Determinants of Foreign Direct Investment in China - Case Study Example

Determinants of Foreign Direct Investment (FDI) in China Table of Contents Introduction 3 2. Research hypotheses 4 3. Predictor variables 4 3 Gross domestic product 4 3.2. Labour wage 5 3.3. Innovation level 5 3.4. State ownership level 6 4. Descriptive analysis 6 5. Regression Analysis 9 6. Limitations 12 7. Conclusion 12 Reference List 13 1. Introduction China has successfully proved itself as one of the rapidly growing nations in the world over the past decade or so. The country’s deposit of rich resources has attracted a large number of foreign companies who have been more than encouraged to set extend their business operations in this country. Known as a country with very cheap source of labour and abundant natural resources, the economy of China has experienced an unprecedented growth in FDI over the last ten years. Consequently, China has demonstrated consisted economic growth with its GDP soaring new heights every passing year. According to organizational leaders, the stability in the Chinese economy is what attracts them the most when it comes to making investments. The country has shown increased acceptance for all forms of industries and sectors (Yuqing, 2006). The policies and reforms in terms of international business are adequately flexible which is why FDI activities have increased proportionately with every passing year. Chinese corporations, SMEs and small business hailing from all industrial sectors have extensively engaged in bilateral and multilateral trade which in turn has boosted the quantity of goods that have been sold overseas over the last decade. The flow of currency has been extensively fluid as a result of exports and that is why the country has been recording trade surpluses. Such prosperity has also garnered a lot of attention from international investors who have showed increasing interest in making investments in the Chinese economy (Boermans, Roelfsema and Zhang, 2011). A considerable amount of researches have focussed on the factors that have closely explained the movements in FDI inflows in the Chinese economy (Kolstad and Wiig, 2012; Cheng and Kwan, 2000). The abundance in literatures surrounding determinants of FDI in China bears evidence to the constantly growing interest among researchers as well as academic scholars in studying FDI activity in China. A major proportion of these literatures have either focused on determinants of FDI on the national or the regional realm. Nonetheless, not much research has been done on the determinants of FDI in industry or sector level. This is where the relevance of this research lies as the researcher will endeavour to indentify the aspects which may have a significant impact on the FDI inflow in China as per sector or industry (Kolstad and Wiig, 2012). This is precisely the reason why the researcher has chosen to select a dataset of the amount of FDI inflows by industry in China. Alongside that the researcher has identified four key factors which are believed to have an impact on FDI flow in an economy. The factors are gross domestic product (GDP), wage rate, innovation level and ownership level. The following section will involve a thorough explanation of the reasons that contributed to the selection of these variables. The researcher will mainly be testing four hypotheses with FDI inflow being the dependant variable and GDP, wage rate, innovation level and ownership level being the independent variables. At first a descriptive analysis will be carried out of all the variables which have been incorporated in this research. Thereafter, the researcher will be carrying out ordinary least squared regression in order to determine the statistical significance of the predictor variables. By doing so the researcher will be able to test the following hypotheses: 2. Research hypotheses H0: China’s GDP has no impact on the FDI inflow within the economy. H1: China’s GDP has a statistically significant impact on the FDI inflow within the economy. H01: Wage rate in China has no impact on the FDI inflow within the economy. H11: Wage rate in China has a statistically significant impact on the FDI inflow within the economy. H02: Innovation level in China has no impact on the FDI inflow within the economy. H12: Innovation level in China has a statistically significant impact on the FDI inflow within the economy. H03: Ownership level of companies in China has no impact on the FDI inflow within the economy. H13: Ownership level in China has a statistically significant impact on the FDI inflow within the economy. 3. Predictor variables 3.1. Gross domestic product GDP of a country is computed by summing up all services and products bought by consumers per year. For example when a baker buys butter and wheat to make cookies, the cost of raw materials is included in the final price of the cookies that is bought by consumers. The price of those cookies along with the final price of every other thing that are bought such as clothing, food or electronics is added to what the governments spend on health system and education and other services (Sun, Tong and Yu, 2002). This gives the figure which is termed as GDP or nominal GDP which includes all of a country’s exports while subtracting its imports. However it is not that simple to compute the growth rate in GDP. This is precisely because of the fact that if prices has increased in a particular year, it means inflation is included. Therefore it becomes hard to assess whether a country has actually produced more services and goods. Price fluctuations can hide the real situation of an economy. Ergo, by simply subtracting inflation one can get the real picture of whether a country’s economy is contracting or growing. This is the measure of a country’s GDP. GDP is the proxy for market size of Chinese economy. According to findings provided in empirical literatures, Gross domestic product has statistically significant positive impact on the FDI inflow in an economy. GDP has long been regarded as one of the fundamental indicators of market size and prosperity. This is one economic factor that is analysed by foreign investors while they consider making investment in a country (Zhao, 2003). The country’s GDP has augmented significantly with every passing year in the last five years. Although the GDP growth rate has not equivalently complimented the increasing GDP but it has not stopped foreign investors from extending their business operations in this part of the world. Assessment of GDP enables foreign investors to determine the economic stability of a country. It also helps them to assess the purchasing power of consumers within a particular economy (Cheng and Kwan, 2000). An economy that exhibits consistently growing purchasing power will always lure foreign investors to come and invest in such countries. As a consequence FDI inflow will augment in these countries. This is precisely the reason why GDP has been considered as one of the fundamental determinants of FDI in a country. 3.2. Labour wage Labour wage is also a fundamental determinant of FDI inflow in a particular country. As far as China is concerned, the cost of labour is considerably lower compared to labour rates that prevail in other countries (Ali and Guo, 2005). This is precisely the reason behind the prosperity of China across all industry and sector. Cheap labour in China has also attracted a large number of foreign investors. Foreign investors have been largely successful in reaping the benefits due to the availability of inexpensive labour which in turn has helped them to reduce their operating costs by a considerable margin. This in turn has allowed foreign investors to improve their overall profit margin. The abundance of extremely cheap yet skilled and experienced workforce across all industries have grabbed the attention of foreign investors hailing from different sectors. That is why people can find companies belonging from the apparel industry to companies operating in the technology industry all having their extended operations on this part of the world (Jun, 2002). The lower labour wages have been one of the significant contributors of a consistently increasing FDI inflow within the country for the past two decade or so. This is precisely the reason why findings from empirical literatures have pointed out the fact that there is a negative correlation between labour wages and FDI inflow in China which indicates that with decreasing wages in the country, FDI inflow has increased accordingly (Ali and Guo, 2005). However, whether this relation is statistically significant or not is the main issue that needs to be explored. That is why the researcher has included this predictor variable in order to apply the same in the regression model. 3.3. Innovation level A country’s innovation level is a vital indicator of the progress that is being made in the field of technology (Cheng and Kwan, 2000). China has rapid progress in the field of technology and this is evident in the amount of electronic and information technology companies that have evolved over the last decade or so. Chinese mobile manufactured have proven to be a heavy competition for leading mobile manufacturing companies in the world. Companies like Gionee and Xiaomi have captured a huge market in Asia and South East Asia thereby posing intense competition to corporations such as Samsung and Sony. Companies in China have thrived for creating innovation as they believe it to be one of the fundamental sources of competitive advantage and hence sustainable profit. This level of innovation has inspired foreign investors to enter the Chinese market through joint ventures with companies who have demonstrated their urge for the same (Shan, 2002). This is precisely the reason why the researcher has considered innovation level as one of the fundamental determinants of FDI inflow in China. Empirical researches have suggested that there is a positive correlation between innovation level and FDI inflow (Cheng and Kwan, 2000; Ali and Guo, 2005). In this case the proxy for innovation level is calculated as the ratio between the amounts of investment made in innovation to the country’s GDP according to sector. 3.4. State ownership level State ownership level is considered as another critical determinant of FDI inflow in a country. As far as the state ownership level in China is concerned, it is regarded as a critical indicator of economic reform and privatisation level in the country. Foreign investors assess the state ownership level in a country in order to determine the contribution of market forces as well as evaluate the efficiency of the market (Ali and Guo, 2005). It is obvious that companies would not be willing to extend their business in an economy which is highly regulated and lacks freedom in the market. This is precisely the reason why empirical literatures have suggested that there is a negative correlation between state ownership level and FDI inflow in a country (Zhang and Song, 2002). The author has considered this aspect of an economy within the regression model in order to determine whether there is a statically significant relationship between state ownership level and FDI inflow in China. The proxy for the state ownership level is calculated using the ratio between the number of workers and staffs in state owned entities and the number of workers and staffs in all types of ownership at industrial level. 4. Descriptive analysis The descriptive analysis revealed that China’s GDP has increased considerably over the last five years. This is largely because of the increasing purchasing power of consumers living in this part of the world. Moreover, a considerable increase in the amount of manufacturing export is another reason behind the country’s increasing GDP. The inward flow of FDI has also contributed significantly towards the rapidly growing GDP with every passing year. However, a gradual decrease in the FDI inflow over the last three years has caused a significant decrease in the GDP growth rate of the country. Figure 1: year on year FDI inflow (Source: China Statistical Yearbook, 2014; 2013; 2012; 2011; 2010) The increasing purchasing power parity can be attributed to the increasing wage rate per employee across all sectors and provinces in China. The country has prospered at an unprecedented level and the government has translated the same prosperity in the form of increased wages for its labour. The rising labour cost can also be another reason behind the decreasing level of FDI in China. Even after this increase in the labour wage rate, the FDI inflow has followed a relatively stable path. Figure 2: year on year GDP (Source: China Statistical Yearbook, 2014; 2013; 2012; 2011; 2010) The underlying reason behind this is that labour wage in China is considerably lower than labour wages in other countries. The country has shown increased acceptance for innovation that is why a considerable amount of investment has been made in the field of scientific research and innovation. Figure 3: year on year labour wage (Source: China Statistical Yearbook, 2014; 2013; 2012; 2011; 2010) This is evident from the increasing levels of innovation with every passing year. This gradual increase in innovation levels is sure to attract more and more foreign investors in the upcoming years. The state ownership level has declined significantly in the last five years. This is a positive sign for foreign investors because a decreasing state ownership level means an increasing economic freedom and a less regulated market. Such favourable conditions are expected to make bilateral trade convenient for foreign investors. Figure 4: year on year innovation level (Source: China Statistical Yearbook, 2014; 2013; 2012; 2011; 2010) Figure 5: year on year state ownership level (Source: China Statistical Yearbook, 2014; 2013; 2012; 2011; 2010) 5. Regression Analysis In this research five regression models have been implemented with FDI inputs in five different sectors (in China) as the dependant variable. The predictor variables in all those models are GDP, innovation level and state ownership level. The variable that has been excluded from the model is Wage rate due to data redundancy. The regression analysis on all the five models explained that GDP has a significant positive impact on the FDI inflow in different industrial sectors of China (t value 2.415). Consequently alternative hypothesis has to be accepted. This outcome is in complete alignment with the theories that have been set forth in empirical literatures. GDP is the indicator of the economic stability of a country. It helps analysts to assess the market prosperity in terms of the purchasing power of people, the export and imports, the trade balances, inflation rate and so on and so forth. Such a robust analysis enables analysts to evaluate the prospect of making an investment in a particular company and recommended strategies to foreign investors. China’s GDP has increased consistently over the past five years which in turn has not only contributed to a stable FDI inflow within the country but has also strengthened the economic constancy of the country. Production values are at an all time high which in turn has enabled Chinese companies to operate in joint ventures with foreign based companies. Figure 6: Regression model 1 As far as innovation level is concerned, the regression analysis revealed that this predictor variable also has no impact on the FDI inward flow in Chinese manufacturing sectors. In this case null hypothesis H02 has to be accepted (t value -0.822). In majority of the models, innovation level has a negative correlation with the dependant variable. However, this correlation is not significant at all signifying that inflow of FDI in Chinese industrial sectors is not triggered by the amount of investments that the country makes in scientific research and development. This outcome is quite contrasting to the results presented in empirical researches. Figure 7: Regression model 2 One of the major reasons that can be attributed to such a misalignment is that data for the last five years were only considered in the model. Data spanning over the last ten or twenty years would have yielded a different result altogether. Another reason behind this misalignment is the fact that the country’s FDI level decreased by a certain margin owing to a rise in the labour wages. The enhancement in the innovation level was not adequate enough to compensate for the steep increase in the labour wages which in turn decreased FDI inflow in Chinese industries. Figure 8: Regression model 3 Talking about the state ownership level, majority of the regression models revealed that this predictor variable has a statistically significant effect on the inflow of FDI in Chinese industries (t value -6.218). The correlation between the predictor and the dependant variable is negative in majority of the models. This in turn indicates that with lower state ownership levels the inflow of FDI increases. This is precisely because of the fact that lower state ownership levels indicate greater economic freedom and market flexibility. In such economies, resources are not heavily regulated by the government owned enterprise. This makes it easier for private enterprise to conduct bilateral trade and business with relative ease. In addition, lower state ownership level also indicates a free market economy which is a favourable operational situation for both domestic as well as foreign based companies. This is precisely the reason why FDI inflows in Chinese industrial sectors increased with decreasing levels of state ownership. Figure 9: Regression model 4 Figure 10: Regression model 5 6. Limitations One of the major limitations of the research data is that economic figures were collected for the last five years only which in turn restricted the study of some of the independent factors. Alongside that only four independent variables were taken into account for the purpose of statistical assessment. This has also restricted the scope of analysis that could have been done if the researcher selected more predictor variables. Therefore in order to improve the quality of these kinds of researches one should consider collecting data for longer time periods. Besides that more independent factors should be included. 7. Conclusion The determinants of FDI vary across countries. The factors which determine the inflow of FDI within a country depend largely on its economic situation. As far as the determinants of FDI in China are concerned, the researcher identified GDP and state ownership level to have a statistically significant impact. While the correlation between China’s GDP and FDI inflow was positive, the correlation between state ownership levels and FDI inflow was negative. The innovation in level in China did not have much impact on the country’s FDI inflow. The consistently increasing GDP of China indicated the country’s improving economic prosperity. Such a favourable economic environment encouraged more foreign investors to extend their business in this part of the world. On the other hand, the negative significant correlation between state ownership level and FDI inflow in China indicated that with decreasing level of state ownership the economic freedom within the country increased. This enabled foreign investors to operate in the Chinese economy with relative ease. Given the fact that the Chinese economy emulated the traits of a free market economy, foreign investors started showing greater interest in making investment in this country. Reference List Ali, S. and Guo, W., 2005. Determinants of FDI in China. Journal of global business and technology, 1(2), pp. 21-33. Boermans, M. A., Roelfsema, H. and Zhang, Y., 2011. Regional determinants of FDI in China: a factor-based approach. Journal of Chinese economic and business studies, 9(1), pp. 23-42. Cheng, L. K. and Kwan, Y. K., 2000. What are the determinants of the location of foreign direct investment? The Chinese experience. Journal of international economics, 51(2), pp. 379-400. China Statistical Yearbook, 2010. China Statistical Yearbook. [online] Available at: [Accessed on 9 February 2015]. China Statistical Yearbook, 2011. China Statistical Yearbook. [online] Available at: [Accessed on 9 February 2015]. China Statistical Yearbook, 2012. China Statistical Yearbook. 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