Economics the international effects of china’s growth, trade and education booms by

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Richard G. Harris

Simon Fraser University
Peter E. Robertson

Business School

The University of Western Australia
Jessica Y. Xu

The University of New South Wales


Richard G. Harris

Simon Fraser University
Peter E. Robertson*

Business School

The University of Western Australia
Jessica Y. Xu

The University of New South Wales

January 2010

China’s international trade flows have increased by 500% since 1992, far outstripping GDP growth. Likewise tertiary education enrollments have increased by 300%. We simulate these changes using a multi-sector growth model of the Chinese and USA economies. A decade of trade biased growth in China is found to have a large effect on the USA economy – raising GDP approximately 3-4.5 percentage points. We also show that the trade bias in China’s growth accounts for more than half of the observed growth in tertiary enrolments in China. In contrast neutral growth has practically no effect on USA incomes or China’s stock of skilled labour. Finally the simulations reveal that China’s education boom per se has practically no long run impact on the USA economy. The results thus indicate that the pattern of productivity growth in exports sectors, as might be caused by falling trade costs, has been critical in transmitting benefits of Chinese growth to the world economy. They also point to an important link between falling trade costs and human capital formation.

Keywords: Economic Growth, China, Human Capital, Trade Costs.
JEL: O4, O1, F11, F43
1. Introduction

What is the impact of China’s growth on the world economy? In thinking about answers to this question two facts stand out. First, China’s growth has been extremely biased. In particular, China’s international trade flows have increased by 500% since 1992, far outstripping GDP growth, and have changed dramatically in their composition (Amiti and Freund 2008). This is noteworthy since standard trade theory indicates the importance of biased growth as source of terms-of-trade gains to other countries.1 Second as documented by Li et al. (2008), over the last decade China’s investment in human capital has undergone a massive boom. The fraction of the labour force with tertiary degrees has doubled since 1992 and tertiary enrolments have increased by 300% over a similar period. Thus along with China’s trade shares, China’s endowment structure is also changing rapidly and potentially this may also affect have consequences for the intrenational economy.

Aside from trade and wage inequality literature, however, little is known about the effects of Chinese growth on other countries. For example, we know very little about how China’s growth affected factor accumulation and economic growth in other countries, or about how its chnaging trade pattrens and endowmnets hare affecting its trading partners?2 Likewise little is known about how these aspects of China’s growth are related. In particular, how is China’s growth and export boom related to its education boom? Can the education boom explain the changes in trade, or can falling trade costs and changing pattern of trade account for the education boom. The aim of this paper therefore is undertake a quantitative assessment of the broad stylized facts regarding China’s growth, trade bias and education boom, focusing in particular on their impacts on the USA.

To do this we construct a model of the Chinese and USA economies. The model incorporates both optimizing physical and human capital accumulation decisions and multiple traded and non-traded sectors. The model is then solved with endogenous productivity parameters, to reproduce the stylized facts of China’s growth and trade bias. The simulations show, first, that trade biased productivity growth, or falling trade costs, accounts for: 50-70% of China’s overall productivity growth. That is 50-70% of China’s overall productivity originates in export sectors. Second they show more than half of the observed increase in tertiary enrolments, is explained by the sector biased productivity growth. Third they show that China’s trade bias has a large impact on the USA: raising GDP per capita by 3.5–4% over a decade. Conversely, however, the results also show that China’s education boom, and its implied long-run 85 percent expansion in skilled labour stocks, has practically no effect on the USA. Likewise counterfactual simulations show that if China’s growth was neutral, there would practically no impact on the USA.

The remainder paper is organized as follows. Section 2 describes the model structure. Section 3 establishes some stylized facts regarding China’s growth patterns and presents a brief review on China’s growth and trade history over the last decade. Section 4 provides an overview of China’s higher education situation and reforms to expand its enrolments in higher education level. Section 5 discusses the experiment design and the results are reported in Section 6 and 7. Section 8 concludes by summarizing the main findings.

2. Methodology

Calibrated neo-classical growth models have been widely used to look at long run development issues. Examples include Parente, Rogerson and Wright (2000), Hansen and Prescott (2002), Graham and Temple (2006), and Hayashi and Prescott (2008). In this literature, however, the models are constrained to one or two sectors and to closed economy settings. This necessarily restricts the role of trade and any potential trade and growth interactions, which is a significant limitation in attempting to understand China’s growth.

To allow for trade–growth interactions we introduce long run neoclassical steady state factor accumulation conditions into an open economy C.G.E model. The model includes eleven sectors (6 traded and 5 non-traded) and three separate regions (China, USA and Rest of World). The focus of the model is to see how commodity price changes can affect factor prices - the Stolper-Sameulson effects - and how these in turn affect capital and human capital accumulation decisions in each country. Both regions are modeled as small open economies with respect to the Rest of the World (ROW), but not with respect to each other. Thus growth in China, for example, will have an impact on prices in the USA. The appendix also briefly describes the model used in this paper in a non technical fashion, focusing on the features of the model that most assist in understanding the results.3

The model is employed in the following way. We begin by specifying some important stylized facts regarding China’s growth. We then then solve the model with endogenously chosen technology parameters so that these stylized facts are reproduced exactly by the model solution. Thus we first ask, what must be assumed about neutral technical change; trade sector biased technical change, and government funding of education education – to reproduce different aspects of China’s growth experience?

Having reproduced the growth pattern, the model simulations then tell us what the impact of this biased pattern of growth has been on the main endogenous variables of interest, particularly wages, sectoral outputs and income levels in the USA, and also education enrollments and human capital accumulation in China. We can obtain a quantitative measure of the effects of trade bias for example, by considering both sector neutral productivity growth that reproduces China’s GDP growth, with a combination of neutral and trade-sector specific productivity growth that reproduces not only China’s GDP growth, but also the pattern of export growth. As discussed below, the results from these comparisons of alternative simulations, point to the enormous impact of trade biased growth - falling trade costs - for the the USA and also for raising education levels in China.

3. Growth in China - Some Stylized facts

As noted above, China has not only grown very rapidly, but the traded good sector has far outstripped growth in the rest of the economy. Moreover, the endowment structure has changed, as has the composition of trade. In this section, we briefly review the data and describe some broad stylized facts regarding the average rate of growth, the changes in trade shares of GDP and the changes in China’s export composition. Specifically over this period we show that China has experienced: a growth rate of GDP per capita of 8.9 percent per year; a 59 percent increase in exports to GDP ratio; and an 85 percent increase in higher education investments, as measured by enrollments. In addition we also consider changes in commodity export shares and tariff reductions over the last decade.

3.1 Post Cultural Revolution Growth Rate

The measurement of China’s economic growth has not been without controversy. Ruoen (1995) and Woo (1998) argue that the official GDP deflators are biased and tend to understate inflation. Using an alternative price deflator series and adjusting alternative labour market participation data, Young (2003) finds that China’s growth rate over the reform period 1978-98 is reduced significantly from official figures.4

However, the alternative data for the latest decade of China’s growth, 1995-2005, appear to be more consistent. Table 1, taken from unpublished data used in Bosworth and Collins (2008), compares different estimates of average growth rates in China. As shown in Column 2, the official growth rate of 8.05% per year falls to 6.71% per year using the price deflator series preferred by Young (2003).5 Bosworth and Collins' (2008) preferred estimates are given in Column 3 which uses the alternative price deflators for the industrial sector but the official deflators for agriculture and services. It can be seen that the differences in these series have declined in recent years. In what follows, we shall assume a growth rate of GDP per worker of 8.9% per year over the decade 1995-2005, based on Bosworth and Collins' (2008) preferred estimate.

[Table 1 about here]

3.2 China’s Trade Shares

A second set of stylized facts concern China’s trade flows. First, the value of trade has been growing more rapidly than GDP – leading to a rising trade share of GDP. Figure 1 illustrates this by showing total Chinese export and import values as a fraction of GDP. It can be seen that the trade share of GDP has approximately doubled since the 1990's.

[Figure 1 about here]

Second, the composition of China’s trade has also changed dramatically in the last decade. As emphasized by Schott (2006) and Rodrik (2006), and Amiti and Freund (2008), China’s export bundle has become increasingly sophisticated. Table 2 shows the value shares of China’s exports in 1990, 1995 and 2005. It shows that there has been a very dramatic decline in agricultural goods over the last 15 years and a more than doubling of the share of durable in China’s export basket.

[Table 2 about here]

The measurement of trade shares, however, is also the subject of some debate. Anderson (2007) has argued that the recent acceleration in the exports relative to GDP in this decade largely reflects measurement error.6 Nevertheless, Anderson (2007) also reports larger increases in export to GDP ratios over slightly longer periods, such as 1990 to 2005.

There are several other non-mutually exclusive explanations for the rise in trade to GDP ratio and changes in export composition. Part of this expansion for both the changing level and pattern of trade is likely to be due to falling trade barriers. Rumbaugh and Blancher (2004) report that the average (unweighted) tariff rates in China fell from 55.6% in 1982 to 12.3% by 2002. The most rapid change was during the 1990's. Table 3 reports data derived from the World Bank's Trade, Production and Protection database, specifically for the period of interest, 1995-2004.7 It shows that China’s tariffs, on both the USA and ROW, fell substantially over this period.8

[Table 3 about here]

A second consideration is productivity growth. Some studies have claimed to find evidence that productivity growth has been higher in export sectors.9 A related explanation is that export specific productivity growth has occurred due to falling trade costs and this rise of global fragmentation of production (Jones and Kierzkowski 1990, Deardorff 2001, Yi 2003). This geographical fragmentation of production is best understood as a result of changes in technology and falling trade costs. Specifically, fragmentation is only possible if trade and communications costs are sufficiently low.

Unfortunately, as noted by Anderson and van Wincoop (2004) and Hummels (2007), the evidence on how trade costs have fallen over time and the relationship to global fragmentation is very limited.10 Athukorala (2003), Branstetter and Lardy (2006) nevertheless argue that this fragmentation been particularly pronounced in the East Asian region with the integration of China into production networks. Amiti and Freund (2008) also provide evidence to suggest that fragmentation lies behind the apparent increasing skill intensity of China’s exports.

Another source of biased productivity may relate to foreign investment patterns. According to Branstetter and Lardy (2006) and Lardy (2003), the sectors that have expanded, such as transport, machinery and electronics, are those where foreign investment has been largest. During the 1990's, the government reduced the non-tariff barriers and also introduced special privileges for export processing firms including all foreign owned and joint owned firms.

Thus, in modeling China’s trade biased growth, we want to allow both for falling trade barriers, changes in trade costs and other sources of trade biased productivity growth. In what follows, we therefore introduce trade biased technological change in a parsimonious way that can be interpreted either as falling trade costs, or direct productivity gains specific to traded good sectors.

4. Tertiary Education Reforms

The rapid expansion of skilled labour in China has occurred on the back of a long reform process in education and rapid growth. It is also a result of deliberate government targets which have been set in response to a perceived skills shortage. These supply side changes, however, may be seen also as a policy response to rising demand for higher education as a result of China’s growth.

In order to disentangle the effect of education policy changes from endogenous factors, we first consider the quantitative impact of China’s trade biased growth, as discussed above, on the supply of skilled labour. We then examine the impact education subsidies required to meet the observed education enrolment increase, and examine the long run implications of these education policy changes.

4.1 Growth in Tertiary Enrolments.

Freeman (2007) and Li et al. (2008) have recently drawn attention to possible international economic implications of the education revolution that is occurring in China. As shown in Figure 2, the ratio of tertiary student enrolments to the labour force in China has approximately doubled in just four years. It increased from a rate of 1.2% in 2000 to 2.2% in 2004, which is an 85% increase. In absolute numbers, this represents an increase of 8.3 million tertiary students.11 Figure 2 also reports data from Islam et al. (2006) suggesting the skill intensity of China’s workforce has been growing consistently since the end of the Cultural Revolution.

[Figure 2 about here]

This poses two questions. First, what is the cause of the rise in enrolment rates? In this section we briefly outline a number of reforms that have allowed an increase in education supply. In addition to these reforms in the education sector, we also wish to explore the role of China’s growth, and in particular the trade bias of the growth, in understanding the increase in education investment.

Second, what is impact of this expansion in enrolments on China’s endowment of skilled labour and trade patterns? As noted by Freeman (2007) and Li et al. (2008), China’s sheer size means that a rising skilled labour force may have a significant effect on the world supply of skilled labour, and hence also on international trade patterns. What is the long term impact of the increased tertiary education investment? In a new steady state the 85% increase in education enrollments investment will also raise skilled labour stocks by 85%, because the ratio of students to labour force must be constant. Thus in the long run the education boom will have an equally large impact on China’s skilled labour endowment. In the final part of this paper, we use our model to examine this long run effect on China’s GDP trade and also on the USA economy.

4.2 Education Reforms and Planning

Initially, China’s reform process amounted to an undoing of the impact of the Cultural Revolution. Tsang (2001) and Chow (2002), among others, document how as part of this policy, higher education in China ceased from 1966 to 1976. Though enrolments recovered at the end of the Cultural Revolution, the current education boom did not begin until the late 1990's.

The recent expansion in enrolments exceeded the targets set out in the “Tenth Five-Year Plan”, covering the period 2001 to 2005.12 The targets were achieved through several different policies. First, is the rise in private education institutions. Private institutions of higher education – known as “minban” institutions – were legally established in the 1990s. By 2004, there were 226 minban institutions with 1.4 million students (Zhang, 2006; Min, 2005). Likewise, until 1990, universities did not charge students for tuition, but by 2004, fees accounted for 18.6% of educational expenditures (Zhang 2006, Min 2005 and Hannum et al. 2008).

Second, the government has increased funding for secondary and tertiary education. China’s aggregate education expenditure as a percentage of GDP grew from 3.4% in 1991 to 5.3% in 2004. Though the proportion of funds for education coming from government has fallen from 85% percent in 1991 to 62% in 2004, public funding has still grown as a share of GDP (Hannum et al. 2008). The “Eleventh Five-Year Plan” aims to increase public spending relative to GDP to 4% – which is a 66% increase over the level in the mid 1990s.

Reforms in the labour market have also complemented the liberalisation policy in higher education. Historically, the wage policy in China forced a low rate of return to skilled labour and there are still distortions on the wage setting in the labour markets (Knight and Shi 1996, Young 2003, Heckman 2005 and Fogel 2006). Fleisher and Wang (2004, 2005) and Fleisher et al. (2006) suggest that these wage differences understate the return to education by 30 to 40%. Heckman and Li (2004), however, report evidence that the return to education has been rising in response to labour market reforms.

Thus, the combination of: reforms to education sector; increased government spending, and; reforms to the labour market have provided the basis for the expansion in enrolments. In addition, however, given the rapid growth and growth of manufacturing output, China was also likely to have experienced rapid growth in demand for skilled labour. Indeed, according to Tsang (2001), the motive for the supply side measures – such as raising education enrolment targets – was that China was perceived to be facing a “skills shortage” which was thought to be a bottleneck to sustaining current growth rates.

5 Policy Simulations

Our aim is to provide some quantitative insights into: (i) how China’s economic growth, and growth bias, has affected the long run stock of skilled labour, (ii) how this growth bias has affected the USA economy, and (iii) how the expansion of China’s stock of skilled labour, including the long run effects of recent education reforms reforms, might have affected China and USA economies.

We begin by constructing a benchmark equilibrium. This is calibrated to steady state growth path where all variables are growing proportionally, prices and factor returns and the debt to GDP ratio are constant, and there is balanced trade.

Bosworth and Collins' (2008) measure of growth of 8.9% implies a 2.15 fold increase in GDP per capita over a decade. The underlying assumed world trend rate of growth, of just under 2% per year, leaves an additional growth premium for China of 6.8% per year, or equivalently, a 80% increase in GDP per capita, above the trend rate over 10 years.13

In the simulations below, we use this figure as a target for the aggregate growth of the Chinese economy. We shall consider alternative combinations of sectoral productivity parameters which, in combination with endogenous accumulation responses, generate an 80% fold increase in GDP per capita. Thus, the total amount of growth is fixed across each simulations. Across different simulations however, the composition of growth and sectoral bias of this growth will vary.

Second, as shown in Figure 1, from 1995-2005 the export to GDP ratio has increased from approximately 23 to 37% while the import to GDP ratio increased from 21 to 32%. We use export and import to GDP ratios based on the average of these values as a target value. Thus, exports and import growth is targeted to grow from 22% of GDP in the base to 35% of GDP – that is, a target increase of 59%.

As discussed, we employ combinations of changing trade cost parameters and falling tariff rates that achieve the export growth targets observed in the data. Firms in the model face a revenue function that describes the revenue faced by selling to each market. The parameters of this revenue function can be interpreted as trade costs (Bergstrand, 1985, Baier and Bergstrand, 2001). That is, they represent the fraction of value received by firms per unit of value received in each market. We denote these revenue function, or trade costs parameters, for China’s exports of each traded good i, as , and . In the benchmark we normalize these to unity. A value greater than unity therefore means that trade costs have fallen relative to the benchmark. Specifically, a fall in trade costs associated with China’s export markets means that and .

Given these productivity parameters, we proceed as follows. Simulation 1 (s1) examines the effect of a pure labour augmenting increase in productivity: that is a uniform increase in the effective labour supply parameters on skilled and unskilled labour, across all sectors, i=1-11.

In the second simulation, s2, we add to this a uniform fall in Chinese export trade costs across all tradable sectors. Thus, we choose , such that the export to GDP ratio adjusts to a target increase of 59%.

In s3, we allow for productivity bias across the traded goods sectors. Thus, we choose so that (i) the export value share in each sector reaches their 2005 share value, as given in Table 2, and (ii) the export to GDP ratio increases to its 59% target as before. Thus, in s3 we allow for composition of trade effects. In s4, the targets remain the same but we also include the tariff reductions described in Table 3. The simulations, with relevant targets and assumed endogenous variables are summarized in Table 4.

[Table 4 about here]

6 Results: Trade Biased Growth

6.1 Steady-State Solutions for China

Table 5 records the steady state solutions to the simulations, s1-s4, for China and the results for the USA are reported in Table 6. From Table 5, column s1, it can be seen that the target increase in GDP requires a 107% increase in the labour productivity parameters, and . It can be seen further that the exogenous productivity growth also generates an 85-96% increase in the physical capital stocks.

[Table 5 about here]

It can also be seen, however, that the assumption of labour augmenting productivity change generates a number of counterfactual results. In particular, exports as a fraction of GDP do not increase, but fall by 26%. Intuitively, this is because the Chinese domestic economy, including the non-traded goods sector, has grown relative to the world economy. Thus the multi-product firms substitute away from export markets and towards the domestic market.

Likewise, under neutral productivity growth, the pattern of growth across sectors is also very even. With respect to skilled labour, neutral growth does induce accumulation of skilled labour but the increase of 18% is again small relative to the stylized facts where tertiary education enrolments have approximately doubled. Thus, the assumption of labour augmenting productivity does not explain the strong labour up-skilling or rising trade-GDP ratios that have been features of Chinese economic growth.

Columns s2 and s3 of Table 5 shows the effects of falling trade costs, or equivalently, trade-sector biased growth. In s2, this is achieved by endogenously choosing the trade cost parameters for China’s exports to the USA and to the ROW, and , where the change in these parameters is constrained to be the same across the regions and commodities, () . These adjust in such a way that the export to GDP ratio increases by the target of 59.1%.

It can be seen in Table 5, column s2, that this trade-GDP target requires a 93% increase in and across all traded goods sectors, (or equivalently, a 52% fall in trade costs). The presence of trade-biased growth also reduces the required aggregate labour augmenting productivity substantially, from 107% to just 38%. Thus falls in trade costs consistent with observed export shares, is capable of accounting for the bulk of China’s productivity growth.

Allowing for trade biased productivity growth also generates dramatic impact on the stock of students and skilled labour, which increase by 46%. Thus trade biased productivity growth also accounts for a large fraction of the observed 85% increase in enrolments. This suggests that there is an important link between falling trade costs and skill accumulation. This, moreover, is a topic which has received very little attention in either the trade or growth literature.14

Another important effect of allowing for trade biased technical change (or falling trade costs) is on the terms of trade. With neutral growth there is a 10.7% fall in the terms of trade, but with trade biased growth this increases to a 39% fall. We shall return to this in our discussion of the implications for the USA, below.

Allowing for trade biased growth in s2, nevertheless, results in counterfactual outcomes for the export shares. The share of low-tech manufacturing in s2 is more than double the actual value in China in 2005 and the share of Durables is only half the actual value. Thus, in s3 we introduce sector specific export share targets, using the changes in sectoral export shares between 1995 and 2005 in Table 2. To meet these targets we allow the trade cost parameters, and to vary across sectors.

The results of this experiment are reported in column s3 of Table 5. The most notable difference between s2 and s3 is that s3 involves larger trade costs reductions in Durables exports, relative to the other sectors. This results in even greater skilled labour accumulation – with a 53% increase in the stock. Thus, both the increased trade volume, as well as the changing trade shares, have contributed to rising demand for skilled labour.

Next, we allow for the changes in tariffs that occurred 1995-2005, as reported in Table 3. As shown in column s4 of Table 6, allowing for the changes in tariffs again results in only small changes relative to s3. The required fall in trade costs is reduced substantially relative to s3, due to the effects of the tariff, but there is still a very large, 49%, increase in skilled labour stocks and the terms of trade effects are similar. Thus, from the experiments s1 to s4 we conclude that China’s trade sector biased economic growth is a more plausible explanation than Harrod neutral non-sector specific productivity growth. It also accounts for a substantial increase in tertiary enrolments, and hence the long run skilled labour stock, and also generates substantial falls in China’s terms of trade.

6.2 Impacts of Chinese Growth for the USA

The impact of these simulation experiments on the USA is shown in Table 6. First, we note that under the assumption of neutral productivity growth, s1, there is practically no impact on factor incomes in the USA. This, of course, is related to modest terms-of-trade effects in this experiment.

[Table 6 about here]

Allowing for the rise in China’s trade–GDP ratio in s2, however, generates a 8.6% improvement in the USA terms-of-trade and a 33% increase in its export to GDP ratio.15 These flow through to significant aggregate benefits with a 3.8% increase in USA's GDP and a similar 3.5% increase in consumption. It can be seen further that the increase in GDP is generated by capital deepening and a significant fall in the price of traded goods. Thus the gains in the USA are driven primarily by the lower cost of capital generating increased capital deepening.

Including sector specific trade cost reductions, s3 further increases the change in USA GDP to 4.5%. The main impact of this change, however, is on the distribution of USA output levels. The greater reduction in trade costs for Chinese Durables exports in s3 implies a 43% decline in USA Durables output. Finally, allowing for Chinese tariff reductions in s4 moderates these changes somewhat, though the results are broadly similar to those in s3. Thus, results suggest that Chinese growth has had quite a large impact on USA income levels and has also caused a large contraction in Durables output.16

Finally, the effects of China’s biased growth on the USA labour market has received considerable attention in the literature but less attention has been given to the long run effects on skill accumulation in the USA. Furthermore, though early studies have found limited evidence that trade affects wage inequality, Krugman (2008) claims that the impact of China’s economic expansion on international trade patterns over the last decade, which is our focus, is likely to be much greater than was observed previously, due to its dramatic increase in size and much greater prominence of the tradable goods sector.

In Table 6 s3 we see that Chinese growth increases both skilled and unskilled wages in the USA by approximately 3%. Likewise, in cases s2and s4, there is strong growth in both skilled and unskilled wages in the USA with little long run change in the skill premium. It can also be seen, however, that China’s growth leads to a 0.7-1.7% increase in the USA stock of skilled labour across these experiments. Thus the impact on the labour market in the long run are relatively neutral for wage inequality, but nevertheless imply significant wage growth and skill deepening in the USA.

7. Long Run Implications of China’s Education Boom

7.1 Education reforms

Simulations s1-s4 not only draw out the implications for China’s growth on the USA, but also illustrate the effects of biased growth on skill upgrading in China. Importantly, falling trade costs were shown to have a large effect on the equilibrium quantity of skilled labour.

As discussed above, there have also been reforms in the education sector and increases in government spending. To capture the impact of these policy environment influences factors on China’s skill-upgrading, we consider the impact of an increase in tertiary education subsidies. The subsidies capture the effect of increases in government spending on education. Moreover, to the extent that some of the reforms can be thought of as removing quotas on education enrolments, the increase in subsidies can also be thought of as an index of these reforms, given the tariff-quota equivalence.

Specifically, we set an education target equal to the 85% expansion of education enrolments. Then, in s5, we choose an education subsidy, , such that the stock of tertiary students increases to the target value. The value of the education subsidy is assumed to be zero in the base, and the new level is thus endogenously determined.

Next, in order to allow for the expansion in education demand due to trade biased growth, we add in all the trade and labour augmenting productivity and tariff rate changes that were derived in s4 above. These are added as exogenous changes in the model. To this we then add the 85% education enrollment target and allow the education subsidy to adjust endogenously and compute the percentage differences from s4. We label this experiment as s6. The results in s6 thus report only the incremental effect of the government's education supply changes, conditional upon China’s trade biased growth experience.17

7.2 Results

The results, in s5 of Table 7, show that meeting the education target without the benefit of trade biased economic growth, requires an education subsidy of approximately 91.8 cents per dollar – from an assumed base subsidy of zero. The education subsidy has a relatively large impact on GDP in China, of 6.8%, but a much less dramatic increase in consumption. Somewhat counter–factually, however, it also results in a 51% fall in skilled wages and exports relative to GDP.

[Table 7 about here]

The more sophisticated experiment is s6 where we first reproduce the results for s4 by incorporating all the endogenously determined trade costs and labour productivity changes – as well as the tariff changes – as exogenous changes, and then also add the endogenous education subsidy and education target. We know from the preceding discussion that the biased productivity growth generates an endogenous education expansion of 49%. Column s6 reports the incremental effect of these education subsidies compared to s4. It can be seen that the education required to meet the observed targets is now only 51.9 cents per dollar.

Naturally, the education subsidy causes a rise in skilled labour but otherwise the incremental effects are quite modest. GDP rises only 2% relative to the trade biased growth benchmark, s4, but consumption only increases by 0.6% due to the greater fraction of GDP spent on investment. Skilled wages are shown to fall by 22%, but this is relative to a base case, s4, in which wages rising by 28%. Thus the combination of education subsidies and trade biased growth leave skilled wages relatively unchanged. Likewise, education subsidies exert only a small negative effect on trade levels of half a percent.

[Table 8 about here]

Finally, Table 8 shows that the impact of China’s education policy on the USA is minimal – the largest changes being a 0.5% increase in exports and minerals production. These modest results stand in contrast to the attention given to China’s rapid education expansion in recent literature. In our conventional trade and growth settings, the effects of this expansion, even in the long run, is likely to be very small. This insignificance is highlighted when compared to the effects of trade biased growth in China on the USA economy, which were large and positive. Moreover, the education itself is perhaps best seen as largely a consequence of the growth.

8. Conclusion

Our simulation results indicate that the last 10 years of Chinese economic growth is responsible for 3 - 4.5 percentage points of growth in the USA above its trend rate. In contrast, we have found that a potential 85% increase in China’s skilled labour supply, which is an implied as well as a long run consequence of its tertiary education boom, has effectively no impact on the USA economy. This suggests that either anxiety or effusiveness over the effects of this change on the USA, may well be misplaced, especially relative to the effects of China’s growth.

Second, we have found that the bias of productivity growth is crucial in understanding the international transmission effects of growth. Specifically an 80% increase in Chinese GDP generated by labour augmenting productivity has practically no effect on USA consumption or GDP. The extent of gains to the USA from Chinese economic growth thus depend critically on the sources of the growth, with trade biased growth – such as falling trade costs – generating greater terms of trade gains for the USA. This points to the importance of understanding trade frictions and the sources of increased trade flows for world economic g.

Finally, we have also shown trade biased growth generates a large increase in education demand and skilled labour supply in China. Thus, despite the fact that there have been many supply side policy reforms in China’s tertiary education system, the model indicates that the trade biased productivity growth in China can account for more than half of the observed growth in tertiary enrolments in China over the last decade. To the extent that falling trade costs are a feature of the modern era of globalization, our results suggest that this may be having a large impact on skill formation in China, and potentially other developing economies where there has been export led growth.

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