Economic expert who has done extensive research on effects of population on economy. PhD student and lecturer at Kenyatta University.
Within a short time, China has grown to become a major economic center due to rapid continued economic growth over the years. Researchers have tried to explain this rapid economic growth through research, but none were exhaustively conclusive (Guo, Dall’erba, & Gallo, 2012). When China joined the world trade organization in 2001, it was termed as the beginning of true globalization. This opened the world as a market for Chinese goods. Since then, China’s export volume has been increasing and today is at an all-time high (The Impact of China Joining the WTO, 2017).
Foreign Direct Investment in China
In the graph in figure 1 above, during the base year, the growth rate starts at 100, which acts as the base rate. In this chart, it can be observed that Japan has had a total decline from 2006 to 2016, meaning that it is exporting a slightly lower proportion of the world's commodity in 2016 than it did in 2006. The other countries (China, Germany, the Netherlands, and the United States) have had a net growth over this period.
It is noteworthy that during the economic crisis of 2008, the growth rates of commodity export of all economic entities sharply decline in 2009. Another notable observation is that China’s exports have been growing at a higher rate far beyond that of the other economies. In 2012, Chinese export of commodities doubled what it was in 2006. It took only six years to double the annual commodity export for China in a period that Japan had negative growth, and this has seen China become the leading exporter of manufactured commodities in the world. According to the World Trade Organization report in 2017, China is the leading commodity exporter controlling 18% of commodity export market share in 2014, which accounts to almost $2.2 trillion (USD), followed by Germany and United States, which accounts for $1.288 and $1.164 trillion respectively (World Trade Statistical Review 2017, 2017).
In 1978, China underwent radical change in economic policies with the aim of stimulating growth. As an effect of the policy change, the total manufactured products grew by 7.5% from 1979 to 1982, a clear indication that China was heading in the right direction (Tisdell, 2009). The growth stimulated in 1978 persisted to date. As seen in figure 2, even after the 2008 economic crisis, China has maintained to grow FDI into the manufacturing sector at above 20% annually which is higher than any other country within this period.
Most of the FDI into China has been directed to the cities in the eastern coast with four major cities commonly referred to as the ‘tiger’ (Zuliu Hu, 1997). The local state policies in these cities made them to preferentially attract the bigger share of FDI (Wei, 2016). The federal government reacted to this and has instituted policies meant to expand the FDI destination to about 20 cities all within the eastern provinces by influencing the institution of attractive policies particularly tax policies.
The high inflow of FDI has appealed to many researchers and may have tried to access whether there is any correlation between FDI and China’s economic growth. In his study, De’murger (2000) argued that FDI is an effective channel for technology transfer and benefits the host country and in the case of China, the benefits are felt by the eastern provinces which have been home to most of the FDI in China. Contrary to this, Shiu and Heshmati (2006), Yeung and Mok (2002) and Ng (2006) found the negative impact of FDI on GDP or TFP growth, while Ying (2004) and Zhang (2002) did not find any significant impact. In his study, Zhang (2002) concluded that the contribution of foreign direct investment to China's technological progress through technology transfer is still not obvious. A study by Görg and Greenaway (2002) suggests that evidence of a positive spillover effect of foreign direct investment in the host country is sometimes weak and negative. This is because, as suggested by Aitken and Harrison (1999) and Konings (2001), resource redistribution from manufacturing to multinationals may initially be ineffective in terms of productivity.
Clearly, there is no theoretical consensus as to the effect of FDI in Chinese economic growth, however, one thing is apparent, China has managed to maintain a healthy positive economic growth since 1978 to date. From the previous studies it is not clear whether FDI is the real driver of economic growth in China, however, there is a consensus that growth in the manufacturing sector is a key driver in the economic growth in China (Haruchi, Smeet, & Chen, 2017).
This study will seek to answer the question of whether the level of FDI has an effect on growth of manufacturing sector output in China. In answering this question, the research will also answer two more related questions:
- Is there a correlation between the level of foreign direct investment and economic growth in China?
- Is high FDI responsible of the rapid economic development that China experienced in the since 1980?
The research question will seek to evaluate the relationship between growth in the manufacturing sector and whether this is influenced by FDI. To help achieve this, linear regression will be used to try and estimate the relationship between manufacturing sector value added output and net FDI using current value US dollar. Current value US dollar was chosen because this represents data that is currently and readily available and thus reduces the error that could be introduced in data manipulation to a particular base year. Net value for FDI was chosen because the if capital is flowing in and other capital flowing out, only the net is relevant as only that amount has an economic sense to the country. For manufacturing output, value added figure was used instead of total output because value added output captures efficiency of the industry to actually contribute to economic output. Total output could be misleading as the input could be even higher than the output making the industry contribute negatively to the net economy (GDP). The primary model will have two variables, manufacturing output as a factor of FDI. However, since there are many factors that actually do contribute to the output, controlling factors including, total labor in China, labor participation rate, and services industry value added output. There are three major factors of production, labor, capital and natural resources. Since natural resources are fixed, (Waterman, 1987) they are taken as constant for this study, labor is taken as flexible and the labor stock is determined by participation rate as well as total labor available. Services sector is competing for the same factors as well as it complements the manufacturing sector and thus its output level is a significant factor affecting the manufacturing sector.
Data for this study was collected from the World Bank database. This is a comprehensive database that allows one to access different metrics of data from different regions and different indicators. It is reputable as is watched closely by different economies and is often used for unbiased data sources as well as analyses. Data of years ranging from 1990 to 2017 was used. The range was chosen based on data availability for all the variables. However, the range of years was regarded as representative enough with 29 years.
To visualize the trend, data of the two main variables was expressed in a scatter plot with the most fitting trend line. The best fit trend line for both data was deemed to be a polynomial function of order 4 (see figure 3 and 4) Since both data are scattered in almost a similar trend defined by a polynomial of the same level, it was deemed hypnotized that there is a high correlation between the two variables. The high relationship was tested using linear regression analysis with FDI as the independent variable and Manufacturing output as the dependent variable.
Manufacturing output was first regressed against FDI (see results of this analysis are in table 2). The single regression was conducted at 99% confidence level and the p-value was so low to a power of -11 and thus the regression was model was found to be significant. The null hypothesis that there is no relationship between FDI and manufacturing output was rejected as the model indicated that there is indeed a relationship. The R square was 80.78% indicating that about 80.78% of manufacturing output can be accurately predicted using the level of FDI in China. The resulting model was (Values in USD)
Manufacturing output = 11.24 *FDI - 44149756529
To bring in the effect of other controlling factors, multiple regression was conducted with results as in table 3.
From table 3, the multiple regression with the controlling factors indicated that FDI and Manufacturing industry output are still significantly related. The regression results indicated that all the other controlling variables are also significant in estimating the level of manufacturing output except labor participation rate. Labor participation rate had a very high p value and it was deemed necessary to drop it from the analysis. A final analysis was conducted without the labor participation rate.
Man Output= 2.43*FDI + 1733.18 * total labor + 0.474 * Svs sector output -1,232,285,840,282.96
This model can be used to predict up to 99.77% of China’s manufacturing sector output at 99% confidence level.
Discussion and Conclusion
This analysis indicates that there is a very high correlation between the level of FDI and manufacturing industry productivity. This indicates that to promote manufacturing industry productivity, there is need for china to attract more FDI. A 1% change in FDI has a corresponding 2.43% change in FDI. Thereby, if china wants to fuel growth in manufacturing industry, it must attract more FDI. In addition, the labor force has a significant impact to the productivity of the manufacturing sector. A 1% change in labor force leads to 1733.18% corresponding change in manufacturing sector productivity in China. This would follow that it is important for china to control labor through macroeconomic policies such as control of birth and training/education. Further, a 1% change in services sector output leads to a 0.474% change in manufacturing sector. Although this is not a huge contribution, it is significant and thus there is a need to ensure that both the services and the manufacturing industry are all growing as they are complementary from the findings of this study.
In conclusion, there is a high dependency of manufacturing output to net flow of FDI, labor and services industry output. This study cannot single out one factor among the three as the key contributor of the rapid economic growth in China, However, the study can conclude that both FDI and Labor growth have been critical in fueling China’s economic growth.
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© 2019 Gitiya Geoffrey Karanja