Trade Skills and Wages Explaining Growth in China and India

Trade, Skills and Wages: Explaining Growth in China and India. Richard G. Harris Simon Fraser University Peter E. Robertson * University of New South Wales Draft Only - Comments Welcome July 28 2008 We quantify the effects of trade liberalization on, not only skilled and unskilled wages, but also capital and skill accumulation. We find first that, in both the cases we consider, China and India, trade liberalization generates significant growth and capital accumulation and strong wage growth for skilled and unskilled labour. Under our preferred parameterization the accumulation of capital also causes rising skill premiums human capital accumulation. We show also that the path of the skill premium can be very non monotonic and changes sign over the transition. Third we show that the magnitude of changes in skill premiums and the degree of skilled labour accumulation depends critically on the degree of Hicks-Allen capital-skill complementarity. The results do not rest on assumptions regarding barriers to entry, imperfect competition or barriers to foreign technology adoption. They depend only on standard neoclassical trade and growth theory. Changes in commodity prices raise returns to capital and this induces capital accumulation. Under standard assumptions regarding capital skill-accumulation this induces rising skill premiums in the short run and human capital investment in the long run. The results are discussed in the light of the tradewage literature and the literature on trade and growth. Keywords Trade, Economic Growth, Human Capital, Education, Wage Inequality, China, India. * Corresponding Author: Peter Robertson, School of Economics, John Goodsell Building, UNSW, Sydney 2052. Email p.robertson@unsw.edu.au, Phone. (02) 93853367. 1 1. Introduction If trade has a significant impact on the relative return to skilled labour, as is maintained in recent trade-wage literature such as Krugman (2008), it should also affect human capital investment decisions. This potential investment response is particularly important in developing economies where capital and human capital are relatively scarce. For example in China, India and many Latin American countries, trade liberalization has been associated with rising relative returns to skilled labour. 1 In China and India, however, the rising skill premiums have also been accompanied by rapid growth in education enrollments. Though there is considerable literature on the link between trade reforms and wage inequality, very little is known about this has altered the incentives for skill accumulation. The aim of this paper, therefore, is to consider the effects of trade liberalization on skill premiums and factor accumulation in China and India. Specifically we describe a dynamic computable general equilibrium model which solves the optimal time path for multiple types of physical capital and also skilled labour. The model is calibrated both to Chinese and Indian data and the results of unilateral trade liberalization in each country are reported. We show that tariff elimination has very different impacts on relative factor incomes in the long run and in the short run and can change sign over the transition. Specifically in both China and India the skill premium rises on impact and over the medium term but fall in the longer term. In both countries we also find that further unilateral tariff reductions lead to substantial capital deepening and up-skilling of the labour force. Lastly we show that the magnitude of these responses results depend critically on capital-skill complementarity. In particular we find that, given capital-skill complementarity, induced capital accumulation from trade liberalization has a very large impact on the skill premium which dominates the direct effects of trade on factor prices. 1 Goldberg and Pavcnik (2007) suggest that trade liberalization in developing economies tends to be associated with rising, rather than falling, skill premiums. They also discuss the extensive literature which aims to reconcile theory and evidence. Most of the literature focuses on problems of interpretation of the Stolper-Sameulson, time-lags and possible effects of induced skill biased technology effects. 2 2. Background 2.1 Trade–Wage Studies The rapid opening up many developing economies has resulted in extensive research agenda on the effects of trade liberalization. Much of the focus of this research is on the effects of trade on poverty and inequality, as survey recently by Winters, McCulloch and McKay (2004) and Goldberg and Pavcnik (2007). 2 One of the interesting facts to emerge from this literature is that, contrary to the spirit of the Stolper-Sameuson theorem, trade liberalization often seems to be associated with rising skill premiums in developing economies. 3 The evidence from computable general equilibrium model studies is also rather mixed. For example Hertel et al (2003) report the effect of multi-lateral trade liberalization for 14 developing countries using the GTAP model. They find that trade liberalization reduces the skill premium most countries but find the opposite occurs in Bangaldesh and Uganda. Likewise Harrison, Rutherford and Tarr (2003) find that trade liberalization reduces production labor wages in their CGE model of Turkey and attribute this to heavy protection in labor intensive sectors. 4 As documented by Chaudhuri and Ravallion (2007) the recent reforms in China and India have also been associated with a rise in inequality. Moreover in both countries rising inequality appears to be associated with rising skill premiums, (Dutta 2005, Fleisher and Wang, 2004). The roe of trade liberalization is unclear however. With respect to India, Chamarbagwala (2006) and Kijima (2005) argue that rising wage inequality resulted from technological change. Mishra and Kumar (2005) in find that the 1991 reforms led to a reduction in skill premiums in India. 2 The debate over the consequences of economic integration and globalization on wages incomes is extensive and contentious. Examples of the literature include Lawrence and Slaughter (1993), Berman Bound and Griliches, (1994), Wood (1998), Krugman (2000) Falvey (1999), Leamer (2000), Haskel, and Slaughter (2002), Neary (2002). The debate has been reinvigorated by Krugman (2008) and Lawrence (2008). As shown by Winchester, (2007) within this literature the use of Computable General Equilibrium (CGE) models has been widespread. 3 Trade liberalization has been found to increase the skill premium increased in Mexico, Chile,and Costa Rica (Hanson and Harrison, 1995, 1999a,b; Revenga,1997; Robbins, 1995, 1996). 4 Other recent studies that explicitly report the impact of trade on relative skilled and unskilled wage ratios include: Harrison et al (2004) who find that trade liberalization reduces the skill and poverty levels premium in Brazil; Devarajan et al (2000) find that trade liberalization reduces inequality in South Africa. Field and Wongwatanasin (2007) find that tariff reduction is associated with falling wages and a rising skill premium in Thailand. 3 Likewise with respect to China, Ianchovichina and Martin (2004) find that the WTO accession reforms would reduce skill premiums. Anderson et al (2002) find that WTO accession reforms in China cause a small fall in the skill premium for non-farm labour but a widening gap between farm and non-farm unskilled labour. 5 Thus the evidence tends to suggest that, despite rising skill premiums in China and India, there is little evidence that this can be attributed to trade liberalization per se. Nevertheless several studies of India have pointed to potential links between trade liberalization and skill accumulation. For example Edmonds et al (2007) find that trade liberalization prompted skill accumulation in states that benefited the most from liberalization. Less formal arguments are also made by Wood and Calandrino, (2000) and Kochar et al (2006). Fleisher and Wang, (2004) similarly note the need for increasing skilled wage to promote education enrollments in China. 2.1 Models of Trade and Skill Accumulation A benchmark model by Findlay and Kierzkowski (1983) is useful to help fix ideas regarding human capital dynamics. In their mode there are two final goods produced by skilled and unskilled labour. Education is an intermediate good which also employs a specific capital good. They show that trade liberalization has no long run effect on the skill premium but induces skill accumulation in the skilled labour abundant country and de-accumulation in the unskilled labour abundant country. As they argue the incentive to acquire skills is reduced if the goods requiring skills more intensively can he imported more cheaply. Other models to examine these interactions include Das (2001), Deardorf (2000), Long, Riezman and Soubeyran (2007) and Bond, Trask and Wang (2003). All of these emphasize theoretical reasons why allowing for human capital accumulation can give very different results from usual fixed endowment models. 6 5 Trade liberalization was only part of the reform package in these countries however. The ongoing reforms in the education sector in India and China are documented respectively by Kapur and Mehta (2007) and Li and Whalley (2008).5 This makes it very difficult to empirically identify the effects of trade liberalization alone. 6 Das (2001) for example has emphasized that fact that skilled labour is used for training as well as production. Thus a model where skills have to be accumulated, or acquired through schooling, which gives rise to alternative sources of demand for skilled labour with implications for the skill premium. Deardorf (2000) considers a model with endogenous skill formation and Long, Riezman and Soubeyran (2007) extend the model to include firm specific skills Falvey et al (2007) look at adjustment costs on different generations. Abrego and Edwards (2002) also note the potential for human capital policy to mitigate skill premium effects of trade liberalization and Bond, Trask,and Wang (2003) observe the possibility of multiple equilibria in a model with trade an endogenous growth. 4 Despite the obvious implications that increases education investments might have for poverty reduction in the longer term, and potentially important adjustment issues in the short term, little attention has been paid to how trade reforms might affect human capital accumulation and growth, especially compared to the large literature on wage inequality. Several models exist which suggest which suggest that trade liberalization could have important effects on skill accumulation and growth Nevertheless, as with the trade-wage literature, the impact is likely to vary across countries and to depend on the types of trade reforms considered as well as modeling assumptions. In what follows we therefore set out a neoclassical model which explicitly models international trade and economic growth through capital and human capital accumulation. The model is thus similar to Findlay and Kierzkowski (1983) in that world prices are exogenous and factors are mobile and all sectors are competitive. The model however is much more detailed with 11 sectors and four types of physical capital, and seven factors of production in each region. We also explicitly consider the trade and growth relationship by solving the perfect foresight transition path. In this way we obtain results not only with respect to skill upgrading in the long term, but also how expectations of higher investment rates affect short term factor returns and the skill premium. 3. A Model of Skill Formation To consider the effect of trade liberalization on human capital accumulation we first consider a simple aggregate model of human capital accumulation. Latter we will integrate this with multisector general equilibrium model. Let the working population, or labour force, at time t be denoted Pt . The net increase in the labour force is Pt +1 = (1 + bt − d t ) Pt (1.) where bt is the birth rate, d t is the retirement rate. At a point in time the labour force is defined in terms of skilled labour, LS, unskilled labour LU, and stock of students, H. Pt = LS t + LU t + H t (2.) The stock of skilled labour depends on past schooling decisions by unskilled workers. There is an endogenous flow of students graduating each year and entering the skilled labour force. Denoting this flow on skilled entrants as E t , we have Et = H t / ζ where H t is the stock of students, E t is the flow of graduates and ζ is number of years in tertiary education. The updating equation for skilled labour is then LS t +1 = LS tt + H t / ζ − d LS t (3.) 5 The optimal pattern of investment in schooling is chosen by a representative household who maximizes the present value of total labour incomes subject to on the job training costs faced by skilled labour. This is given by ∑ t =0 ∞ 1 u s ,t LS t − u s ,t C ( H t , LS t ) + u u ,t LU t − p e,t Ae t H t 1 + ρ tt [ ] (4.) where u s ,t is the after tax skilled wage, u u ,t is the after tax unskilled wage, p e ,t is the consumer price of education, net of education subsidies, Ae t is a technology parameter determining the quantity of education required to produce a skilled graduate, ρ is the households rate of time preference, and C ( H t , LS t ) , C1 ( H t , LS t ) > 0 C 2 ( H t , LS t ) < 0 , is an on-the-job training cost function which says that it is costly to raise the flow of new entrants (indicated by the stock of students H) relative to the existing pool of skilled workers. We assume that the on-the-job function takes the form C= α ( H t − bζ LS t ) 2 2 LS t (5.) Thus the size of the training costs depend on deviations of H from the reference level bζ LS , where, as discussed below bζ is ratio of students to skilled labour that will exist in a steady state. The Household’s objective is to maximize (4.) subject to (1.), (2.) and (3.). The Lagrangian is ∑ t =0 ∞ 1 ˆ u s ,t LS t (1 − C ( H t , LS t )) + u s ,t LU t − p e,t Ae,t H t − Π t ( LS t +1 − Et − γ M t − (1 − d ) LS t 1+ ρ t [ ] Where Ae,t H t is the output of the education services which is proportional to the stock of students, and hence pe,t Ae,t H t is the direct cost of schooling. The household takes the relevant prices and wage rates as given. These are determined in a general equilibrium model taking account of inter-temporal preferences, optimal investment decisions and factor supplies, trade flows and world prices. The first order conditions associated with the preceding Lagrangian are: ˆ − u s ,tt C1 ( H t , LS t ) ζ − u u ,tt ζ − qe ,t Ae ,t ζ + Π t = 0 ˆ − Πt + 1 ˆ u s ,t +1t (1 − C 2 ( H t +1 , LS t +1 )) − u u ,t +1t + (1 − d )Π t +1 = 0 1+ ρ (6.) ( ) (7.) 6 Equation (6.) says that the shadow price of a unit of skilled labour is just equal to the sum of the maginal costs of schooling, that is variable cost of education per unit, the opportunity cost and the marginal adjustment cost. From (5.) the marginal adjustment cost function is C1 ( H t , LS t ) = α ( H / LS − bζ ) . Substitution into (6.) gives ˆ Π t / ζ − u u ,t − q e ,t Ae ,t Ht = + bζ LS t α u s ,t (8.) which is the demand for schooling function. It can be seen that the demand for schooling relative to the existing stock of skilled labour depends upon the shadow price of skilled labour relative to the cost of schooling, which include the direct costs, qe,t Ae,t , and the opportunity cost u u ,t . Equation (8.) shows thus shows that variations in demand for students will depend on the asset ˆ value of skilled labour relative to the current wage rate Π t / u s ,t , the inverse skill premium, u u ,t / u s ,t , and the direct costs of education. Intuitively it says that the asset price of a unit of skilled labour must be equal to the discounted future net benefits, which are after adjustment services wage gap and the scrap value of the asset. 3.1 Steady State Growth path We define a steady state growth path as a path all stocks are growing at the rate of effective population (1 + n)(1 + g ) . Note first that the ratio of skilled labour to population LS t / Pt must be constant and hence LS t must grow at the long run population growth rate. Dividing (3.) by LS t and re-arranging gives H / LS = bζ as asserted above, in discussing (5.). 7 ˆ The wage rates, u u ,t u u ,t , and shadow price Π t must be growing at the growth rate of productivity, 1 + g on the balanced growth path. In view of this it is useful to redefine these wage rates and shadow prices in terms of efficiency units that will be stationary. To this end define an efficiency ˆ adjusted shadow price of skilled labour as Π As ,t t ≡ Π t . Likewise let ˆ u u ,t = u u ,t / Au ,t and ˆ u s ,t = u s ,t / As ,t . In terms of these efficiency unit adjusted shadow prices (7.) becomes As ,t Π t (1 + ρ ) = As ,t +1 u s ,t +1t (1 − C 2 (ζ E t +1 , LS t +1 )) − Au ,t +1 u u ,t +1t + (1 − d ) As ,t +1 Π t +1 ( ) (9.) 7 This holds irrespective of the nature of the dynamics, ie whether they are forward looking or recursive. Essentially any model of skill accumulation that permits a steady state, must exhibit a similar relationship between skilled labour inflows and education, as long as both types of skilled labour produce the same factor services. 7 On a steady state we have Π t = Π t +1 , u s ,t = u s ,t +1 , u u ,t = u u ,t +1 and C 2 = 0 . In the benchmark we choose units so that the ratio of efficiency units of unskilled to skilled labour be Au / As ≡ 1 . Given these assumptions (9.) becomes Π = Δ (u s − u u ) (10.) where Δ ≡ (1 + g ) /((1 + ρ ) − (1 − d )(1 + g )) . This shows that the asset price of a unit of skilled labour is simply proportional to the skilled unskilled wage gap. Likewise on a steady state (6.) becomes Π = ζ ( uu + qe ) (11.) Which shows that the asset price of a unit of skilled labour is simply equal to the opportunity and direct costs of schooling. Combining these expressions gives the steady state relationship between the skill premium and the price of education. u s − u u = ( q e + u u )(ζ / Δ ) (12.) Thus the steady state value of the skill premium, u s / u u depends on the number of years of schooling it takes to be complete tertiary education, ζ, and is proportional to the consumer price of education (including subsidies) relative to the after tax unskilled wage p e / u u . Note that these steady state relationships are not sufficient to predetermine the effects of trade liberalization on the skill premium. Equation (12.) does show, however, that for the skill premium to rise in the long run p e , must rise. 8 Thus the long run effects of trade liberalization on the skill premium depend on how changes in factor returns change the costs of education through the zero profit conditions. 3.2 Education Supply Education services are produced by a non-traded competitive industry. The treatment is completely symmetric with other non-traded goods in the model. The main difference is that education is assumed to be subsidized by an amount equivalent to the governments share of education 8 This contrasts with Findlay and Kierzkowski (1983) the costs of education is fixed in the steady state so that in the long run the effect on the skill premium is always zero and changes in demand induced by trade liberalization only affect factor supplies. Note however that if education sector used a Ricardian technology with skilled labour as the only input then the percentage change in u s would always equal the percentage change in p e and hence by (1.) te skill premium would be constant. 8 spending. Thus education production comprises of intermediate and primary factor inputs. Dual to the value added function is a nested CES unit cost function and unit factor demands are given by Shepherd's Lemma. Zero profits in the education imply that * p e = (c e + ∑ a je p j ) j (13.) where p e is the producer price of education and education subsidy, s e , we have ce is the unit cost function. For a given p e = (1 − s e ) p e (14.) Finally educational output is assumed to be proportional to the number of students enrolled in a given year. Hence y e ,t = Ae ,t H t (15.) The efficiency parameter Ae ,t t must also grow at the steady state rate 1 + g , otherwise education output will become infinitesimally small relative to the supply of skilled labour. Thus the education sector must be able to produce output of the same “quality” as the skill level of the labour force. 4. Calibration 4.1 Education Data The main inputs into calibration of the education sector and human capital investment equations are data on; the relative stock of skilled labour, LS/P; the student stock relative to skilled labour stock H/LS; and values of total higher education spending. With this data (2.) can be used to determine the stocks of skilled labour, LS, unskilled labour, LU and tertiary students, H. We equate the relative stock of skilled labour to the population as equivalent to Barro and Lee (2000) figure for the fraction of the labour force aged 15 and over who have completed tertiary education. This value is 2.2% in India in 2000 and 2.1 percent in China. Estimates of current tertiary enrolments are also available. Despite this China and India are both undergoing massive increases in education investment the current stocks of students is very large relative to the stock of skilled labour. To obtain a benchmark figure that would potentially be consistent with the steady-state path, we use 9 the USA value of tertiary student population to skilled labour stock which is 0.35. 9 The effective units of these labour flows are then respectively As LS , Au LS , As H . 10 Given an appropriate normalization of these efficiency parameters then, from (15.), the value of As H gives a value for education output y e . * The value of education output, p e y e is equal to total tertiary education spending which is obtained from UNESCO data, for India and the OECD (2002) data. In both China and India tertiary education spending represents just under 1 percent of GDP. These data sources also break spending into government and private sources. The ratio of government tertiary education spending relative to total education spending gives the value of education subsidies, s e . For India this represents a 77.7 percent subsidy and for China the figure is 56.7 percent. Dividing spending by the education output level, y e , then gives the producer price of education * p e . The value of education subsidies, s e , is defined to be equal to government share of tertiary education spending and the consumer price is then determined from (14.). Given GTAP data on unskilled labour incomes, wu LU , we can use the value of LU to determine the unskilled wage rate, wu , and the after tax unskilled wages u u = (1 − t u ) wu , where t u is labour income taxes. Finally we consider the skilled wage rate ws , after tax skilled wages, u s skill premium, u s / u u . GTAP data on skilled labour income, ws LS and a value of LS, imply a value of ws and u s . However (12.) also implies a value of u s , given q e and u u and the years of schooling, ζ. 11 To complete the calibration we therefore scale skilled labour income, ws LS , from GTAP so that the implied value of ws and u s are consistent with (12.). Equations (10.) and (11.) then gives the 9 Thus changes in these variable effects the level values of the benchmark, but have little impact on the comparative statics in terms of percentage deviations from the benchmark. See Harris, Robertson and Xu (2008) for a discussion of these transition issues an their implications for modeling 10 The values of the efficiency parameters are normalized to be equalt to the ratio of each countries’ per capita GDP relative to USA per capita GDP. 11 We assume a tertiary qualification takes 4 years. 10 steady state shadow price Π . 12 This data and key variables from this calibration process are summarized in Table 1 [Insert Table 1 about here] 4.2 CGE Model Calibration Overview The benchmark model for each region is calibrated to a year 2000 benchmark. The starting point for this benchmark is the GTAP V.6 data base on trade flows, intermediate usage matrices, consumption taxation, final demands and Penn World Tables data on aggregate national accounts. 13 The base 2000 tariff schedules for each country are also taken from GTAP data, for commodities. This is supplemented with data on service trade barriers from Brown et al (2001). The benchmark tariff rates for China and India are given in Table 2. China’s average import weighted tariff is 14% and India’s is 24%. The pattern of trade flows for each region is also summarized in Table 3. The benchmark is calibrated to steady state growth path where all variables are growing proportionally and prices and factor returns and the debt to GDP ratio are constant and there is balanced trade. These steady state assumptions require us to modify the source data in several ways. First we aggregate trade flows are scaled so that trade is balanced. Second industry value added flows are scaled to be consistent with the values aggregate investment spending flows for each type of capital and skilled labour. Calibration also requires choosing the parameters of: the unit expenditure functions for each of the spending aggregates; the unit revenue functions that determine the allocation of outputs across international markets, and; the unit cost functions that describe factor input choices by firms. These revenue elasticities along with the expenditure and costs function elasticities are given in the Appendix. 12 Note that this calibration procedure determines skilled labour income from data on education spending an unskilled labour income. This in turn means that GTAP data on skilled labour income must be scaled to be consistent with the steady state income implied from (1.). A similar scaling procedure is used to reconcile capital income implied by steady state relationships from investment expenditure and capital income from the GTAP data base. An alternative procedure would be to scale investment spending data to be consistent with GTAP data. 13 The GTAP data base is documented by Dimaranan (2006). 11 5. Trade Liberalization and the Skill Premium 5.1 Steady State Results for China To consider the impact of trade liberalization we present the results of unilateral trade liberalization. We begin by considering the case of China. Summary results for China are presented in Tables 4 and 5. More complete results, including sector output changes, are given in the Table 9 in the Appendix. We first consider the steady state results shown in the final column of Table 4. It can be seen that trade liberalization generates strong growth in both unskilled wages and skilled wages of 4.7% and 3.7%. Since growth in unskilled wages is a little stronger the skill premium falls by approximately 1 %. In this sense the long run effects are positive for all wage earners and have a dampening effect on wage inequality. [Insert Tables 4 and 5 about here] Second it can be seen that there is a substantial capital deepening in machinery and structural capital with the stock per unskilled worker increase by 13.2% and 10% respectively. The most dramatic result however is the large increase in stock of skilled labour, with the ratio of skilled to unskilled workers increasing by 20.6%. Thus, though the skill premium falls in the long run, this is accompanied by a very large expansion in the stock of tertiary educated labour. Table 5 decomposes the steady state changes in endogenous factors into an output effect, which shows the impact of changing sectoral composition at given factor prices, and a factor prices effect, which shows the effect of the change in factor prices at initial sectoral output levels. It can be seen that the bulk of the accumulation of capital can be attributed to the expansion of the output effect. Thus for example approximately 8.5 percentage points of the 12.8 percent increase in machinery and equipment capital can be attributed to the relative expansion of capital intensive sectors. Table 9 in the appendix shows that the sectors which expand most significantly is the Low-Tech Manufacturing sector, which grows by 24%, and Agriculture is the only traded sector to contract, falling 6.6%. Thus it is this shift toward Low-Tech Manufacturing and away from Agriculture that are the main proximate causes of the growth in capital and also the large expansion of skilled labour. 12 5.2 Transition Path Results for China Though the large increase in skilled labour supply is interesting these steady state results reveal little about the short or medium term impacts of trade liberalization. In the short to medium term, factor demands will be influenced by investment demands. These investment demands moreover may “jump” sharply in anticipation of future factor price changes and output supply changes. Consequently the short run effects are of particular interest since they are not captured in typical static models and may have a large impact on factor returns. To consider the short run dynamic responses we need to solve the perfect foresight transition path of the model. This is the path that satisfies the first order conditions and reaches the steady state as time extends to infinity. As practical mater we approximate this infinite horizon problem with a solution that reaches the steady state after 100 years. We solve the model using a modified FairTaylor method due to Wilcoxin (1988). 14 Table 4 therefore also reports the Impact, 5 year and 10 year results from the transition path for China. It can be seen that, although factor income and the capital stocks tend to expand smoothly toward their steady state values, the path of other variables is non-monotonic. In particular the real wage of skilled wages displays a hump sharp pattern reaching a peak after approximately ten years before falling to the steady state level. This generates a similar hump shaped pattern in the skill premium which rises 1.5% on impact then rises to above 2 percent before falling to -1% in the steady state. This is a result of rising physical capital accumulation which raises demand for skilled labour, and skill accumulation which eventually slows the growth of skilled wages relative to unskilled wages. Thus the initial increase the skill premium stimulates an increase in human capital investment with student enrollments expanding by 3 percent on impact and 5% by year 10. Thus the effects of trade liberalization on the skill premium are non-monotonic and change sign. Though this has implications for inequality it may be noted there is also a strong growth in unskilled wages over the entire transition. Thus, in this model, the effects of trade liberalization are very positive for wage labour and also promote a significant increase in education investment. As we shall argue below, the relative wage outcomes are largely independent of Stolper-Samuelson type mechanisms and depend instead on capital accumulation and growth. This then appears to be a very “pro-poor” aspect of trade liberalization which has received little attention in the applied modeling literature to date. Before considering this idea further we first turn to the case of India. 14 The model is solved using FORTRAN. 13 5.3 Trade Liberalization in India The results of unilateral trade liberalization for India are given in Tables 6 and 7. More detailed results are given in Table 10. Table 6 shows that Trade liberalization generates a much larger increase in output than was the case for China, with factor incomes rising by 19.1% The capital stocks of Machinery and Equipment and Structures expand by 35.7% and 28% respectively. This accumulation drives labour demand with wages of skilled and unskilled labour both rising by approximately 12% and the skill premium falls only very slightly by -0.7%. In the India case there is also a large expansion of skilled labour, with the stock of skilled labour rising by 20%. The general pattern of results in the India case, therefore, is similar to that of China. Nevertheless the magnitude of the change in India is approximately 3 times larger. [Insert Tables 6 and 7 about here] As with China, in India unilateral tariff reductions also result in a large decline in Agriculture. In contrast to china the sectors which gain the most are Durables followed by Traded Services and Low-Tech Manufacturing. Thus the pattern of growth is more even in the India case. 15 Table 7 shows that, even more so than China, the accumulation of skilled labour can be attributed to the expansion of sectors that use skilled labour intensively, and not due to substitution toward skilled labour within sectors due to factor price changes. Solving the transition path for the model yields the transition path which, for India, is reported in the remaining columns of Tables 6 and 10. It can be seen that trade liberalization induces a very large initial jump in the skill-premium of 7.7 percent - with skilled wages jumping 11.2%. Thus again the long run steady state results are completely unrepresentative of the short trem factor price effects. The path of skilled wages displays a similar hump shaped pattern to that of China, reaching a peak at approximately 11 years. Investment spending and education spend also jumps initially and much of this investment spending is funded through a large trade deficit. Even more so than the case of China, trade liberalization very large gains to both skilled and unskilled labour despite rising wage inequality The preceding results nevertheless indicate that endogenous skill accumulation provides some fresh insights into possible relationships between trade, factor prices and wage incomes. In 15 An interesting case is that of Minerals. India is a net importer of minerals and accordingly this sector contracts initially. However capital accumulation leads to a large expansion of the minerals sector in the longer term – i.e. the trade pattern responds to factor accumulation as in the Rybczynski theorem. A similar transition occurs in the Intermediate Manufactures sector. 14 particular, though the ratio of skilled to unskilled wages does fall in the long run, the results for both China and India show that the short run behavior is very different. Moreover despite rising relative returns to skilled labour and a decline in Agricultural output in the short run, trade reforms in both cases generate large increases in unskilled wages. In this sense the results show that trade induced growth is “pro-poor.” In the new steady-state the economy is more capital and skill intensive and has a substantially large non-agricultural sector. 6. Accumulation and Capital-Skill Complementarity. 6. 1 Capital-Skill Complementarity. What is the source of the rising skill premium and skilled labour investment? It was asserted above that the main reason is the strong capital accumulation effects from trade liberalization and the expansion of manufacturing sectors. In particular the base calibration assumes a moderate degree of capital-skill (K-S) complementarity, so that accumulation of physical capital tends to raise the marginal product of skilled labour by more than the marginal product of unskilled labour. In this section we consider hw the results vary under alternative assumptions regarding K-S complementarity. There is substantial evidence supporting the proposition that capital and skilled labour are complements in the production, Fallon and Layard (1975), Krusell et al (2000) and Duffy et al (2004). 16 Capital complementarity can be defined in terms of the Hicks-Allen elasticity of complementarity as ε ij = (1 / s j )(∂ ln f i / ∂ ln V j ) , where f i is the marginal product of factor i, Vj is the stock of factor j. As demonstrated by Fallon and Layard (1975), letting Ls denote skilled labour, Lu denote unskilled labour and K denote capital, K-S complementarity requires ε Ls K > ε Lu K . As discussed in the appendix, the cost function for each sector is a nested CES function with skilled labour and capital in a lower nest aggregate, and labour and other fixed factors in the upper level. That is the unit cost function for sector i, ci , is ci = ( δ r ri 1−υ + ∑ δ m wm m =1 4 1−υ 1 ) 1−σ (16.) 16 For applications using CGE models see Falvy et al and Winchester 15 where ri = ( ∑ δ k wk k =1 4 1−υ ) 1 1− v (17.) and where factors k ∈ (Machinery, Structures, Residential Capital and Skilled labour) and factors m ∈ (Unskilled Labour, Land Resources). Thus the degree of substitution is the same across the between reproducible capital factors and equal to ν. Likewise the degree of substitution across unskilled labour and the exogenously supplied factors land and resources is σ. Capital-Skill complementarity is satisfied if σ > ν . That is, an increase in the quantity of capital increases the marginal product for skilled labour relative to the marginal product of unskilled labour. To verify the role of K-S complementarity we consider different elasticities parameters in the cost functions. In our benchmark calibration we take the elasticity of substitution parameters from Krusell et al (2000). They report σ =1 2/3 for the upper nest and ν = 2/3 for the lower nest. The degree of K-S complementarity is then ε SK − ε UK = σ − ν =1 across all sectors, i=1,11. To vary the degree of K-S complementarity we hold the elasticity of substitution in the upper nest of the cost function, (16.), constant at σ= 1 2 3 and vary the value of the elasticity in the lower nest from ν ∈ (1/3, 2 3 , 1 2 3 , 3). This keeps the overall elasticity of substitution between endogenous and fixed factors constant across different cases, but allows the degree of capital-skill complementarity to vary by allowing the substitutability of each capital type and skilled labour to vary. As shown in Table 8 we label these cases High, Medium, Zero and Substitutes respectively. Note that in the case Zero we have σ−ν=0 which means that the cost functions are ordinary CES functions. Likewise in the Substitutes case we have σ−ν <0 which means that increases in capital reduce the ratio of skilled to unskilled wages. [Insert Tables 8 about here] 16 6. 2 Results. The path of the skill premium and the stock of skilled labour for each case, and each country, is represented in Figures 1. The figures illustrate that the degree of capital-skill complementarity assumed has a critical effect on the behavior of the skill premium over the transition and also the degree of skill accumulation. In the case of China we see that the impact effect on the skill premium is nearly doubled in the high case relative to the medium case. Interestingly however the skill premium still rises even when capital and skilled labour are substitutes. In that latter case, however, there is practically no effect on skill accumulation. The case of India the results are more dramatic. In the High KS case the skill premium jumps by approximately 24 percent on impact, relative to the Medium KS case of 7.7 percent. As with China the skill premium still tends to rise even when σ−ν=0 or is negative. In the case where capital and skills are substitutes the skill premium still rises initially but begins to fall after year 5. More over in this case the stock of skilled labour falls. [Insert Figure 1 about here] The difference between the results for China and India largely reflects the different amount of capital accumulation that occurs in each case. To see this note that the results for China and India in the Zero KS case are quite similar in terms of the size of the impact. When even a small amount of capital-skill complementarity is allowed however, the much large quantity of capital accumulation in India, relative to China, generates much larger increases in the skill premium. In this way the results also distinguish between the traditional “trade impacts” of trade liberalization on the skill premium and the effects that come from capital accumulation and investment demands. By “trade impacts” we think of as the effects of commodity prices changes on factor prices and industry outputs with given capital stocks. These traditional trade impacts can be identified by the path of the Zero KS complementarity case, and especially the impact effect when capital stocks are constant. The results thus show that the quantitative impact of capital accumulation on the skill premium, under capital-skill complementarity, greatly dominates all the other impacts. In particular over the first 10 years the capital-skill complementarity has a large impact on the skill premium relative to the Zero complementarity case. The idea that capital-skill complementarity might be important in understanding changes in wage inequality is not new. Studies by Tyers and Yang (2000) Krusell et al (2000) and Winchester, and Greenaway, (2005), have show that capital-skill complementarity offers a compelling explanation 17 for aspects of growth in developed market economies, such as the rising skill premiums and capital deepening. These studies were conducted in the context of a prevailing view that trade liberalization only has a small effect of skill premiums in developed economies. Hence they focus on the role of factor augmenting technical change as the source of capital accumulation. The preceding results show, however that with respect to developing countries, trade may induce a large amount of capital deepening with equally large consequences for skill premiums. This stands in contrast with practically all of the trade-wage literature which tends to focus on StolperSamuelson effects and technological trade as explanations for changes in the relative returns to skilled labour. 17 6. Conclusion An extensive literature on the effects of trade linearization exists with respect to its impacts on poverty rates and wage inequality. We have argued that the standard treatment of these issues takes factor supplies as given and, hence, ignores the effects of trade on returns to education and the incentives for skill accumulation. Using a multi-sector growth model we simulate the effects of unilateral tariff reductions in China and India. These countries are of interest not only because of their size, but also because both have experienced rapid growth in conjunction with rising wage inequality and rising returns to education. Our results suggest that in both countries trade liberalization generates this type of skill biased growth. Specifically unilateral tariff reductions raise wage inequality in the short to medium term and induce a large quantity increase the stock of skilled labour in the medium to long run. We find that the effects of trade on wage inequality or non monotonic and change sign, rising on impact and becoming negative in the very long run. The changes in the skill premium are also associated with rising wages of unskilled workers. Thus despite rising wage inequality, trade liberalization was shown to generate strong positive labour market outcomes for both skilled and less skilled workers. We showed further that the cause of these changes in skill premiums and human capital investment is almost entirely due to capital-skill complementarity. Though previous studies have highlighted the importance of capital skill complementarity in conjunction with capital augmenting technical Stokey (1996) and Cragg and Epelbaum (1996) make a similar argument with with respect to capital inflows. They do not model trade explicitly but assume that capital market liberalization will result increases in installed capital. 17 18 change, there has be very few studies of the effects of capital deepening following trade liberalization. Our simulations, however, show that these growth effects of trade liberalization have consequences for the skill premium that appear to be the most important factor determining the relative wages of skilled and unskilled workers and its critical in determining the extent of skill accumulation. 19 References Abrego L and Whalley J. “The Choice of Structural Model in Trade-Wages Decompositions”, Review of International Economics, 8, 3 462-77, 2000. Abrego, Lisandro, Edwards, and T. Huw (2002) “The relevance of the Stolper-Samuelson theorem to the trade and wages debate” Centre for the Study of Globalisation and Regionalisation (CSGR) Working Paper No 96/02 , University of Warwick Adelman, Irma, and Robinson, Sherman. 1978. 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Exp (fraction of GDP) 2 Years of tertiary schooling Education subsidy Implied Skill Premium LS/P H/LS p e* y e China India ζ s w s / wu 0.02 0.35 0.80 4.00 0.57 2.55 0.02 0.35 0.99 4.00 0.77 2.32 1. Source of Skilled labour stocks: China and India Barro and Lee (2001). Student enrolment, Statistical Abstract of the USA. 2. Sources of education expenditure data: China OECD (2002), Online Education database, Table B2.1a, B2.1b. available at http://www.oecd.org/education/database; India, UNESCO Institute for Statistics, http://www.uis.unesco.org/. Table 2: Tariff Rates China's tariff on USA Agriculture Minerals Low-Tech Manufacture Intermediate Manufacture Durables Traded Services 0.55 0.06 0.10 0.10 0.11 0.19 China's tariff on ROW 0.28 0.02 0.13 0.12 0.15 0.19 India's tariff on USA India's tariff on ROW 0.24 0.20 0.20 0.31 0.21 0.13 0.47 0.16 0.25 0.30 0.27 0.13 Table 3: Trade Shares China Import Shares Agriculture Minerals Low-Tech Manufacture Intermediate Manufacture Durables Traded Services 0.05 0.06 0.30 0.21 0.22 0.15 China Export Shares 0.04 0.02 0.46 0.13 0.28 0.06 India Import Shares 0.08 0.20 0.04 0.23 0.26 0.19 India Export Shares 0.11 0.02 0.25 0.20 0.21 0.20 1 Table 4: Trade Liberalization In China Summary (% change) Impact Total Factor Income Skilled Wages Unskilled Wages Skill Premium Skilled Workers, Ls Unskilled Workers, Lu Students Machines per unskilled worker Structures per unskilled worker Residential per unskilled worker 1.4 3.2 1.6 1.5 0.0 0.0 3.0 0.0 0.0 0.0 Year 5 3.0 5.3 3.0 2.2 1.1 -0.1 4.2 4.8 1.7 -1.4 Year 10 4.0 5.7 3.5 2.1 2.3 -0.1 5.0 7.9 3.6 -2.2 SS 6.7 3.7 4.7 -1.0 7.9 -0.2 7.9 13.0 10.2 -0.1 Table 5: Decomposition of Steady State Factor Supply Changes in China Total Change % Machinery and Equipment Structures Residential Capital Skilled Labour Unskilled Labour 12.8 10.1 -0.2 8.0 -0.2 Output Effect Factor Intensity Effect 4.3 3.0 1.1 1.1 -4.8 8.5 7.1 -1.3 6.9 4.6 2 Table 6: Trade Liberalization In India Summary (% change) Impact Total Factor Income Skilled Wages Unskilled Wages Skill Premium Skilled Workers, Ls Unskilled Workers, Lu Students Machines per unskilled worker Structures per unskilled worker Residential per unskilled worker 2.4 11.2 3.2 7.7 0.0 -0.1 8.0 0.1 0.1 0.1 Year 5 7.2 14.7 6.4 7.8 2.9 -0.2 11.3 11.1 4.3 -1.7 Year 10 10.9 15.6 8.3 6.8 6.3 -0.2 13.2 20.5 9.3 -2.1 SS 19.1 11.5 12.3 -0.7 20.0 -0.6 20.0 35.7 28.0 6.7 Table 7: Decomposition of Steady State Factor Supply Changes Total Change % Machinery and Equipment Structures Residential Capital Skilled Labour Unskilled Labour 35.0 27.4 6.4 20.2 -0.6 Output Effect Factor Intensity Effect 9.1 5.6 1.6 1.2 -12.5 25.9 21.9 4.8 19.0 11.9 3 Table 8: Sensitivity to capital-Skill Complementarity Case High Medium (base case) Zero (CES) Substitutes σ 1 2/3 1 2/3 1 2/3 1 2/3 ν 1/3 2/3 1 2/3 3 σ−ν 1 1/3 1 0 -1 1/3 4 Appendix Tables Table 9: Results – Trade Liberalization In China (% change) Impact Real Factor Income, Y Real GDP Real Govt and Pvt Consumption Investment in Machinery and Equipment Investment in Structures Investment in Housing Real return to Machine and Equipment Real return to Structures Real return to Housing Real Skilled wages Real Unksilled wages Land rents Resource rent Skill Premium Education Output relative to GDP Ls/Lu Relative Price of Education (Pe / wu ) Exchange Rate (Traded v Non Traded) Terms of Trade Openness Trade Volume China - USA Trade Volume China - ROW Trade Surplus/GDP (Level) Sector Outputs Agriculture Minerals Lowtech Intermediate Manufacture Durables Traded Sevices Construction Non Traded Services Public Housing Education 1.4 -1.9 -0.1 5.0 2.5 -6.3 3.3 2.3 -1.1 3.2 1.6 -2.7 -1.0 1.5 1.6 0.0 -0.6 -0.9 -7.5 30.6 21.1 19.3 -2.7 Year 5 3.0 -0.3 -2.2 4.9 2.7 -6.5 1.4 3.9 -3.5 5.3 3.0 -4.3 -0.4 2.2 1.1 1.1 -1.1 -1.1 -6.7 31.8 28.9 24.5 0.2 Year 10 4.0 0.7 -1.5 3.9 2.6 -6.5 -0.7 3.4 -1.7 5.7 3.5 -3.5 0.2 2.1 0.9 2.4 -1.6 -1.2 -6.8 31.0 30.1 25.5 0.4 SS 6.7 3.3 1.0 3.6 3.1 -6.5 -1.9 0.0 0.1 3.7 4.7 -1.4 1.4 -1.0 1.1 8.1 -3.2 -1.1 -7.2 28.5 31.0 26.6 0.0 -7.4 1.1 13.8 -1.1 0.3 -1.6 1.4 1.0 0.5 -0.4 3.0 -9.1 3.3 21.9 2.0 4.2 -0.7 2.7 2.1 1.2 -3.5 4.2 -8.5 4.8 23.2 3.5 5.7 0.8 3.9 3.3 2.3 -3.3 5.0 -6.6 9.3 24.2 7.1 9.4 4.8 7.5 6.4 5.3 -1.3 7.9 5 Table 10: Results - Trade Liberalization In India (% change) Impact Real Factor Income, Y Real GDP Real Govt and Pvt Consumption Investment in Machinery and Equipment Investment in Structures Investment in Housing Real return to Machine and Equipment Real return to Structures Real return to Housing Real Skilled wages Real Unksilled wages Land rents Resource rent Skill Premium Education Output relative to GDP Ls/Lu Relative Price of Education (Pe / wu ) Exchange Rate (Traded v Non Traded) Terms of Trade Openness Trade Volume India - USA Trade Volume India - ROW Trade Surplus/GDP (Level) Sector Outputs Agriculture Minerals Low-Tech Intermediate Manufacture Durables Traded Services Construction Non Traded Services Public Housing Education 2.4 -2.7 5.0 8.6 6.1 -8.7 3.8 2.1 4.0 11.2 3.2 -2.6 -17.4 7.7 5.5 0.1 0.2 -2.9 -10.9 52.5 27.0 31.6 -9.5 Year 5 7.2 1.8 -4.2 11.5 7.3 -9.3 1.6 7.4 -5.8 14.7 6.4 -7.8 -1.2 7.8 3.8 3.1 0.1 -4.3 -9.0 58.9 62.1 46.2 0.6 Year 10 10.9 5.3 -1.7 9.2 6.6 -9.8 -3.0 7.7 -2.6 15.6 8.3 -6.3 3.8 6.8 2.1 6.6 -0.6 -4.5 -9.4 57.1 69.1 50.7 1.3 SS 19.1 13.0 6.7 7.4 7.2 -10.6 -5.6 0.0 0.1 11.5 12.3 -1.5 17.6 -0.7 0.8 20.7 -3.3 -3.4 -10.6 50.6 73.2 57.0 0.0 -8.5 -56.1 20.2 -11.7 3.9 5.0 3.3 3.1 0.6 5.2 8.0 -14.1 5.9 31.0 8.3 50.9 12.1 8.2 4.4 -1.6 -2.9 11.3 -13.0 21.5 32.0 15.9 63.4 19.0 12.0 8.3 0.9 -1.8 13.2 -9.3 74.3 32.6 29.7 75.5 32.7 22.0 18.0 8.5 4.6 20.0 6 Figure 1: Alternative Capital−Skill Complimentarity Scenarios (i) Skill Premium − China 25 High Medium Zero Substitutes 25 (ii) Skilled Labour Stock − China High Medium Zero Substitutes 20 20 15 Percent Change Percent Change 0 10 20 Years 30 40 50 15 10 10 5 5 0 0 −5 −5 0 10 20 Years 30 40 50 (i) Skill Premium − India 25 High Medium Zero Substitutes 25 (ii) Skilled Labour Stock − India High Medium Zero Substitutes 20 20 15 Percent Change Percent Change 0 10 20 Years 30 40 50 15 10 10 5 5 0 0 −5 −5 0 10 20 Years 30 40 50