Perspectives: Recent Trends and Prospects for Major Asian Economies by The International Center for the Study of East Asian Development. 8 For the period 1992-2001, the export shares of general machinery, office and computer machinery, telecommunication machinery, other electrical machinery, road vehicles, and other transport equipment were 16.85, 8.40, 6.68, 15.10, 19.82, and 2.79 percents, respectively.
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increased by that of 2.39 percent. The growth rates of R&D stock and exports were much bigger than those of TFP and output by 5.52 and 5.01 percents, respectively. In order to get economic inferences from the sub-manufacturing industries, we divided 45 sub-manufacturing industries into two groups of light and heavy industries. Light industry group includes from the industry of livestock products (#11) to that of rubber products (#26). Heavy industry group covers from basic metals (#27) to precision machinery and equipment (#44). Other manufacturing (#45) is included into light industry. In the case of light industry, most of the growth rates (TFP, output, and exports) were showing very small and even in the negative rates, while only R&D stock showed larger values. TFP growth rates were negative in 7 industries. Negative growth of exports accompanied with negative output growth was noted in 6 industries. The stagnant and even negative values of export and output growths in light industry after the 1970s seem to imply that lots of Japanese light industry manufactures faced difficulties in domestic and foreign macro-economic surroundings, specifically in lower productivity growth, rising labor wages, and the appreciation of the Japanese Yen since the 1970s. On the contrary, the growth of R&D stock seemed to be positive and larger than other growth rates. The reason for the larger rates in R&D stock for these industries seemed to be that the initial level of R&D stock was small. Introducing the growth pattern of heavy industry, compared to light industry, negative growth rates were found in fewer industries. TFP growth rates were negative only from other steel (#34) and other transportation equipment (#43). The growth rates of exports were negative in 4 industries. Output growth rates were negative in 5 industries. However, R&D growth rates were all positive. As the light industries were affected, these heavy industries in Japanese manufacturing seemed to be vulnerable to both a rise in wage and an appreciation of the Japanese Yen since the 1970s. In this context, the international competitiveness of Japanese industry adopting labor-intensive technology seemed to have deteriorated.
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Table 3: Growth Rates of TFP, Output, R&D stock, and Exports in Japanese Manufacturing: 1973-1998 Sector Aggregate Manufacturing 11 Livestock products 12 Processed marine products 13 Rice polishing & flour milling 14 Other foods 15 Beverages 16 Tobacco 17 Silk 18 Spinning 19 Fabrics and other textile products 20 Apparel and accessories 21 Lumber and wood product 22 Furniture 23 Pulp, paper, paper products 24 Printing and publishing 25 Leather and Leather products 26 Rubber products 27 Basic materials 28 Chemical fibers 29 Other chemicals 30 Petroleum products 31 Coal products 32 Stone, clay & glass products 33 Steel products 34 Other steel 35 Non-ferrous metals 36 Metal products 37 Machinery 38 General machinery equipment 39 Household equipment 40 Electrical machinery 41 Motor vehicles 42 Ship buildings 43 Other transportation equipment 44 Precision machinery & equipment 45 Other manufacturing
(average annual GTFP GOP 0.52 2.39 –0.03 3.21 0.14 0.77 –0.69 –0.16 –0.20 2.42 –0.20 2.39 0.62 1.39 0.72 –4.98 –1.37 3.48 0.20 –1.99 –0.01 –0.50 0.96 –1.56 0.36 0.21 0.50 1.36 –0.20 2.79 0.23 –0.07 0.52 1.96 10.35 1.79 1.27 –0.22 1.78 5.36 0.42 1.61 0.46 –0.30 0.57 –0.16 0.84 –1.08 –0.19 0.32 0.86 1.81 0.17 0.73 0.75 3.10 0.25 1.82 1.45 5.31 2.79 11.39 0.28 4.41 0.24 –2.88 –0.03 0.56 1.28 3.24 0.28 2.55
growth rate, %) GRD GEX 5.51 5.01 9.01 3.17 1.56 –4.80 –1.31 –3.52 3.88 1.24 10.63 1.30 n,a 11.97 –9.60 –91.23 7.08 2.05 3.81 –0.54 6.16 –5.28 –11.14 –7.27 58.02 2.35 3.75 n.a 5.56 –0.42 25.38 –3.29 5.58 3.95 2.34 2.77 5.29 0.51 5.94 6.20 3.96 2.58 36.28 n.a 5.63 –1.48 22.43 9.42 2.80 –1.57 5.35 8.52 4.24 –3.00 5.15 5.92 3.71 6.49 3.53 4.87 10.80 17.35 8.06 7.20 3.18 –1.23 7.07 2.12 8.43 3.49 10.52 1.99
Notes: i) GTFP, GOP, GRD, and GEX are growth rates of manufacturing TFP, output, R&D stock, and exports, respectively. n.a means data unavailable. ii) JIP Database [Hukao et al (2003)] was used for calculation.
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The major exports of Japanese manufacturing were machinery equipment, motor vehicles, and other high-tech products. The growths of high-tech industries in both exports and output seemed to be higher than any others. Output growth rates in machinery (#37), electrical machinery (#40), and motor vehicles were 3.11, 11.39, and 4.41 percents, respectively. Export growth rates in those industries were 5.93, 17.35, and 7.20 percents, respectively. In addition, TFP growth rates of high-tech industries seemed to be higher than any other industries. For example, TFP growth rates of household equipment (#39), electrical machinery (#40), and precision machinery and equipment (#44) were 1.46, 2.79, and 1.29 percents, respectively.
3 3.1
TFP Growth Model and EBA Methodology TFP Growth Model
TFP measures the economic and technical efficiency with which resources are converted into products. TFP growth includes a variety of elements as well as technical change such as economies of scale, quality improvements in inputs, imperfect product and factor markets, Xinefficiency due to government regulations, etc. TFP growth, which can be defined as a residual of output change obtained by ruling out the component of input changes, is considered to be a pure measure of technology improvement, if economy of scale can be disregarded. In this study, we adopt a commonly used Tornqvist expression of TFP growth that is a discrete approximation written as follows:
T F Pj,t ln T F Pj,t−1
!
Yj,t = ln Yj,t−1
!
−
" X 1 j
Xj,t (Sj,t + Sj,t−1 )ln 2 Xj,t−1
!#
, (1)
where Yj,t , Sj,t , and Xj,t are output of industry j, the cost shares of factor in industry j, and factor input of industry j, respectively. This formula shows that real output growth can be divided into two portions. One is due to input growth (Xj ) and another is due to TFP growth. We employ three factor inputs: labor, capital stock, and intermediate
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input. All the raw data of output, labor, capital stock, and intermediate input were gathered from the JIP database of Hukao et al. (2003) Although there does not exist a consensus theory to guide empirical work on TFP growth, in this study we set up a simple TFP growth model based on Kwon (1986) by including scale economies and R&D externalities as explanatory variables.9 Argument for scale economies emphasizes the benefits that can be derived by means of expanding the scale of operations. In the cases of countries whose domestic markets are small in size, TFP can be improved through exporting which affects the enlargement of production scale. This argument was emphasized by both Chen and Tang (1990) and Kwon (1986) in their empirical studies on Taiwan and Korea, respectively. Effort devoted to R&D denoted by its expenditure was considered a good proxy for domestic technical progress, which explicitly contributed to TFP growth. Kim (2003) found a significant impact of the ratio of R&D investment to output on the growth of TFP in Korea’s information technology industries. Goto and Suzuki (1989) suggested to use R&D stock in order to investigate spillover effects from R&D stock to TFP growth and found a significant spillover effect of R&D stock on TFP growth in Japanese manufacturing industries. We employed the data of R&D stock in this study. Based on Kwon (1986), the basic model of TFP Growth of this study is specified as follows:
GT F Pi = β0 + β1 GOPi + β2 GRDi + ui ,
(2)
i = each sub-industry in manufacturing, where GT F Pi is the TFP growth rate of industry i, GOPi is the growth rate of output in industry i, and GRDi is the growth rate of R&D stock in industry i. GOPi and GRDi are used in order to represent the scale effects and domestic technical progress, respectively. Therefore, the equation (2) becomes the basic regression model of EBA for 45 Japanese manufacturing industries.
9
In particular, Kwon (1986) decomposed TFP growth into three parts: changes in technology, economies of scale, and capital utilization.
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3.2
Does Openness to Trade Affect Total Factor Productivity Growth
EBA Methodology
In a widely cited paper, Cooley and LeRoy (1981) argued that economic theory “...ordinarily does not generate a complete specification of which variables are to be held constant when statistical tests are performed on the relation between the dependent variable and the independent variables of primary interest.” Existing empirical models regarding TFP growth, by the same token, do not completely specify the variables that should be held constant, while conducting statistical inference on the connection between TFP growth and the variables of primary interest. Levine and Renelt (1992) used the EBA method to investigate whether there existed a robust or fragile relationship between per capita income growth and a variety of macro-variables, even in the changes of conditional information set. In this paper, the EBA method was applied in order to test the robustness of openness to trade (M -variable) on TFP growth. Extended from the basic model of TFP growth in the equation (2), the econometric specification of EBA can be reformulated as follows:
GT F Pi = β0 + βI Ii + βM Mi + βZ Zi + ui ,
(3)
i = each of sub-manufacturing industry, where GT F Pi is the growth rate of TFP of each sub-industry i, Ii is a set of variables always included in the regression, which were specified as GOPi and GRDi in the equation (2), Mi is trade related variables, which are of primary interest, and Zi is a subset of variables chosen from a pool of macroeconomic variables, which are considered as potentially important explanatory variables. The M-variables chosen for each industry are the growth rate of export (GEX), the growth rate of import penetration ratio (GIP),10 and the growth rate of the ratio of total trade (exports + imports) to production (GTT). Seven macroeconomic variables are employed as Z-variables for Japanese economy, which seemed to be adequate based on the literature. They are the growth rate of 10
Following Okuda (1994), import penetration ratio is defined as ((import value)/(import value + production value - export value)).
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consumer price index (GCP), which indicates the inflation rate, the growth rate of the ratio of government consumption to GDP (GCG), the growth rate of government surplus or deficit (GSR), the growth rate of official interest rate (GIJ), the growth rate of money-M2+CD (GM2), the growth rate of domestic credit (GDC), and the growth rate of real exchange rate of Japanese Yen (GRE). These macroeconomic variables were selected for two main reasons in this study: empirical findings from the current literature and the developments of Japanese macroeconomic performance (King and Levine (1993), Easterly and Rebelo (1993), Fisher (1993), and Barro (1991)).11 In each of the EBA test for the equation (3), three of the Z-variables are employed as done in Levine and Renelt (1992). The core of EBA method involves varying the subset of Z-variables included in the regression to find the widest range of coefficient estimates on the variables of interest (M) that standard hypothesis tests do not reject. We first choose a M-variable that has been the focus of past empirical studies and run a basic regression that includes only the I-variables and the M-variable. We then compute the regression results for all possible linear combinations of up to three Z-variables and identify the highest and lowest values for the coefficient on the variable of interest, βm , that cannot be rejected at the 10 percent significant level. Thus, the extreme bound is defined by the group of Z-variables that produces the maximum (minimum) value of βm , plus two standard errors. The degree of confidence that one can have in the partial correlation between the GT F Pi and Mi variables can be inferred from the extreme bounds on the coefficient βm . If βm remains significant and of the same sign within the extreme bounds, then one can maintain a fair amount of confidence in that partial correlation. In such case, the result is considered to be “robust.” If the coefficient does not remain significant or if the coefficient changes sign, then one might feel less confident in the relationship between the GT F Pi and Mi variables, because alternations in the conditioning information set affect the statistical inferences that 11
King and Levine (1993) found that various measures of the level of financial development are strongly associate with real per capita GDP growth. Easterly and Rebelo (1993), Fisher (1993), and Barro (1991) reported that macroeconomic indicators and policies (i.e., fiscal structure, government’s budget surplus, inflation rate, foreign exchange rate, and etc) are consistently correlated with economic growth.
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one draws regarding the relationship between GT F Pi and Mi . In this case, the result is seen as “fragile.” The empirical results of EBA test for the robustness of effects of trade on TFP growth will be reported in the next Chapter.
4
Empirical Results
The main objective of this study is to test the robustness of openness to trade (M -variable) on TFP growth. Empirical results of EBA tests for each of Japan’s 45 sub-manufacturing industries are compiled. The main results of EBA tests are summarized in Table 4. In the case of aggregate manufacturing, only robust and positive effects on TFP growth come from output growth (GOP). None of the M -variable of openness to trade was robust in explaining TFP growth. Thus, TFP growth for aggregate manufacturing seems to be mainly explained by the scale effect of output growth. We apply our EBA test for the 45 sub-manufacturing industries. On the contrary to aggregate manufacturing, the EBA test results of the 45 sub-manufacturing industries are not clear-cut but complicated. First of all, there is a phenomenon, which is similar to aggregate manufacturing. Output growth rate of each sector (GOP) appeares to be so powerful in explaining most of TFP growth in Japanese 45 sub-manufacturing industries. It shows a robust and positive effect on TFP growth in 29 out of 45 sectors examined, which leave the results of fragile in only six sectors. But the effects of real R&D stock (GRD) on TFP growth are minimal and complicated as a whole. A robust and positive relationship between R&D stock growth and TFP growth is found in 5 out of 45 sectors: furniture (#22), rubber products (#26), petroleum products (#30), steel manufacturing (#33), and other manufacturing (#45). A negative and robust relationship is also found in 9 out of 45 sectors.
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Table 4: Summary of EBA Test on Japanese 45 Manufacturing Sectors: 1973-1998
Sector Aggregate Manufacturing 11 Livestock products 12 Processed marine products 13 Rice polishing, flour milling 14 Other foods 15 Beverages 16 Tobacco 17 Silk 18 Spinning 19 Fabrics and other textile products 20 Apparel and accessories 21 Lumber and wood product 22 Furniture 23 Pulp, paper, paper products 24 Printing and publishing 25 Leather and Leather products 26 Rubber products 27 Basic materials 28 Chemical fibers 29 Other chemicals 30 Petroleum products 31 Coal products 32 Stone, clay & glass products 33 Steel manufacturing 34 Other Steel 35 Non-ferrous metals 36 Metal products 37 General machinery equipment 38 Electrical machinery 39 Household equipment 40 Other electrical machinery 41 Motor vehicles 42 Ship buildings 43 Other transportation equipment 44 Precision machinery & equipment 45 Other manufacturing
I-variable GOP GRD R(+) F F F F F F F R(+) F R(+) F R(+) n.a R(+) F R(+) F R (–) F R(+) F R(+) F R(+) R(+) R(+) F R(+) R (–) R(+) R (–) R(+) R(+) R(+) F R(+) R (–) R(+) R (–) R(+) R(+) F F R(+) F R(+) R(+) R(+) F F R (–) R(+) F R(+) R (–) R(+) F R(+) R (–) R(+) R (–) F F R(+) F R(+) R (–) R(+) F R(+) R(+)
GEX F F F R(+) R (–) F R (–) F F R(+) R (–) R(+) R (–) n.a R(+) R(+) R(+) F R(+) F F n.a F F F F F R(+) R (–) F F F F F F F
M-variable GTT F F F F R(+) R(+) F F F F F F F n.a F F R(+) F R(+) F F n.a F R (–) F F F F R (–) F F F F F F F
GIP F F R(+) F R (–) F F F F F F F F F R (–) R (–) R (–) R (–) F F F F F R (–) F F F R(+) R (–) F F F F R(+) R (–) F
Notes: i) GOP indicates growth rate in real production value, GRD: growth rate in real R&D stock, GEX: the growth rate of exports, GTT: the growth rate of total trade to production, GIP: the growth rate of import penetration ratio.
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ii) R stands for Robust and F for Fragile. + and – mean positive robust or negative robust, respectively. n.a means data unavailable. iii) JIP Database [Hukao et al (2003)] was used for calculation.
Predominantly a negative and robust relationship between TFP growth and R&D stock growth is found in heavy industries: chemical fibers (#28), other chemicals (#29), non-ferrous metal (35), general machinery equipment (#37), household equipment (#39), other electrical machinery (#40), and other transportation (#43). This finding is contradictory to what we learned from Table 4 in which we found that heavy industries, such as machinery and equipment related industries, showed high TFP growth and also high R&D stock increases. Furthermore, the findings of Goto and Suzuki (1989) are not supported by our results. Goto and Suzuki (1989) investigated the spillover effects of R&D capital on the TFP growths of its own industry and other industries. In the case of the electronics industry, they found that the effect was greater where technological knowledge diffused from electronic-related industries to those industries with similar technological positions. We summarize the main empirical results of testing the robustness of openness to trade on TFP growth. Most of the results for testing the growth rate of export (GEX) in relationship with TFP growth are insignificant or negative signs when various combinations of the Zvariable are included in the sensitivity test. Positive and robust effect on TFP growth are 8 out of 45 sectors: rice, polishing, and flour milling (#13), fabrics and other textile products (#19), lumber and wood products (#21), printing and publishing (#24), leather and leather products (#25), rubber products (#26), chemical fibers (#28), and general machinery equipment (#37). Only 2 heavy industries, chemical fibers (#28) and general machinery equipment (#37), are robust and positive between export growth and TFP growth. However, there is a negative and robust relationship between export growth and TFP growth in 5 sub-industries: other foods (#14), tobacco (#16), apparel and accessories (#20), furniture (22), and electrical machinery (#38). It is surprising that electrical machinery (#38) as one of the typical heavy industry shows a negative and robust relation between TFP growth and export growth. In summary, the export growth effect on TFP growth is found to be limited to a small number of industries, which are mostly
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from light industries not heavy industries. This result does not appear to back up the “virtuous process of trade and growth” which what we expected in Japanese manufacturing. Regarding the variable GTT, which is defined as the growth rate in the ratio of total trade (export + import) to gross output. Most of the GTT show a fragile relationship with TFP growth. Only 4 out of 45 sectors are positive and robust in explanation for TFP growth: other foods (#14), beverages (#15), rubber products (#26), and chemical fibers (#28). Also, there is a negative and robust relationship in two typical industries of heavy and high-tech areas: steel manufacturing (#33) and electrical machinery (#38). Finally, most of GIP, which is defined as the growth rate of the import penetration ratio, is minimal in explaining the TFP growth. Only 3 out of 45 sectors are positive and robust: processed marine products (#12), general machinery equipment (#37), and other transportation (#43). In addition, 8 out of 45 sectors are negative and robust in explaining TFP growth: other foods (#14), printing and publishing (#24), leather and leather products (#25), rubber products (#26), basic metals (#27), steel manufacturing (#33), electrical machinery (#38), and precision equipment machinery (#44). It simply said that other things being equal more manufacturing products imported could penetrate the domestic market, and then more TFP growth could have been weakened in Japan’s industries. Finally, we briefly summarize our main findings. First, regarding the I-variables, only the growth rate of real production seems to show robust and positive effects on TFP growth. This result is similar to that found in Chen and Tang (1990), Okuda (1994), and Kim (2003). The R&D variable does not have robust effects on most of the industries. Second, the M -variables of openness to trade (the growths of export, total trade, and import penetration) do not show a positive robust effect on TFP growth for most of Japanese manufacturing industries. Third, the positive effects of openness to trade on TFP growth seem to be industry specific phenomena rather than explaining for most of Japanese manufacturing industries. A few industries show a robust relationship between TFP growth and the variables of openness to trade. Among them, there is a robust and negative relationship. Given our findings that trade expansion has an insignificant and ambiguous effect on TFP, a scale effect of output growth may have governed the growth
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of Japanese manufacturing industries for the period of 1973-1998.
5
Conclusion
In light of much debate about the growth experience of Japan and HPAEs, this study examined the significance of the effects of openness to trade on total factor productivity growth using data of 45 Japanese manufacturing industries. A Tornqvist expression of TFP growth was employed to define and calculate total factor productivity. The EBA test was employed to test the robustness of openness to trade on TFP growth. The main conclusion is that the EBA test results of 45 Japanese manufacturing industries seem not to comply with the hypothesis of “virtuous cycle of trade and growth.” Among several measures of growth factors including those of openness to trade, output growth was mostly robust and positive in explaining the TFP growth of 45 Japanese manufacturing industries. From the findings of this study, we have two economic implications. First, while the direct export effects faded out, the scale effect stood out as the dominant explanatory variable for TFP growth. Aside from its contribution to the production scale, export growth had a rather ambiguous and weak linkage to promote TFP growth in Japan. Therefore, it is still an unclear a priori that export expansion always enhances productivity for the case of Japan. Second, the positive effects of openness to trade on TFP growth seemed to be an industry specific phenomena and did not give explanations and applications to most Japanese manufacturing industries. The TFP growth of an industry will be affected positively or negatively by the development of foreign trade. It has been an usual belief that expanding shares of foreign trade will lead to positive effects on economic growth and also on TFP growth. Our study seems to question such a constant belief. Lots of empirical evidence from an aggregate economy might show only the positive aspects between export-led growth and TFP growth. But, when we analyze individual sub-industries or the micro level economy, we will have complicated and various kinds of relationships between openness to trade and TFP growth as our study have shown. “The virtuous dynamics between openness to trade and TFP growth” was
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not supported empirically by most of the 45 Japanese manufacturing industries. In order to obtain further empirical evidence, firm/plant investigations will be needed like Aw et al (2000) and Bernard and Jensen (1999).
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Appendix A. R&D Stock : Using real R&D expenditure, R&S stock was processed by using following estimation: RDKt = RDt + (1 − δ)RDKt−1 , where RDK t , RDt , and δ are R&D stock, R&D expenditure, and a depreciation rate, respectively. Detailed information can be found from Hukao et al. (2003). B. Data Description : In order to measure TFP growth of 45 Japanese sub-manufacturing industries, the Japan Industry Productivity (JIP) database was used. JIP database contained annual information on 84 sectors, including 49 non-manufacturing sectors, from 1970 to 1998 [Hukao et al (2003)] based on 1990 constant price. For the period 1973-1998, all variables were in real terms and all growth rates were calculated in the form of log difference, mainly for the consideration of data stationarity. GT F P : the TFP growth rate of manufacturing industry GOP : the growth rate of manufacturing output GRD : the growth rate of manufacturing R&D stock GEX : the growth rate of manufacturing export GIP : the growth rate of manufacturing import penetration ratio GT T : the growth rate of manufacturing total trade to production GCP : the growth rate of consumer price index
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GCG : the growth rate of ratio of government consumption to GDP GSR : the growth rate of government surplus or deficit GIJ : the growth rate of official interest rate (GIJ) GM 2 : the growth rate of money–M2+CD GDC : the growth rate of domestic credit GRE : the growth rate of real exchange rate of Japanese Yen
