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Impacts of FDI on Employment in China
Introduction
Foreign Direct Investment (FDI) has been defined by different scholars, with the common definition referring to an investment where a firm gets acquisition and control over another foreign firm or such a firm set up its subsidiary in another foreign country. Taking many different forms, such investments could include mergers and acquisitions, intercompany loaning facilities, reinvestment of profits in foreign countries and development of new facilities overseas.
A clear distinction is drawn between FDI and portfolio investment, which involves the investments in the security of another country, either equity or debt securities (Sornarajah, 2011).
Due to the rapid changes resulting from globalization, better opportunities arise in the FDI arena. Foreign investments have flowed to different countries and had great impact on these countries’ economy. Developing countries, for instance, have endeavored to set policies that are competent and able to attract foreign investors. China, in its developing stages, managed to conceptualize the Reform and Opening Policy as early as 1978, a move that started revolutionary policy guidance for Foreign Direct Investment in China (Hale & Long, 2011, p. 16).
Since its beginning, FDI in China has undergone rapid developments. Within 1979 and 1986, a total amount of about $8.304 Billion was transacted as a result of FDI with the main players being Taiwan, Hong Kong and Macao (Chen, 2011, p. 93). This good trend was distorted from 1987 through 1991, when China’s legal system was unsound and incapable of attracting foreign investments
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Mainly referred to as the Rapid Development Stage, happened between 1992 and 1997 after China embarked on their socialist market economic system, hence improving tremendously the opportunities for investments. During this time, China’s FDI hit the highest at $196.7 Billion.
Though the following years witnessed a dwindling trend in FDI, this changed in 2001 to the present, due to China’s involvement in the World Trade Organization and its conducive environment that attracts investments internationally. Mostly, the main sectors which China concentrated on to stabilize their FDI included technology and telecommunications, banking, retail and wholesale growth.
Other than this, China promulgated new government policies that were business friendly. By the year 2011, the country had invested in over 400,000 enterprises that were internationally funded (Deng, 2013, p. 213). Apart from the inflow on FDI, there was massive effect of such investment to the indigenous firms in China. Such effects are referred to as spillover effects, which are usually divided into monetary and demonstration effects.
Due to their technological advancements, multi-national firms are competent compared to the local companies hence giving excessive competition grounds. As a result, local companies seek better managerial skills, technological equipment and production efficiency to meet the standards of the multinational companies (Zhang, et. al., 2016, p. 180). Despite being advantageous, this kind of competition between firms can be detrimental on the local firms, where multinational companies using technological advancements and productivity snatch market shares from local firms.
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There are various forms of FDI in China, including; equity joint ventures, wholly foreign-owned enterprise, joint exploration, FDI shareholding and contractual joint ventures. As its name may suggest, equity joint ventures are owned jointly by foreign and Chinese companies, individuals or other governmental organizations. Both companies manage the company together, hence sharing profits and risks together on determined scales as per capital contributions.
Contractual joint ventures, on the other hand, are somewhat similar to the equity joint venture, only that obligations and duties arising on the parties are laid off in a contract. Wholly foreign owned refer to foreign companies, individual and enterprise investments who establish themselves in China. In this scenario, all capital derives from such foreign firms. FDI shareholding involves the purchase of equity by foreign investors, hence leading to foreign invested enterprise.
Joint explorations, on the other hand, refer to various economic cooperation on the international arena, usually divided into exploration, exploitation then production. In many instances, joint explorations venture into exploitation of natural resources (OECD, 2013, p. 53).
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As earlier mentioned, FDI has a spillover effect. Particularly, FDI ensures creation of jobs, explained through the greenfield and brownfield analogies. Greenfield analogy can be explained as an investment that creates new production lines in host countries, through starting of a new company. Brownfield investment, in contrast, involves overseas mergers and acquisition. Due to its nature, brownfield investments cannot be certainly denoted as job openers, considering no new companies are created.
Another effect of FDI experienced in China is the crowding-effect, considering many multinationals are investing in the country. Local firms are overly pressurized to exhibit good performance, or risk winding up. This leads to severe pressure on employees. The inter-dependency between FDI and employment is usually affected by diverse variables, including population, exports and growth of domestic economy (Michael, 2013, p. 24).
Literature review
In the recent years, China has been trying to support the foreign direct investment to enhance its purchasing power via wages and to create job opportunities. Through understanding factors that impacts on employment, particularly those associated with FDI, China can realize its potential expansion of its productive sector and the required production innovation techniques to improve its economy.
It is because FDI can create jobs through the direct hiring of individuals for the new industries. Moreover, the enhanced aggregate domestic employment via various types of jobs created, income distribution, wages levels, and skills transfer will result in indirect effects. The increased FDI inflow to China has led to the creation of many job opportunities, and as a result, many people have been employed (Hu, 2011).
Therefore, FDI has positively impacted on employment in the long-term since individuals who could have been unemployed, now can have jobs. However, since FDI bring new business culture and technology, its influence relies on the interaction between the growth of the productivity, labor specialization, and output growth.
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FDI has led to the improvement of technology, skills, and trade in the long-term in China. Moreover, it has facilitated adverse effects on jobs and wages as realized in China in the short-term. The findings in China in both secondary and tertiary firms for the period 1985-2008 indicated that FDI growth led to the creation of employment, enhanced skills and technology, and trade for the period.
FDI needed high-skilled personnel to work in their organizations that had sophisticated technologies, hence, necessitated an individual to acquire skills that matched FDI requirements, making one to have improved skills in the end. However, in situations where there was a bidirectional linkage between employment and FDI, in the short-term, FDI led to the loss of jobs because of displacements of workers, according to Liu (2012).
Furthermore, on one hand, new technology made industries more competitive that allowed them to employ more employees and to grow. On the other hand, new technology led to decrease in demand because of substitution of many low-skilled workers by fewer high skilled employees. Therefore, new technology had both merits and demerits attributed to job creation and employment.
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Recent empirical evidence studies show that China should not expect to have any job opportunities despite the benefits she gets from FDI (Zia & Rizvi,2011). It is because elasticity growth associated with employment in China is extremely low, which makes employment enhancing policies be priorities. Initially, when foreign investors and their companies came, many people were employed, but over time the rate of absorption became low. T
he new companies were able to attain the required number of employees in their organizations with time, meaning new people could not be employed leading to low elasticity growth associated jobs.
When China is studied using the two-sector dual economy model to show the influence of foreign investment on domestic capital accumulation and underemployment, it shows that foreign investment lower manufacturing sectors in the long-term. The manufacturing sector decline because some of the local companies were not able to compete adequately with foreign organizations associated with FDI as they had a lower level of technology and skills.
FDI also had a large effect in the high-wage manufacturing firms than on a one-for-one basis and crowds out domestic capital. The study of FDI effect using analysis of panel information to find labor demands roles for white and blue collar employees showed that FDI had significantly positive outcomes. However, the positive effect, especially with the blue-collar jobs, declined with the rise of the skilled intensity of manufacturing companies (Liu, 2012).
According to Duan (2011), labor market, market size and market potential, clustering and cluster, macroeconomic policies, openness, and scientific research level account for the reason of determining the FDI location. Labor productivity and labor costs also influence FDI location, which indicates that improved workforce skills level attracts FDI. Thus, FDI favors high-skill workers because they are the ones mostly likely to get employed in the new job markets, and makes low-skill workers liable to lose their employments due to replacement.
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Ownership that is so crucial in the creation of the jobs affects employment in China. Research indicates that the major reason for inadequate employment creation is because the state own enterprises absorb employees quickly than the private sector. The low absorption rate is attributed to the fact that both joint ventures and foreign-owned multinationals that are supposed to employ many people belong to the private sector.
Thus, it means that the private sector has a higher capacity of creating more employment opportunities when compared to the state-owned enterprises (Sjoholm, 2011). In a similar analysis of employment, Hale & Long (2012) found out that FDI indirectly and directly impacts jobs. According to them, FDI can directly increase jobs and indirectly lower jobs by improving productivity levels indirectly and supplanting domestic investment. However, when the effects of the two are combined, FDI has significant positive influence in China.
Liu (2012) analyzed the effect FDI has on employment creation in China as far as manufacturing companies are concerned. Liu relied on the industry-level data in the Chinese manufacturing industry for the period 2000-2009. Also, Liu presented an analysis of direct and indirect job impacts. The findings indicated that both the private domestic industries and FDI have higher employment growth than the non-private domestic companies in China.
Furthermore, firms with other types of ownerships had less advantageous features than the FDI, in particular, their access to the export market, when the cross-ownership comparison is done. The conclusion was that FDI had led to employment creation in the Chinese manufacturing sector.
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The impact of FDI on employment may not be beneficial to China. Hu (2011) illustrates that when crowding-out is taken into consideration, the crowding-out only becomes significant when foreign multinational enterprises focus on the recipient nation’ market. It is because the FDI influx may bring in more pressure on domestic firms. Moreover, the external investment requires higher efficiency and better technology, which implies that it will only need fewer employees than before, making the crowding-out effect of FDI lead to more workers being laid off as a result of more of the domestic companies going bankrupt.
Zia & Rizvi (2011) indicate that FDI has more favorable when China faces economic crises. It is because FDI has an advantage over other investments programs such as loans or portfolio as it often prove to be more resilient in times of economic crisis. The other types of investment are subject to large reversal when there is a financial crisis. Thus, economic crisis presents a major challenge to employees and employment.
Workers who are employed in other types of investment are more likely to be laid off because their organizations may go bankrupt, which is unlikely of FDI that is more resilient and stable in an economic crisis. In this scenario, FDI positively impacts on employment in China because workers are not likely to lose their jobs due to the economic crisis.
FDI has also led to the loss of employment among people in China, according to Zia & Rizvi (2011). The increased competition associated with FDI’s international corporations has pushed out some of the more productive local business enterprises as they are not able to compete. It is because the local business enterprises have lower technology and skills in most cases than the FDI’s companies making them less favorable to compete in the market.
Therefore, the increased competition brought in FDI has led to the loss of jobs, rather than creating. Moreover, it illustrates that FDI does not contribute to local economy development because the increased competition associated with FDI leads to people being laid off in local business ventures.
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The amount of FDI in China increased by 225.20 USD HML between December and February 2016. This is according to data the ministry of commerce of the people’s republic of china. The country averaged 416.01 USD HML between 1997 and 2016 hitting an ultimate high of 1262.7 HML in December 2015.
Labor is affected by a variety of factor in an economy both from either side of the border of the economic space of a country or an economy. Empirical research has given much more attention to the effects of trade on labor markets than to the impacts of FDI on employments. Analysis on the effect of FDI on employment is thus more complicated.
A large number of studies have been conducted that try to establish whether OFDI substitutes or complements domestic jobs and this is split into two. In the home employment effect of foreign direct investment: from empirical results, China’s OFDI contribution to the employment of the country is a noticeable difference in the studies conducted over time. It was found that FDI can stimulate exports thus, in turn, achieving more employment.
These multinationals, in the process of processing trade of foreign investments, source most of their materials from the domestic markets. This are raw materials, spare parts and other half finished products. This increases the demand for these goods in the domestic market hence raising the employment in the different industries producing them and those related to them.
However, with the surplus of china’s labor being serious and FDI still being at a start stage, many investments belong to the defensive industry. These investments cause an increase in the demand of domestic capital and goods thus edging out domestic investments from the market.
Research also shows that china’s FDI does not influence employment in the primary industries but gives a significant effect in secondary and tertiary industries. With the composition of capital in the tertiary industries being comparatively small, labor is higher compared to other industries at similar investment levels thus FDI achieving more influence in tertiary sectors.
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Research done on the effect of FDI on the home country have elicited mixed results. Research dealing in detail with the employment effect of FDI found a substitution effect between a foreign subsidiary’s activity and its parent’s employment. These studies have majorly concluded that change mainly occurs between countries with comparable endowments. This thus implies that low-wage countries make better substitute among themselves than.
Studies indicate that American multinationals are employing vertical FDI seem to be reducing employment back home compared to production by transferring labor intensive stages of their production processes to their affiliates in developing countries. Other studies have concluded that labor substitution is more likely to take place when factor proportions are different in various locations and vertical FDI prevails. The second group of research has found that the complementary effect prevails, this noted a positive effect on employment due to an affiliate activity in the host country.
The main reason behind this is that the opportunity to invest in a low-cost host country could increase the firm’s competitiveness, promote its use of economies of scale, and reduce its costs, which may lead to an increase in home-country employment.
This brings the picture of a scale effect dominating over a substitution effect for the parent country’s firms and the parent country’s employment. In the North American car industry, studies have found that jobs in Japan were growing as a consequence of investing abroad. This is explained as the result of allocating labor intensive production to developing countries thus increasing supervisory and ancillary employment to mainly service foreign operations.
According to Hu (2011), the two factors affecting employment are economic development and capital stock, with capital stock encompassing both domestic and foreign FDI.
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The third group of studies shows that outward investments can increase the demand and wages for skilled labor in both the parent and host countries. This is attributed to the differences in labor demand in both countries
Nunnenkamp, Bremont and Waldrich (2011) posed the question on whether foreign direct Investment contributed to employment creation in Mexico. An analysis of FDI and employment data covering manufacturing firms in Mexico were used. From this, they estimated the dynamic labor demand functions for blue and white collar workers, including both FDI and its interaction with major industry characteristics.
Using the GMM estimator proposed by Nunnenkamp, Bremont and Waldrich (2011) they accounted for the relatively short time dimension of the panel data (1994-2006). It showed that FDI had a positive though the quantitatively modest impact on manufacturing employment in Mexico. This was in contrast to a widely held view applying to both white collar and blue collar jobs.
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Hu (2011) found that it is very difficult to assess the outcome of FDI on employment in European nations mainly due to the different stages of economic development in the countries. According to him, the first stage of FDI is characterized by elimination unproductive jobs giving way to new productive jobs.
This is mainly due to the restructuring of jobs by extensive mechanization and automation leading to loss employment while the organisations became better and more profitable. From these, the multinationals create a better and more productive labor force. This process of creatively destroying labor ends up creating a more positive effect on employment. Finally, it is found that the research shows that FDI is not a golden wand to the creation of jobs.
Liu (2012) using data collected between 1986-2010, concludes that that the effect of FDI on employment was positive before 1996, but the effect was not noticeable after 1996.
According to established theory, the activities of affiliates can be related to the motives of FDI, namely efficiency seeking, market seeking and strategic-asset-seeking flows. The impact of these types of FDI on trade patterns are explained by distinguishing four kinds of trade linkages between the parent firm and her affiliates:
- The substitution of former exports through FDI
- Growing (re-)imports of goods and services produced abroad
- FDI associated exports of goods and services
- FDI induced exports of other product lines neither generated by the foreign affiliates nor exported earlier by the parent
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The overall impact of FDI on domestic employment is the sum of negative (export substitution, re-imports) and positive effects (associated and induced exports) and can be tested only empirically. Any distinction between direct and indirect FDI is justified only if their trade linkages differ. In a broad view, the literature reviewed shows that MNE (Multinational Enterprises) employment can promote growth and poverty reduction in host countries in four ways.
(i) Multinational Enterprises job has a direct and indirect impact on domestic employment: this is through direct employment and indirect employment through forward and backward linkages in the local firms.
(ii) Multinational Enterprises employment boosts wages in host countries:
A number of studies have shown that Multinational Enterprises pay higher wages than local firms even after controlling for firm and worker characteristics. The presence of multinationals will also at times cause wages to be higher in industries and in provinces that have a higher foreign direct investment
(iii) Multinational Enterprises employment fosters technological transfers:
Through labor turnover, technology gets diffuse into the host countries as domestic employees move from foreign firms to local companies.
(iv) Multinational enterprises employment enhances labor force productivity in host country:
Several studies have shown that workers in foreign-owned enterprises are more productive than workers in domestically owned enterprises.
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Methods
Time series and AUTOREG process in SAS were used to access historical association between the inflows of FDI and employment in China. Since, dependent and independent variables are time series data, model error has a significant probability of not being independent based on time The AUTOREG procedure measures and predicts linear regression for time series in the event that errors are Autocorrelated.
Dependent and independent Variables
As indicated early, FDI has led to crowding-out influence on employment, as such an essential indicator of job opportunities. Besides FDI, various variables may impact employment including GDP, interest rates and wages. Some of the components of GDP include government expenditure, consumption, value of net exports and investment (Mankiw, 2012). The thesis uses China’s Statistical Yearbook that has FDI as an investment element.
For that reason, the assessment was performed using GDP values from this Statistical yearbook and provided values of GDP subtracted with FDI. The results were then utilized in testing the association between GDP and employment. For easier understanding, model outcomes of GDP are obtained from Statistical Yearbook of China. The estimates of the model are similar, with same independent variables under the requirements of alternative model. The model outcome of GDP with no adjusted FDI is demonstrated in analysis section.
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Moreover, since China’s FDI information from World Bank beginning in 2005 demonstrate considerably amounts in comparison to Statistical Yearbook, however, employment and GDP are similar in these sources, models were evaluated using FDI and other variables from World Bank and Statistical Yearbook respectively. The results are demonstrated in table 1.
Table 1: Chinese National Economy Data
| Year | Emplo yment (100mi llion) | FDI | FDI ˄World Bank˅ | GDP | GDP- FDI | total wage | intere st rate for depos its | intere st rate for loans | Exchan ge rate |
| 1985 | 4.9873 | 19.56 | 16.59 | 2943.0 7 | 2923.5 1 | 430.9 1 | 8.28 | 7.92 | 3.2095 |
| 1986 | 5.1282 | 22.44 | 18.75 | 2816.2 3 | 2793.7 9 | 444.7 9 | 9.36 | 7.92 | 3.7314 |
| 1987 | 5.2783 | 23.14 | 23.14 | 3290.2 9 | 3267.1 5 | 504.1 3 | 9.36 | 7.92 | 3.7314 |
| 1988 | 5.4334 | 31.94 | 31.94 | 4124.0 9 | 4092.1 5 | 620.7 3 | 10.80 | 13.32 | 3.7314 |
| 1989 | 5.5329 | 33.92 | 33.93 | 4113.1 1 | 4079.1 9 | 622.1 5 | 14.94 | 19.26 | 4.2088 |
| 1990 | 6.4749 | 34.87 | 34.87 | 3695.7 1 | 3660.8 4 | 563.7 0 | 11.52 | 11.16 | 5.2352 |
| 1991 | 6.5491 | 43.66 | 43.66 | 4162.9 6 | 4119.3 | 612.8 8 | 9.00 | 9.72 | 5.4234 |
| 1992 | 6.6152 | 110.0 8 | 111.56 | 4739.0 5 | 4628.9 7 | 677.2 3 | 9.00 | 9.72 | 5.8166 |
| 1993 | 6.6808 | 275.1 5 | 275.15 | 6345.6 6 | 6070.5 1 | 844.5 6 | 12.06 | 12.24 | 5.8210 |
| 1994 | 6.7455 | 337.6 7 | 337.87 | 5906.2 7 | 5568.6 | 782.8 8 | 13.86 | 14.04 | 8.5024 |
| 1995 | 6.8065 | 375.2 1 | 358.492 | 7584.4 2 | 7209.2 1 | 966.4 9 | 13.86 | 14.04 | 8.3351 |
| 1996 | 6.8950 | 417.2 6 | 401.8 | 8904.2 7 | 8487.0 1 | 1076. 29 | 12.06 | 15.12 | 8.3290 |
| 1997 | 6.9820 | 452.5 7 | 442.37 | 9874.0 6 | 9421.4 9 | 1161. 11 | 6.66 | 10.53 | 8.2700 |
| 1998 | 7.0637 | 454.6 3 | 437.51 | 10463. 32 | 10008. 69 | 1228. 04 | 6.66 | 8.01 | 8.2700 |
| 1999 | 7.1394 | 403.1 9 | 387.53 | 11018. 74 | 10615. 55 | 1324. 66 | 2.88 | 6.21 | 8.2700 |
| 2000 | 7.2085 | 407.1 5 | 383.993 | 11940. 63 | 11533. 48 | 1324. 63 | 2.88 | 6.21 | 8.2700 |
| 2001 | 7.3025 | 468.7 8 | 442.41 | 13183. 56 | 12714. 78 | 1475. 86 | 2.88 | 6.21 | 8.2700 |
| 2002 | 7.3740 | 527.4 3 | 493.079 766 | 14567. 79 | 14040. 36 | 1649. 11 | 2.79 | 5.76 | 8.2700 |
| 2003 | 7.4432 | 535.0 5 | 494.568 471 | 16519. 16 | 15984. 11 | 1853. 64 | 2.79 | 5.76 | 8.2700 |
| 2004 | 7.5220 | 606.3 0 | 621.080 43 | 19462. 7 | 18856. 4 | 2129. 99 | 3.60 | 6.12 | 8.2700 |
| 2005 | 7.5825 | 603.2 5 | 1041.08 694 | 23208. 33 | 22605. 08 | 2554. 22 | 3.60 | 6.12 | 8.0757 |
| 2006 | 7.4978 | 630.2 1 | 1240.82 036 | 28471. 13 | 27840. 92 | 3101. 64 | 4.14 | 6.39 | 7.8224 |
| 2007 | 7.5321 | 747.6 8 | 1562.49 335 | 36166. 7 | 35419. 02 | 3998. 09 | 4.41 | 6.39 | 7.3714 |
| 2008 | 7.5564 | 923.9 5 | 1715.34 65 | 46083. 95 | 45160 | 5147. 09 | 5.58 | 7.47 | 6.8565 |
| 2009 | 7.5828 | 900.3 3 | 1310.57 053 | 51082. 38 | 50182. 05 | 5900. 70 | 5.58 | 7.47 | 6.8227 |
| 2010 | 7.6105 | 1057. 35 | 2437.03 435 | 60602. 15 | 59544. 8 | 7111. 57 | 4.20 | 6.14 | 6.6469 |
| 2011 | 7.6420 | 1160. 11 | 2800.72 219 | 73453. 39 | 72293. 28 | 9455. 83 | 5.00 | 6.60 | 6.3405 |
Table 2 Chinese National Economy GDP Disaggregated Data
Unit (100 million US$)
| Year | Gross Domestic Product | |||
| household expenditure | government expenditure | Gross capital formation | Net export | |
| 1985 | 1460.48 | 404.70 | 1077.27 | 0.62 |
| 1986 | 1420.94 | 407.27 | 1056.41 | -68.39 |
| 1987 | 1641.77 | 449.83 | 1195.80 | 2.89 |
| 1988 | 2108.62 | 528.33 | 1527.63 | -40.49 |
| 1989 | 2093.85 | 558.73 | 1504.63 | -44.10 |
| 1990 | 1805.26 | 504.20 | 1288.78 | 97.47 |
| 1991 | 1978.57 | 619.78 | 1450.75 | 113.86 |
| 1992 | 2235.00 | 722.62 | 1734.05 | 47.38 |
| 1993 | 2819.46 | 942.76 | 2700.17 | -116.73 |
| 1994 | 2569.18 | 870.11 | 2392.40 | 74.58 |
| 1995 | 3403.64 | 1005.21 | 3055.76 | 119.81 |
| 1996 | 4076.83 | 1196.25 | 3455.99 | 175.20 |
| 1997 | 4464.51 | 1356.60 | 3623.70 | 429.25 |
| 1998 | 4743.57 | 1494.43 | 3786.48 | 438.84 |
| 1999 | 5068.97 | 1658.59 | 3984.46 | 306.72 |
| 2000 | 5544.69 | 1893.76 | 4213.16 | 289.02 |
| 2001 | 5977.74 | 2115.84 | 4808.88 | 281.10 |
| 2002 | 6415.55 | 2268.43 | 5509.67 | 374.14 |
| 2003 | 6970.96 | 2422.70 | 6766.99 | 358.51 |
| 2004 | 7886.15 | 2700.62 | 8363.77 | 512.16 |
| 2005 | 9034.35 | 3268.92 | 9640.88 | 1264.18 |
| 2006 | 10556.29 | 3902.69 | 11883.06 | 2129.09 |
| 2007 | 13068.41 | 4870.23 | 15050.50 | 3177.56 |
| 2008 | 16286.79 | 6089.42 | 20174.33 | 3533.41 |
| 2009 | 18100.47 | 6691.89 | 24087.65 | 2202.37 |
| 2010 | 21176.58 | 8027.25 | 29126.95 | 2271.37 |
| 2011 | 26014.54 | 10033.29 | 35487.21 | 1918.35 |
Data Source: China statistical yearbook.
Table 3 Chinese Economy Primary Sector Source Data
Unit (100 million US$)
| Year | Employment (100 million) | FDI | GDP | GDP-FDI | Total Wage |
| 1997 | 3.484 | 6.2763 | 1746.3 | 1740.02 | 31.78 |
| 1998 | 3.5177 | 6.2375 | 1791.73 | 1785.49 | 30.22 |
| 1999 | 3.5768 | 7.1015 | 1786 | 1778.9 | 30.58 |
| 2000 | 3.6043 | 6.7594 | 1807.1 | 1800.34 | 31.45 |
| 2001 | 3.6513 | 8.9873 | 1908.26 | 1899.27 | 32.43 |
| 2002 | 3.687 | 10.2764 | 1999.64 | 1989.36 | 33.63 |
| 2003 | 3.6546 | 10.0084 | 2101.78 | 2091.77 | 40.6 |
| 2004 | 3.5269 | 11.1434 | 2589.21 | 2578.07 | 42.46 |
| 2005 | 3.397 | 7.1826 | 2776.23 | 2769.05 | 45.65 |
| 2006 | 3.1941 | 5.9945 | 3073.23 | 3067.24 | 51.56 |
| 2007 | 3.0731 | 9.2407 | 3883.52 | 3874.28 | 63.03 |
| 2008 | 2.9923 | 11.9102 | 4915.34 | 4903.43 | 75.32 |
| 2009 | 2.889 | 14.2873 | 5159.28 | 5144.99 | 78.71 |
| 2010 | 2.7931 | 19.1195 | 6098.12 | 6079 | 94.34 |
| 2011 | 2.6594 | 20.0888 | 7489.35 | 7469.26 | 110.03 |
Data Source: China statistical yearbook.
Table 4 Chinese Economy Secondary Sector Data
Unit (100
millio n US$)
| Year | Employment (100 million) | FDI | GDP | GDP-FDI | Total Wage |
| 1997 | 1.6547 | 325.6989 | 4539.66 | 4213.96 | 556.41 |
| 1998 | 1.66 | 313.2749 | 4716.35 | 4403.08 | 514.97 |
| 1999 | 1.6421 | 277.8432 | 4961.74 | 4683.9 | 521.02 |
| 2000 | 1.6219 | 295.798 | 5508.57 | 5212.77 | 546.18 |
| 2001 | 1.6284 | 348.0844 | 5986.98 | 5638.9 | 575.7 |
| 2002 | 1.578 | 394.7185 | 6517.14 | 6122.42 | 627.26 |
| 2003 | 1.6077 | 391.9696 | 7549.94 | 7157.97 | 719.88 |
| 2004 | 1.692 | 454.6306 | 8936.43 | 8481.8 | 831.48 |
| 2005 | 1.8084 | 446.9243 | 10847.12 | 10400.2 | 1009.74 |
| 2006 | 1.8894 | 425.066 | 13259.3 | 12834.2 | 1248.39 |
| 2007 | 2.0186 | 428.6105 | 17070.21 | 16641.6 | 1587.29 |
| 2008 | 2.0553 | 532.5624 | 21731.71 | 21199.1 | 2018.55 |
| 2009 | 2.108 | 500.7582 | 23088.12 | 22587.4 | 2272.02 |
| 2010 | 2.1842 | 538.6037 | 28191.07 | 27652.5 | 2789.19 |
| 2011 | 2.2544 | 557.487 | 34762.69 | 34205.2 | 4030.86 |
Data Source: China statistical yearbook.
Table 5 Chinese Economy Tertiary Industry Data
| Year | Employment (100 million) | FDI | GDP | GDP-FDI | Total Wage |
| 1997 | 1.8432 | 120.5952 | 3263.38 | 3142.78 | 549.09 |
| 1998 | 1.886 | 135.1151 | 3697.76 | 3562.64 | 578.94 |
| 1999 | 1.9205 | 118.2424 | 4095.94 | 3977.7 | 642.53 |
| 2000 | 1.9823 | 104.5907 | 4681.25 | 4576.66 | 710.91 |
| 2001 | 2.0228 | 111.7042 | 5361.16 | 5249.46 | 822.43 |
| 2002 | 2.109 | 122.4337 | 6033.72 | 5911.29 | 930.53 |
| 2003 | 2.1809 | 133.0687 | 6772.03 | 6638.96 | 1093.16 |
| 2004 | 2.3011 | 140.5258 | 7806.69 | 7666.16 | 1256.05 |
| 2005 | 2.3771 | 149.14 | 9277.12 | 9127.98 | 1498.82 |
| 2006 | 2.4143 | 199.0819 | 11320.68 | 11121.6 | 1801.7 |
| 2007 | 2.4404 | 309.8277 | 15105.94 | 14796.1 | 2347.77 |
| 2008 | 2.5087 | 379.4812 | 19155.54 | 18776.1 | 3053 |
| 2009 | 2.5857 | 385.2817 | 21681.98 | 21296.7 | 3549.97 |
| 2010 | 2.6332 | 499.6292 | 26116.83 | 25617.2 | 4228.04 |
| 2011 | 2.7282 | 582.5342 | 32329.08 | 31746.5 | 5314.93 |
| Data Source: China statistical yearbook. |
Unit (100 million US$)
Model outcome of industry with respect to GDP without FDI are indicated in table 6
Table 6: Chinese National Economy Normal Least Squares Results
The AUTOREG Procedure
| SSE | 2.74028586 | DFE | 18 |
| MSE | 0.15224 | Root MSE | 0.39018 |
| SBC | 44.5152978 | AIC | 32.852766 |
| MAE | 0.22047964 | AICC | 43.4410013 |
| MAPE | 3.56510991 | HQC | 36.3206481 |
| Durbin-Watson | 0.7275 | Regress R-Square | 0.8492 |
| Total R-Square | 0.8492 |
| Variable | DF | Estimate | Standard Error | t Value | Approx Pr > |t| |
| Intercept | 1 | 5.4839 | 0.8842 | 6.20 | <.0001 |
| FDI | 1 | 0.003170 | 0.001355 | 2.34 | 0.0310 |
| household | 1 | -0.000139 | 0.000722 | -0.19 | 0.8497 |
| government | 1 | 0.001427 | 0.001350 | 1.06 | 0.3044 |
| GCF | 1 | -0.000182 | 0.000230 | -0.79 | 0.4394 |
| export | 1 | -0.000146 | 0.000220 | -0.67 | 0.5144 |
| wage | 1 | -0.000588 | 0.000951 | -0.62 | 0.5443 |
| deposit | 1 | 0.006644 | 0.1214 | 0.05 | 0.9569 |
| loan | 1 | 0.0137 | 0.0893 | 0.15 | 0.8795 |
Additionally, wages influence employment. A number of studies have assessed the connection between wages and employment. Wages cannot affect employment, in other words, reducing real wages in not useful to increase job opportunities. On the contrary increased job opportunities do not affect wages. When job opportunities increase, it implies that increased demand while reducing real wages. Interest rates also affect employment.
For instance, a decrease in interest rate on deposit means that individuals will deposit less hence promote consumption in households while promoting production and recruitment as the market will require additional employees. In contrast, when there is a reduction of interest rate on loans, producers will borrow from banks at reduced costs thus assist in expanding production and a nation will need extra employees.
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Sources of data
In this study, to assess the connection between FDI and employment in China, eight independent variables were used including wages, FDI, government expenditure, consumption, net exports, investment, interest rates for loans and deposit from 1985 to 2011.
In china, the Reform and Opening Policy was introduced in 1978, a period when FDI started to flow. Nonetheless, as a result of inadequate information on FDI, interest rate for deposit and loans, wages while ensuring that independent variable , data was collected from similar source as well as period- 1985to 2011.When it comes to statistical analysis, three major industries in the economy of China, information on four elements of GDP and interest rates for loans and deposit was not available.
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Additionally, in the analysis of three major sectors; GDP, FDI and wages were used as independent variables. Information on industry analysis was available for 1997 to 201. And for national economy and industry analysis dependent variable was similar – number of employed individuals, which was represented as 100 million employment opportunities.
Consequently, original Chinese information source FDI units, household spending, net exports, wages, government spending and gross capital are represented by 100 million US dollars. On the other hand, household spending, net exports, wages, government spending and gross capital are represented based on Chinese currency RMB. To ensure that there is uniformity in the information, exchange rate for RMB to USD from 1985 to 2011 was employed to convert RMB to 100 million USD. The interest rates units are expressed as a percentage.
Owing to the fact that each industry has various units/sectors, the primary, secondary and tertiary data values are the totals of every sector in each industry. The primary sector comprises of forestry, agriculture, fishing and animal husbandry while the secondary industry involves manufacturing, mining, supply of water, gas, and waters. Addition, tertiary industry represents other sectors not in the primary and secondary industries.
Some of these sectors are storage, transport, information dissemination, hotels and catering; realtor, scientific research and so forth (China Statistical Yearbook).For that reason, the useful data for primary, secondary and tertiary sectors, and FDI information and wages were estimated. GDP information is collected from China’s yearbook. Information for the China’s economy is illustrated in Table 1 and Table 2. Table 3 presents primary sector data and Table 4 and Table 5 represents secondary and tertiary sectors for the economy of china respectively.
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Results
Data analysis was performed using AUTOREG procedure to demonstrate the connection between independent and dependent variables of employment. The modeling approach changes due to inadequate interdependence of data values as well as estimation error through modeling errors as lag-one autoregressive, or AR(1), framework. According to the model errors in the analysis, it demonstrates cases of moderate level of skewedness for particular model while approximating normality in certain cases.
Much as heteroskedasticity of errors is not directly tackled, AUTOREG process id developed to deal with such issues; based on the fact that there is insufficient volatility in the information to assess the models.
Findings for the national economy of China
Based on the findings from the overall economy of China, it is evident that independent variables including household spending, FDI, gross capital, government spending wages, net capital and interest rates of loans and deposits have a significant relationship with employment- the dependent variable. According to estimations from Ordinary Least Squares, 84.95 percent of changes in employment can be forecasted by independent variables (table 6).
In addition, from Maximum Likelihood that involves adjusted Autocorrelated errors, there were about 95.89 percent changes in employment, which can be estimated by eight predictor variables (Table 7). The association between every independent as well as dependent variable is illustrated in Tables 6 and 7. Since the information is focused on 27 year while standard errors are huge compared to large datasets, estimations demonstrate that particular p-values are more than 5%.
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Using, Ordinary Least Squares, it is clear that there is a strong correlation between FDI and employment- with a p-value of 0.031. There is no evident association between dependent variable and household spending, FDI, gross capital, government spending wages, net capital and interest rates of loans and deposits (Table 6 With respect to Maximum Likelihood that comprises of the impacts of autoregressive lag-1 framework there is a negative correlation between employment and interest rates loans, because the p-value is more than 5% at 0.0616.
While this is not a strong relationship, it indicates and provides areas fro further studies in future. Again, there is no correlation between employment and other independent variables (Table 7). First-order autoregressive or AR (1) model is utilized to address trends of high serial reliance within data. It is estimated at -0.9779 with a Pearson value of less than 0.0001. This suggests that independent errors associated with data for one time though closely associated. in other words, every year independent error is closer to the previous year’s error.
Based on Maximum Likelihood and Ordinary Least Squares, the association between independent and dependent variables are not similar. In the Ordinary Least Squares, there is a strong correlation between dependent and independent variables (Table 6), this is because the p-value is at 0.031 while estimate for parameter at 0.00317. On the other hand, for Maximum Likelihood, there is no significant correlation between employment and FDI; however, there is a negative association between employment and interest rates for loans (Table 7), since the Pearson value and parameter estimates at 0.0653 and -0.0653 respectively.
This variation is due to Maximum Likelihood put into account First-order autoregressive procedure and also the impact of independent variables that significantly affects parameter estimations of every predictor variable. This result indicates that putting into account autoregressive framework, the impact of FDI weakens while interest rates for loan demonstrate a significant influence on employment. Residual analysis is shown in Figure 6.
Residual analysis is important when it comes to measuring the variation between estimated and observed values for every year employment level. For employment- the individuals employed in China, every year’s residual is at -1 and 1 apart from 1988 to 1990. In other words, the observed and estimated values are closely, other than in 1988 t0 1990.
According to lag framework, the probability of white noise reduces as the lag time increases, when the lag period increase, it becomes challenging to forecast employment level. The autocorrelation function is a trend of autocorrelation in a given time series at several lags while the partial autocorrelation function is the trend of incomplete autocorrelation in any given time series at different lags
Findings for primary, secondary and tertiary sectors of the economy of China
In the primary sector it is evident that 3 independent variables including FDI, wages and GDP have a significant correlation with employment. With Ordinary Least Squares estimation, 97.36% of difference in the employment is described by these variables as demonstrated in Table 8while Maximum Likelihood represents 97.39 percent of difference in employment (Table 9).
Since the data used is fifteen year data, it is intricate to achieve a small Pearson value, which demonstrates a positive statistical association. With regards to Ordinary Least Squares, there is a strong connection between employment and FDI as the p-value is at 0.001. In addition, the Maximum Likelihood, there is a strong relationship between employment and FDI because the Pearson value is at 0.005.
When it comes to secondary industry, 3 independent variable such as GDP, FDI and wages have a strong correlation with employment. The Ordinary Least Squares, 96.63 percent and Maximum Likelihood estimations indicate that 97.91percent changes in employment level can be forecasted by wages, FDI and GDP (Table 4.5 and 4.6). Much as the Ordinary Least Squares value indicate that there is a strong connection between employment and FDI since the Pearson value is 0.0007, there is a strong negative association between employment and wages with a p-value of 00187 and -0.000276 parameter estimates.
Again, Maximum Likelihood demonstrates a strong positive association between FDI and employment since the p-value is less than 0.05 at 0.027 and parameter estimates at 0.0000461. While the there is no statistical significance between employment and wages, Maximum Likelihood is similar to Ordinary Least Squares Figure 8).
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In the tertiary industry, there is a strong correlation between 3 independent variables- wages, FDI and GDP. Ordinary Least Squares results shows 95.16 percent of difference in the employment level, which is due to these variables while Maximum Likelihood demonstrates a 98.27 percent of change in employment level, which can be described by wages, FDI and GDP.
In Ordinary Least Squares values, there is a negative association between FDI and employment, because the Pearson value is greater than 0.05 at 0.0251 and parameter value at -0.002095. There is a strong negative connection between wages and employment with Pvalue and parameter estimation at 0.0264 and -0.001326 respectively. However, there is a significant correlation between employment and GDP since Pearson value is at 0.0046 and parameter estimate at 0.00312.
Based on Maximum Likelihood outcome, the parameter estimations demonstrates a negative correlation between employment and FDI because the p value is more than 0.05 at 0.0251 and strong association between GDP and employment with a p value at 0.0604. The strong negative association between employment and wages in the Ordinary Least Squares results is not significant in Maximum Likelihood results (Figure 9).
To guarantee that these correlations are precise, further estimations were performed by subtracting FDI from GDP, rather than using GDP information from Chinese Statistical Yearbook to establish the reported association between employment and independent variables of primary, secondary and tertiary industries of China’s economy.
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These results are similar to the above results. Nonetheless, the new outcomes will assist in verifying the validity of previous relationships. Furthermore, FDI information collected from Chinese Statistical Yearbook were evaluated against those from World Bank to guarantee that past outcomes are in line with other sources of information. The results of primary industry demonstrate that with careful consideration of autoregressive system, the strong correlation between employment and FDI is still strong.
This confirms that without doubt FDI has a positive influence on employment in the primary industry. In the secondary industry, Ordinary Least Squares and Maximum Likelihood estimates demonstrate a similar correlation between independent and dependent variables, therefore, GDP as well as wages affect level of employment, where GDP has a strong relationship while wages has a negative correlation.
In the tertiary industry, the negative association between wages and level of employment is not statistically significant in Maximum Likelihood; GDP has a strong correlation on the employment in Ordinary Least squares; and GDP is closely a strong independent variable of employment in Maximum Likelihood. Apparently, FDI has a negative impact on employment in the tertiary industry. For the general economy of China, there is no strong correlation between FDI and employment; and there exists a strong negative association between interest rates on loan and employment.
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