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The Endogenous Skill Bias of Technical Change and Inequality in Developing Countries

Author(s):
Alberto Behar
Published Date:
February 2013
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I. Introduction

Over the past generation, developed and developing countries alike have experienced greater inequality amidst unprecedented increases in educational attainment and trade openness. This is puzzling; an increase in the supply of skilled labor should reduce its price, and trade liberalization in developing countries was supposed to have the same effect. Such developments concern policymakers because, although measures to promote openness and increase human capital are thought to promote growth, they can be politically and financially costly, and may not engender inclusive growth.

The Middle East and North Africa (MENA) region has experienced an exceptional expansion in education attainment, contributing nine of the world’s top 20 countries by this measure (Campante & Chor, 2012). The region is still relatively isolated from the global economy by global standards, but it has become more open over time (Behar & Freund, 2011). The MENA region has been no exception to the global trend of rising inequality, and this issue is particularly relevant at present. It is an established narrative that inequality, unemployment and lack of opportunity contributed to the uprisings and regime change beginning in 2011 known as the Arab Spring. More importantly at this juncture for the Arab Countries in Transition (ACTs), the region has been more prone to democratic reversals than others, and there is evidence that higher inequality makes such reversals more likely (Kapstein & Converse, 2008; O’Sullivan & Kersley, 2011). The question of why skill supply and increasing openness have not reduced inequality is therefore particularly pressing for the region.

One explanation offered for the period of increasing income inequality worldwide is that it has been a time of unmatched technological progress and capital deepening that increased the relative demand for skilled workers. The argument finds support in the pervasive nature of such inequality patterns (Berman et al, 1998) and developments like the Information and Communications Technology revolution (Katz & Autor, 1999). However, the capital deepening and Total Factor Productivity (TFP) growth were insufficient to account for the observed changes in inequality (Berman & Machin, 2000; Acemoglu, 2002). Furthermore, the correlation of inequality and the quantity of skills is close to zero after allowing for exogenous trends. Moreover, the conditional correlation between trade and inequality is zero or positive in Middle-Income Countries (MICs) and negative in Low-Income Countries (LICs). The sign of the correlation would be consistent with extensions to the Heckscher Ohlin Stolper Samuelson (HOSS) predictions if we accept that many MICs are skill-abundant relative to the global average, but it appears that the standard HOSS mechanism alone makes a relatively limited contribution to the impact of trade on skill premia (Chusseau, Dumon & Hellier, 2008).2

What else can help explain these patterns? This paper presents endogenous skill-biased technical change (SBTC) in developing countries as a contributing factor. It extends the conceptual framework of directed technological change in advanced countries introduced by Acemoglu (1998) and others. An essential part of the proposed explanation is that technological change is not necessarily skill-biased and can favor either skilled or unskilled workers. The decision to acquire a technology that favors skilled workers depends on that technology’s relative profitability. The profitability of a skill-biased machine depends on the market for that machine, which in turn depends on the availability of skilled workers and on the price of the good produced with that machine; the price can rise or fall after opening up to international trade, depending on the skill endowment. While many papers emphasize the endogeneity of the quantity of technology acquisition to prices and other factors (Burstein, Cravino & Vogel, 2012), which has been skill-biased, our emphasis here is on the endogeneity of the extent of the skill-bias.

The observation that the skill-bias of technological change responds to endowments and other local conditions is consistent with countries adopting appropriate technology (Atkinson & Stiglitz, 1969) and is supported by formal analysis (Caselli & Coleman, 2006) and by examples from history. Treating the bias of technical change as endogenous allows one to predict that a rise in the supply of skilled labor can induce SBTC and hence increase the relative demand for skilled labor. A MIC that is skill-abundant relative to the world and opening up to trade would experience a rise in the price of skill-intensive products and would hence have an incentive to acquire skill-biased technologies. Conversely, skill-scarce LICs would experience technological change that favors unskilled workers and would see a fall in the skill premium after opening up to trade. In addition, developing countries tend to acquire their technologies from developed countries. The nature of R&D in rich countries is also endogenous, but has been skill-biased in recent times. This affects the skill-bias of technologies available to poorer countries and thus tends to make developing countries acquire skill-biased technologies irrespective of their local conditions (Berman & Machin, 2000). Globalization in a broader sense – liberalization of trade in goods and access to foreign technology – therefore plays an important role in the extent to which technical change is skill-biased.

Section II presents evidence on the relationships between inequality, the skill endowment, and trade openness before presenting evidence that the skill-bias of technical change is endogenous in developing countries. Section III presents a formal model of the arguments. In line with prior work on developed countries (Acemoglu, 2002a; Acemoglu, 2003; Kiley, 1999), we use an endogenous growth model with two types of workers: skilled and unskilled. Our emphasis on developing countries means that, instead of incurring R&D costs, firms pay for the monopoly licence to acquire a technology and import machines using that technology from abroad in order to sell them domestically. An implication of this is that the amount of induced SBTC, and hence of inequality, is a function of the rate at which more advanced technologies get more expensive. Furthermore, developing countries are often small and have limited or no impact on the relative price of skilled and unskilled products. This also has quantitative implications for the extent of SBTC and wage inequality responses.

Section IV deploys the model. First, it shows formally how SBTC in developed countries leads to SBTC and higher wage inequality in developing countries. Second, it shows that the HOSS effects on inequality in a skill-abundant small economy opening up to trade are amplified by induced technical change. In a small open economy (SOE), a rise in skill supply unequivocally increases the skill premium. Third, when two large developing economies open up to trade, there are again amplified effects on inequality relative to HOSS, but these are smaller than in the SOE case. Furthermore, consistent with prior work on the closed economy, increased skill supply leads to higher inequality if the elasticity of substitution between skilled and unskilled labor is sufficiently high. Section V concludes with some policy implications.

The emphasis here is on a unified framework for thinking about endogenous SBTC in developing countries and its part in explaining the pervasive increases in inequality, the seeming lack of a correlation with the quantity of skills, and the differential effects of trade in MICs and LICs. However, we recognize that this emphasis comes at the cost of excluding many legitimate and important individual explanations.

II. Empirical background

This section describes patterns of wage inequality and then describes three factors that affect the extent to which technical change is skill-biased.

A. Patterns of Income Differentials

This section documents three stylized yet potentially surprising patterns for inequality in developing countries. The linear projections in Table 1 summarize these patterns. First, inequality increased for all developing countries over time. Second, and surprisingly, there is no correlation between inequality and the proportion of the skilled population. Third, the correlation between inequality and openness appears to be negative for LICs but zero or positive for the Lower-Middle-Income Countries (LMICs) and Upper-Middle-Income Countries (UMICs).

Table 1:Projections of Inequality on Education, Trade and Time for Developing Countries
Dependent Variable:EHII1Skill Premium2
Countries:LICLMICUMICLIC, LMICUMIC
Skilled Proportion-0.204*0.0607-0.05770.0668**0.027
Standard error[0.0980][0.0706][0.0904][0.0298][0.0362]
Trend0.1090.210***0.328**
Standard error[0.0945][0.0701][0.135]
Trade/GDP share (Goods)-6.312**0.03771.898
Standard error[2.158][1.166][3.066]
Open Dummy0.02310.207*
Standard error[0.0502][0.114]
Constant-143.5-375.1**-616.8**0.398***0.292***
Standard error[193.5][137.9][269.3][0.0353][0.0658]
R20.360.230.270.180.20
N3862602617
Countries1520152617

EHII is index of household income inequality and projected on the log of the proportion of the population who have completed at least primary school, the log of goods trade as a share of GDP, and a five-year trend term using country fixed effects in a panel. Regressions are run for Upper-Middle-Income (UMIC), Lower-Middle-Income (LMIC) and Low-Income (LIC) countries.

Skill Premium is the log of the skill premium for people with at least primary school, calculated based on Mincerian earnings estimates. It is projected on the skilled proportion and an openness dummy for countries with high trade/GDP shares. Projections are run for LICs/LMICs and UMICs. *, ** and *** denote significance at 10%, 5% and 1% based on robust standard errors in brackets.

EHII is index of household income inequality and projected on the log of the proportion of the population who have completed at least primary school, the log of goods trade as a share of GDP, and a five-year trend term using country fixed effects in a panel. Regressions are run for Upper-Middle-Income (UMIC), Lower-Middle-Income (LMIC) and Low-Income (LIC) countries.

Skill Premium is the log of the skill premium for people with at least primary school, calculated based on Mincerian earnings estimates. It is projected on the skilled proportion and an openness dummy for countries with high trade/GDP shares. Projections are run for LICs/LMICs and UMICs. *, ** and *** denote significance at 10%, 5% and 1% based on robust standard errors in brackets.

The first three columns of Table 1 distinguish between LICs, UMICs, and LMICs using an index of household income inequality; this is because there is a panel with a comfortable number of observations for each country income group. However, the formal model refers to the skill premium, namely the ratio of skilled to unskilled wages. Goldberg & Pavcnik (2007) note that observed changes in the skill premium are generally but not always reflected in other measures of income inequality. Therefore, columns 4 and 5 describe empirical relationships that more closely match the theory by representing inequality by the skill premium, but this is at the cost of limited observations in a single cross-section of data. In particular, there are insufficient observations for a separate linear projection for LICs. The method and data are described in the Appendix. From this foundation, we discuss the results in the context of other studies of wage patterns.

Inequality Over Time

It is well documented that there has been a rise in inequality in developed countries over the last generation (Chusseau et al, 2008). Table 1, which refers to developing countries, has positive trend terms representing five-year increments from 1980 to 2000. The coefficient is lower for LICs and, of the five individual countries that experienced a fall, three were LICs. Of the 40 countries with sufficient long-range data, 34 experienced increases in inequality over the period, including all the MENA countries (Table 2) and one experienced no change.

Table 2:Increase in Inequality, MENA, 1980s to 2000s
CountryChange in EHII1
Algeria7.4
Egypt9.2
Iran9.7
Jordan2.2
Syria6.6
Tunisia12.1
Source: Behar (2011).

EHII is a GINI-like index of inequality between 0 and 100.

Source: Behar (2011).

EHII is a GINI-like index of inequality between 0 and 100.

As documented in Meschi & Vivarelli (1999) and Behar (2011) with data from the 1960s, the increase lagged that in developed countries and became rapid in the mid-1980s. Behar (2009) presents similar patterns over time based on the skill premium in a limited set of countries. Berman & Machin (2000) also document how the rise in inequality has been pervasive and that the rise in developing countries lagged that in the rich world. This suggests that some driver external to the characteristics of each developing country is pushing inequality upwards, and we will present the case that this is skill-biased R&D in developed countries. While openness to new technology is seen as a driver of long-term growth (Barro & Sala-i-Martin, 2004), it may not be an inclusive form of growth.

Inequality and Schooling

The rise in inequality has coincided with a rise in educational attainment in developing countries. For example, the median proportion of people who completed primary school rose increased by approximately half, and 39 out of 40 countries increased their average years of schooling between 1965 and 2000 (Behar, 2011). The Middle East and North Africa (MENA) is exceptional in terms of educational attainment, at least in more recent times. As noted by Campante & Chor (2012), the nine MENA countries in Table 3 were among the top 20 out of almost 150 countries when measured by the increase between 1980 and 2010 in the average years of schooling attained by the population aged 15 and over.

Table 3:MENA Countries with Large Rises in Educational Attainment, 1980–2010
CountryGlobal RankChange in

schooling

years
Iran35.3
Algeria45.2
UAE55.2
Bahrain84.7
Jordan94.7
Libya104.6
Egypt144.4
Saudi Arabia184.1
Tunisia194.1
Source: Campante & Chor (2012), based on Barro-Lee dataset version 1.2.
Source: Campante & Chor (2012), based on Barro-Lee dataset version 1.2.

Table 1 suggests expanded educational attainment has no robust association with household income inequality or wage premia. These results, like those in Caselli & Coleman (2006), use the completion of primary school as the measure of attainment. Behar (2009, 2011) documents multiple specifications using cross-sectional, panel and time-series data that reveal little or no correlation between measures of income differentials (like wage premia or household inequality) and measures of educational attainment (like the average years of schooling and the proportions of skilled labor, where skilled labor can be defined at various cutoff levels of education), regardless of the country’s stage of development.

The absence of a correlation between income differentials and the quantity of skills is cause for concern given that education is often associated with economic growth but also seen as a way to reduce inequality. By reducing the relative scarcity of skilled labor, education is supposed to reduce its relative price.3 However, there are a number of reasons why this may not be so,4 including the possibility that skill supply creates its own demand.5 As has been argued by Acemoglu (1998) and Kiley (1999) for developed countries, we will argue that increased skill supply makes the market for technologies that favor skilled workers more attractive and hence induces SBTC in developing countries.

Inequality and Trade

Since World War II, the share of goods traded internationally has risen dramatically. While initially dominated by trade between industrialized countries, more recent surges have been a South-South trade phenomenon (Gourdon, 2011; Hanson, 2012). Table 4 shows that most MENA countries also experienced an increase in trade openness since 1980, while Behar & Freund (2011) show that openness increased in the region since at least the mid 1990s.

Table 4:Change in Trade Openness, MENA, 1980–20101
CountryPercent of GDPCountryPercent of GDP
Algeria4Morocco22
Egypt-16Oman1
Iran19Qatar-6
Jordan16Saudi Arabia0
Kuwait-22Sudan7
Lebanon-53Syria7
Libya14Tunisia29
Mauritania45UAE58
Source: IMF World Economic Outlook database.

Goods Trade to GDP Ratio.

Source: IMF World Economic Outlook database.

Goods Trade to GDP Ratio.

Table 1 suggests that LICs that opened their economies to trade more had a smaller rise in inequality. However, LMIC exports have no relationship with inequality. The experience among UMICs varied, as shown by the large standard errors, but those that increased trade more tended to have a larger rise in inequality. Other results, available on request, show that there was no correlation for the developing countries as a whole, which is consistent with Meschi & Vivarelli (2009). Furthermore, Table A1 in the Appendix shows similar results using alternative measures of trade openness.

The linear projections on cross-section data (Table 1, columns 4 and 5) use an alternative measure of openness for robustness, namely the Sachs & Warner (1999) dummy distinguishing between more- and less-open countries. The results suggest that more open UMICs tended to have higher wage premia, but that more openness in LICs and LMICs is not associated with wage premia.

The standard two-country Heckscher Ohlin Stolper Samuelson (HOSS) model predicts a fall in the wage premium in developing countries. Not only is standard HOSS inconsistent with Table 1, it is also inconsistent with the case study evidence, mostly from MICs, that openness increased wage inequality (Goldberg & Pavcnik, 2007). However, modified versions of HOSS allow for more than two levels of skill abundance and possibly a continuum, which would permit UMICs to be skill-abundant relative to LMICs and LICs but not relative to advanced countries. In this setting, HOSS would predict a fall in the relative price of skilled goods and in the skill premium for those few but populous countries abundant in unskilled labor and the opposite effect for the rest. This is consistent with Table 1 and with proposals by others that the opening up of the Chinese economy in particular made many countries skill-abundant relative to the global average. Trade liberalizations in Latin America, which tended to be associated with increased skill premia there, coincided with the integration of many LICs into the global economy (Davis, 1996; Wood 1997; Wood & Mayer 2011). Fajnzylber and Fernandes (2004) find that increased levels of international integration were associated with increased demand for skilled labor in Brazil but with decreased demand in China. Tunisia’s proximity to Europe makes it trade a lot with that continent, rendering it relatively abundant in unskilled labor; recent evidence indicates that increased trade led to a fall in inequality there (Ben Salha, 2012).

SBTC of the form discussed has long been advanced as an alternative explanation6 to trade for the rises in inequality in both developed and developing countries alike. However, there is evidence that trade and technological change are themselves linked in developing countries.

There are a number of mechanisms for trade to affect technological change but we will emphasize how trade can affect the skill-bias of the technology imported. In particular, trade liberalization changes the relative price of skilled and unskilled products and hence the relative profitability of producing skill-biased technologies (Acemoglu, 2003).

B. Drivers of Skill-biased Technical Change

Intrinsic versus Endogenous Skill Bias

Why has technical change favored skilled labor? One response is that technical change is skill-biased by nature because machines complement skills (Griliches, 1969) or because new technologies require skilled people to implement them (Nelson & Phelps, 1966). Accordingly, a large fall in the price of machines or an exogenous boost to technological growth (Katz & Autor, 1999) led to increased relative demand for skilled labor. However, as Acemoglu (2002b) and Berman & Machin (2000) argue, there has not been enough TFP or capital deepening to account for the skill-bias generated, and these can only be part of the explanation.7

An alternative response is that technological change is not always skill-biased and can favor one factor over another, depending on market incentives. This was proposed by Hicks (1963) and by historians Schmookler (1966) and Habbakuk (1962). In modern endogenous growth models, the development of technologies follows a profit motive and it naturally follows that the choice to research or implement a technology that improves the productivity of skilled workers – as opposed to the productivity of unskilled workers – depends on the relative profitability of the two technologies. There are many examples from history in which technological change favored unskilled workers in developed countries (Rosenberg, 1969; Hayami & Ruttan, 1970; Mokyr, 1990) and this study will shortly discuss examples from developing countries.

Determinants of the Skill Bias of Technology

From the perspective of a developing country, the acquisition of technologies favoring skilled workers as opposed to technologies that favor unskilled workers depends on the relative cost of the two types of technology and the relative revenues they generate. Relative costs are based on the relative availability of skill-biased technologies, which in turn is based on the skill-bias of R&D in developed countries. Skill-biased machines yield more revenues if there are more skilled people available to use them and if the relative price of skill-intensive goods is higher.

Most countries do not develop their own technologies but acquire them from abroad (Eaton & Kortum, 2001). The top five country sources of patents account for 84 percent of those worldwide but developing countries account for only 3 percent (Behar, 2011). Savvides & Zachariadis (2005) find empirically that developing countries do not undertake their own R&D but rely on foreign technology transfer. According to Eaton & Kortum (2001), those countries engaged in designing the machines tend to produce and export them. This implies that the skill-bias of R&D in the developed world would affect the skill-bias in the developing world.

However, countries do not absorb new methods automatically; they consider domestic factor markets before choosing appropriate technologies. For example, Knight (1979) notes that capital can replace skilled or unskilled labor, and describes how the introduction of the color bar restricted the supply of skills in South Africa and may have led to capital substituting for skilled workers. When the color bar was relaxed, a large degree of substitution of machines for unskilled labor took place. Fransman (1985) presents case study evidence of developing countries adapting overseas technologies to local conditions. Esposito & Stehrer (2009) find econometric evidence consistent with a positive relationship between the initial relative quantity of skilled labor and subsequent SBTC.

International trade can also affect technological change through a number of mechanisms.8 Our emphasis here is on how this can affect the skill-bias in developing countries. We have noted that technology is often embodied in machines. It follows that trade liberalization would decrease the cost of those machines (Krusell et al., 2000; Burnstein et al., 2012). For this to be a mechanism for openness to lead to SBTC, as proposed in the skill-enhancing trade hypothesis (Robbins, 1996, 2003), opening up needs to lead to a change in the relative access to skill-biased technologies as opposed to all technologies. Furthermore, differential impacts in wages across developing countries would need to rely on differential capacity in absorbing technologies (Meschi & Vivarelli, 1999).

Caselli & Wilson (2004) document that technology is embodied in imported machines, but also find evidence that the skill-bias of machines imported varies according to local conditions including human capital. Similarly, Acemoglu & Zilibotti (2001) speak of multinational corporations making technologies available to their various developing country subsidiaries according to the relative availability of skilled workers. Moreover, they cite an example of Kenya using the hammer mill to grind maize rather than the roller mill because of abundant unskilled labor. This is consistent with the previous discussion on appropriate technologies but also consistent with globalization leading to different degrees of skill bias depending on local conditions.

Almeida (2009) finds evidence that increased openness in Asia led to SBTC in the MICs but not the LICs, offering the prevailing plausible interpretation that this is because LICs do not have the capacity to absorb new technologies. However, it is also legitimate to explain the difference using factor abundance. Endowments affect the pattern of specialization after trade such that MICs are expected to export relatively skill-saving goods to advanced countries and relatively skill-intensive goods to other developing countries. Supporting evidence comes from Gourdon (2011), who finds that technological change in developing countries was more skill-biased in sectors engaged in South-South trade than in South-North trade. Therefore, while trade with advanced countries may grant more access to technology than trade with other developing countries (Meschi & Vivarelli, 2009), there is also evidence that the degree of skill-bias is driven by HOSS patterns.

Advanced countries direct their R&D efforts according to local conditions, which implies that there was SBTC induced by trade and by skill supply (Chusseau et al, 2008; Kiley, 1999; Acemoglu, 1998), which partly explains why technical change has been skill-biased in advanced countries. Putting this advanced country SBTC together with the tendency for developing countries to import foreign technologies, the implication is that technology adoption in developing countries is influenced by that in advanced countries. As argued by Berman & Machin (2000:3), “…developing countries must be choosing from a menu of best practices that includes an ever-increasing proportion of skill-biased technologies.” Further support comes from Berman, Bound & Machin (1998), who find evidence for pervasive global SBTC and that the same industries experiencing SBTC in the developing countries in the 1980s were those experiencing it in the developed countries in prior decades. However, developing countries do not absorb technology by osmosis and, as we have argued, the skill-bias depends on developing country characteristics, including their endowments. A higher skill endowment induces SBTC, which may be why there is no correlation between the quantity of skills and inequality. Recent trade liberalization may have raised the profitability of skill-biased technologies in skill-abundant developing countries and had the opposite effect in skill-scarce developing countries.

III. Theory

This section sets up the model. The core components are skilled and unskilled intermediates, which are used to make a homogenous final good. Skilled intermediates are produced by intermediates producers, who employ skilled labor using numerous varieties of machines. Unskilled intermediates are made analogously with unskilled workers and different varieties of machines. The relative wages of skilled and unskilled workers are a function of their relative physical productivity, which depends on how many machine varieties they have, and on the relative prices of skilled and unskilled intermediates, which may either depend on their relative supply or on the exogenous global price ratio.

The relative availability of machines is determined in the technology market, and depends on the decisions of prospective technology licence holders. Prospective licence holders base their decisions on the costs of those licences, which are a function of global R&D, and on the potential revenues derived from those licences, which are a function of the quantity of skilled labor relative to unskilled labor and of the price of skilled intermediates relative to unskilled intermediates.

Equilibrium in the technology market requires that the relative cost of acquiring a skill-biased technology be equal to the relative value of that technology. By bringing together numerous drivers of SBTC, the presentation in this section attempts to economize on notation, derivation and qualifications of conditions for legitimate equilibria. More details can be found in Behar (2007) and are available on request.

A. Production

Total output of final goods is a CES aggregate of two types of intermediate, as described by the linearly homogeneous technology:

Final output is produced by perfect competitors using two intermediate inputs purchased from intermediates producers. The price of final output is unity. ϵ > 0 is the finite elasticity of substitution between intermediate inputs. Individual producers take the price of final output and intermediate input prices as given before choosing their optimal quantities of intermediates.

This study employs a variety expansion model (Romer, 1990). As in Kiley (1999), intermediates are produced by i perfectly competitive firms, where ys uses skilled labor Ls and Ts different machines while yu uses unskilled labor Lu and Tu different machines. Specifically, yits=(Lits)1αΣj=1TtsXijtα and yitu=(Litu)1αΣj=1TtuZijtα. Xijt is machine input of type j used by firm i at t. It is the quantity of each of Ts machines (capital) that complement skilled labor. Similarly, Zijt is the quantity of each of Tu machines complementing unskilled labor.

The price of final output is unity. Firms are profit maximizers and the quantity of each skilled machine demanded by each intermediates producer is such that the marginal product of the machine equals its price. Firm-level demand for each type of skilled machine is Xijt=(αptspjtX)11αLits.

The price of each skilled machine, pjtX, is set by the firm holding the licence for that type of machine. The technology importer must receive ex post profits sufficient to cover the ex ante licence cost. We describe technology acquisition below but, once the fixed cost of acquiring the licence has been incurred, it costs 1 unit of Y, which has a price of 1, to import each machine. The equation for firm-level demand can be used to show the own-price elasticity of demand is 11α for all machines of any type. Therefore, each monopolist sets a profit maximizing price of 1α for all j, t. For the economy as a whole, we can condition demand for skilled machines on the quantity of skilled labor. Because final goods are produced using a constant returns to scale technology, equilibrium economy-wide demand for each skill-biased intermediate j must be:

Similarly, economy-wide demand for each unskilled machine is:

Economy-wide output of skilled and unskilled intermediates is:

There are constant returns to increases in the variety of inputs; as long as Ts or Tu rise, ys or yu will rise. The model literally considers Ts and Tu as the number of different types of machines a firm can use, but can also be thought of as the technical complexity of the firms’ production processes (Barro & Sala-i-Martin, 2004).

B. Prices and Wages

Each final output producer l chooses its ratio of intermediates such that:

In a closed economy, demand equals supply of each intermediate input. For the economy to be in equilibrium, intermediates must have prices ps and pu such that.

For a closed economy, a rise in supply of one of the inputs relative to the other would necessitate a relative price adjustment. After substituting from (2) and (3), combining (4) and (6), we can write

where TTsTu,LLsLu and σ = ϵ + α – ϵα is the elasticity of substitution between skilled and unskilled labor. An exogenous change in the relative skill supply would lead to a rise in the relative quantity of ys produced. If the ratio of Ts to Tu were to change, this too would necessitate an adjustment in the relative price. Equation (7) shows a negative relationship between the relative price of the skill-intensive good on the one hand and the relative number of skilled technologies on the other.

In a small open economy, prices are set internationally. Final goods producers still choose intermediates according to equation (5) but their choice is no longer influenced by domestic production of intermediates. Demand-supply mismatches are met through imports or exports, so that increases in relative supply of skilled intermediates do not require a fall in relative prices to be absorbed by local final goods producers. Consistent with H-O theory, open economies that have high skill supply will tend to export skilled intermediates.9

Producers of intermediate goods hire labor such that wage equals marginal revenue product. Therefore, relative wages are a function of the relative price received for skilled and unskilled intermediates and the relative physical productivities of the workers (given by T). Therefore, the ratio of skilled to unskilled wages is:

The next section discusses technology itself as a function of skill supply and prices.

C. Modeling Technology Adoption

Relative wages are a function of the relative availability of skilled and unskilled technologies, which is determined in the technology market.

For a prospective licence-holder of a particular technology, it costs one unit of Y to import one unit of a machine, skilled or unskilled, such that the marginal cost is unity for both machine types. The cost of acquiring a skilled licence for a particular skilled machine Xj is Cs and the cost of a licence for an unskilled machine Zj is Cu units of Y exported. The price of a licence is inversely related to the research frontier reached by R&D in the developed world. The price is also higher if the next available technology is closer to the technology frontier: Cs=(TsRs)k and Cu=(TuRs)k,k>0. Defining C=CsCu,

R is the skill-bias of the world’s available technologies such that the relative cost of importing skilled technologies is inversely related to it. k governs how fast the cost of a licence rises as we get closer to the technology frontier.

At any time t, the potential licensee considers if the value of the licence exceeds the cost. The value is the present value of future profits, which is the same for all skilled machine types. Thus the value of a skilled licence at time t is Vts=Σi(Pt+iX1)Xt+i(1+r)i, where r is an internationally set interest rate. Recalling PX=1α, using equation (2) and defining Ω(1α)α1+α1α, the per period profit from a licence for a skilled machine can be written as πx=Ω(pts)11αLts. Similarly, the per-period profit for an unskilled licence is described by πz=Ω(ptu)11αLtu. Therefore, the value of a skilled licence is

and the value of an unskilled licence is

For constant values of L and R, an equilibrium can be found in which p, V and T are constant, and time subscripts can be dropped. Under these assumptions, the relative value of a skilled technology V=VsVu is given by

which intuitively shows that skilled technologies are more valuable to adopt if there are relatively more skilled people to work with them and if the price received for a skilled intermediate is higher. By free entry, Vs = Cs and Vu = Cu, so

and

The ratio of skilled to unskilled technologies in developing countries is positively related to the relative availability of skilled technology licences in the world. It is also positively related to the relative price of skilled intermediates and to the relative supply of skilled labor. Equation (14) summarizes the three mechanisms driving the skill-bias of technical change, namely skill-biased R&D in rich countries (increase in R), expanded education access (increase in L, under certain conditions), and international trade (which increases p for a skill-abundant developing country). By equations (8) and (14):

The skill premium is positively related to the relative price of skill intermediates. It is also positively related to the skill supply and skill-bias of the technology frontier, although these two factors can affect p. Specifically, in a closed economy, equation (7) can be used to substitute for p such that

and

These equations show that the elasticity of substitution between skilled and unskilled labor, σ, is an important parameter governing the sign and extent of the response of wages to skill supply and SBTC in the advanced countries. For R in particular, the response is positive if and only if σ > 1. These parameters govern how much the relative price of skilled intermediates would have to fall to preserve equilibrium after an increase in the supply of skilled intermediates, which has implications for how much p falls in response to a rise in relative skill supply or skill-biased technologies. The other results, in particular for skill supply and opening up to the economy, hold for any feasible value; that is σ > 0, but the mechanisms can be different (see Acemoglu, 2002a; Behar, 2009). Furthermore, there is an empirical consensus that the elasticity lies above 1 (Behar, 2009; Freeman, 1986). The discussion of comparative statics that follows assumes that σ > 1.

IV. Comparative statics

A. Skill-biased Technical Change in Advanced Countries

It is straightforward to show that the steady-state ratio of T is unequivocally positively related to the availability of skill-biased technologies researched and developed by advanced countries. In particular, a rise in R leads to a rise in T in both open and closed economies (equations 14 and 16) and regardless of their endowments. In a small open economy, d ln Wd ln R=1 but, because the change in R is pervasive, all countries would experience this. Extra availability of skilled intermediates necessitates a fall in the the international relative price of skilled goods. This feedback effect into p and hence W is akin to the closed economy setting:

These simple steady-state comparative static findings echo those in Behar (2011), who models these features in a dynamic multi-period context. Faster R&D in the advanced economies leads to faster growth in developing economies but, reflecting persistent SBTC in the North, Rs grows faster than Ru in all periods such that wage inequality is persistently growing.

These results of course rely on the ability to import machines from abroad. Increasing the availability of technology spurs economic growth. Furthermore, an economy that has been closed for a long period of time may not have had access to recent technologies during a period when R is rising, so that its effective relative cost of skilled machines is much higher than implied by R. Upon opening up to trade, this would be tantamount to a rise in R and would consequently induce SBTC. This mechanism would be similar to the skill-enhancing trade hypothesis due to Robbins (1996; 2003). Differential effects between the skill-abundant and skill-scarce developing countries would rely on additional arguments; for example differences in absorption capacity (Meschi & Vivarelli, 2009), or differential effects of trade liberalization on the incentives to acquire skill-biased technologies, as discussed next.

B. Trade and Skill Supply (Small Open Economy)

The SOE is a developing country but is skill-abundant, as discussed earlier; it is straightforward to develop analogues for LICs or other skill-scarce country groups. In particular, the SOE has L > L’, where L’ is the world ratio of skilled to unskilled labor. All other characteristics – including the relative access to skilled and unskilled technologies, R = R’, and the production technology – are the same. In the rest of the world, T=Lσ1σk+1Rσkσk+1<T for σ>1,p=Rkσk+1L(k+1)σk+1>p and W=Lσ2kσk+1Rk(σ1)σk+1.

After opening up the intermediates goods market to trade, the SOE now faces the world price p’. With no technical change, one can show that the skill premium rises in accordance with the standard HOSS effect, i.e.:

However, allowing for endogenous skill-biased technical change, T rises in response to the higher relative prices. As a result, there is a further increase in the wage premium such that, using (14)

The k+1k>1 superscript on the relative price terms shows that the change in relative prices increases the returns to factors in a standard HOSS sense but also the relative productivity of factors through the endogenous SBTC. The endogenous price differential is ultimately a function of the asymmetry in skill endowment, and the additional superscript on the relative endowment can be interpreted as an amplification of the effective relative skill endowment brought about by endogenous SBTC. The magnitude of the SBTC is a function of how quickly the cost of a licence rises as technologies are acquired (k). By equation (5), the rise in relative prices from p to p’ leads to net exports of skilled intermediates by the skill-abundant developing country. Furthermore, the rise in T leads to a further increase in the relative supply (cf. equations (4)) and, hence, net exports of skilled intermediates.

In Acemoglu (2003), the opening up to trade in advanced economies leads to SBTC in those countries, which then translates to SBTC in less developed countries even if the relative price of skilled goods falls in the less developed countries. Here, skill-abundant developing countries experience technical change that favors skilled workers because the relative price of skilled goods is rising in these countries. Importantly, an analogous treatment for a skill-scarce SOE would indicate a fall in the relative price of skilled intermediates and the adoption of technologies that favor unskilled workers. The skill-scarce SOE would also be a net importer of skilled intermediates.

We have assumed that the country had access to R before opening up to trade in intermediates. It is clearly unrealistic to assume a country would have the same access to the technology frontier. As already discussed, it is also plausible that the ratio R would exceed the ratio of available technologies in the closed developing economy. If so, trade liberalization would coincide with a rise in R and hence further SBTC.

Further, note that WSOE=RL1/kpk+1k (cf equation 15) and that, because the SOE is a price taker

which is unequivocally positive. The mechanism is that increased relative availability of skills makes the value of skill-biased technologies relatively high, which leads to SBTC and hence increases the relative wage of skilled labor. The extent of SBTC depends on how quickly licence prices rise (k).

C. Trade and Skill Supply (Medium Economy)

The results in the previous section are strong and clear because of the absence of feedback effects from the SOE onto prices. In a closed economy, a rise in skill supply can induce a standard substitution effect – more skilled labor relative to unskilled labor induces a fall in the skill premium – ceteris paribus. The substitution effect operates via the effect of skill supply on the relative quantity of skilled intermediates and hence the relative price of intermediates. Similarly, SBTC affects the relative quantity and price of skilled intermediates. Therefore, the effect of skill supply or trade on the skill premium becomes a function of the elasticity of substitution (Acemoglu 2002b, Behar, 2009).

To allow for the possibility of feedback effects and qualify the results, the case of two large symmetric developing countries opening up to trade can be interpreted as a multilateral trade liberalization involving two groups of countries, where the former are abundant in skilled labor. As before, L' < L. In this setting, the world price will be a function of the endowments and technologies in both countries:

We know T = R (Lpmedium)1/k and, analogously, T’ = R (Lpmedium)1/k so that T’/T = (L’/L)1/k. Therefore, Ts=Ts(LsLs)1/k and Tu=Tu(LuLu)1/k. Letting λ1+(LsLs)k+1k1+(LuLu)k+1k denote the degree of asymmetry in skill abundance between the two countries, the equilibrium world price of goods can be rewritten as pmedium=(TLλ)1σ. The assumption that L’ < L implies λ < 1. The technology ratio in the middle income skill-abundant country (with analogues for the low income countries) is T=Lσ1σk+1Rσkσk+1λ1σk+1, and pmedium=Rkσk+1L(k+1)σk+1λkσk+1 so that wages are given by Wmedium=Lσk2σk+1Rk(σ1)σk+1λ(k+1)σk+1

It is straightforward to confirm that Wmedium > Wclosed and that the differential is bigger than in the absence of induced SBTC, although the differential is not as large as in the SOE case because of feedback effects to relative prices. This amplification effect is important because quantified HOSS effects tend to fall far short of the observed wage changes (Chusseau et al, 2008). Furthermore, it can attend to two shortcomings of the HOSS model. First, the price feedback effect can explain why there are sometimes big wage changes without the commensurate changes in product prices. Second, SBTC explains the empirical observation that relative wages are not equalized across all countries.

As before, steady state changes in R increase the skill premium. Furthermore, the response of wages to increased skill supply is given by:

In the case where all/both countries simultaneously increase their skill supply, then λ is unchanged and the last term drops out. This case is analytically the same as a closed economy, which has been analyzed by Acemoglu (2002) and Behar (2009). In a closed economy, as before, the mechanism is that increased relative availability of skills makes the value of skill-biased technologies relatively high, which tends to increase the relative wage of skilled labor, and this effect is mitigated by the increasing licence cost. However, the SBTC effect is countered by the standard substitution effect (where an increase in relative skill supply reduces the wage premium in the absence of technical change). Specifically, the increased relative supply of skills and skilled machines brings about an increased supply of skilled intermediates, a fall in the relative price of skilled intermediates and hence a fall in the relative marginal revenue product of skilled labor. The size of these mitigating effects through prices is governed by σ = ϵ + α – ϵα: a higher value of σ means that only small changes in relative prices and wages are needed to restore equilibrium in the intermediates and labor markets.

If skilled and unskilled labor are perfect substitutes (σ),d ln Wd ln L=1/k in a closed economy, which echoes the SOE setting above. This assumption is made in Behar (2011), who models the dynamics of one-off increases in skill supply but also a gradual increase through the dynamic cohort effects of expanded schooling access. In that framework, an increase in schooling provision increases growth rates but at the cost of persistently increasing wage inequality and unskilled wages that remain stagnant for a long time. More generally, the derivative is positive for a value of σ that is sufficiently high. In a closed economy, this is specifically σ > k + 2 or at least 2.

Turning to the case where only the MIC increases its skill supply; that is, L rises but L’ does not; it is straightforward to show that  ln λ ln L<0, reflecting an increase in the endowment asymmetry between countries. In terms of equation (24), the elasticity threshold for a positive derivative is lower than k + 2 but the extent is highly dependent on the relative factor endowments (λ) and k. It would also be dependent on the relative sizes of the economies, which have been assumed equal for convenience but not realism.

For developing countries10 using macroeconomic data, Psacharopoulos & Hinchliffe (1972) estimate σ to be 2.1 to 2.5 but also generate elasticities as high as 1000 and more modern studies are hard to find. Microeconomic studies tend to generate lower elasticities but these are also extremely rare for developing countries. It’s also of course impractical to try to estimate k. However, recall that there appears to be little or no correlation between relative skill supply and the wage premium. As discussed in Behar (2009), this is consistent with the numerator in equation (24) being approximately zero.11 This would imply that the endogenous skill-biased technical change effect is countered by the standard substitution effect. In this context, SBTC offers an explanation for why skill supply does not necessarily reduce the wage premium.

V. Conclusion

Skill-biased technical change (SBTC) is an important contributor to the higher inequality observed in developing countries. This paper has presented the case that technical change in developing countries is not necessarily skill-biased, but has been because of the increasing availability of skill-biased technologies researched and developed by advanced economies. This study has argued that opening up to international trade affects the relative prices of skill-intensive goods, which affects the skill-bias of technology adoption. MICs tend to be skill-abundant relative to the rest of the world, so trade liberalization would increase the relative price of skill-intensive goods and make the adoption of skill-biased technologies more attractive. The opposite is true for skill-scarce developing countries. The skill bias is also determined by the endowment of skilled labor, which is why an increase in the supply of skilled labor stimulates SBTC such that skill supply creates its own demand. SBTC amplifies the Heckscher Ohlin Stolper Samuelson (HOSS) effects of trade liberalization.

These phenomena help explain pervasive increasing wage inequality within developing countries because the technologies available to developing countries increasingly favor skilled workers even if these countries are skill-scarce. The failure of increased educational attainment to reduce wage inequality is attributed to induced SBTC acting against the standard substitution effect. This may explain why inequality rose in the Middle East and North Africa despite the region’s rapid increase in educational attainment. Trade-induced SBTC helps explain the negative correlation between trade and inequality in LICs and the zero or positive correlation in MICs. MENA countries cover a wide range of skill endowments and income levels, so the impact of trade openness on inequality could vary across the region.

While access to foreign technology, an increase in human capital and trade openness are important ingredients for productivity gains and economic growth, they may not be inclusive, especially in MICs. To ensure that all benefit from growth, appropriate policies may be needed to protect the vulnerable and to distribute the proceeds of such productivity gains. Equipping people with skills is a reasonable response to the SBTC brought about by globalization. However, in the race between education and technology (Tinbergen, 1975), recipients of expanded education may be able to adapt to SBTC, but this may not reduce inequality and would not help those who remain unskilled.

Appendix

Data and Methodological Description

The inequality data used in three of the linear projections in Table 1 is the EHII, which is an index from 0–100 of household income inequality from 1963 to 2003. More information on the data, which is also used in Table 2 and is constructed by the University of Texas Inequality Project (UTIP), is available at http://utip.gov.utexas.edu/data.html. International trade data is taken from the IMF World Economic Outlook database (October 2012 version), based on the Direction of Trade Statistics, which is also used in Table 4. Both of these are available annually but we take five-year averages to match the educational attainment data for 1980, 1985, 1990, 1995 and 2000 (Barro & Lee, 2001). The proportion of skilled workers is constructed by aggregating those with completed primary school or more schooling using the procedure in Caselli & Coleman (2006). Similarly, the proportion of unskilled workers is constructed by aggregating the levels of attainment below the completion of primary school. Altogether, data is available for about 50 developing countries. These projections include country fixed effects and exploit the within-country variation over time, such that the coefficients can represent the correlations between the extent of increased skill supply, international trade and inequality over time. The positive trend coefficient can be interpreted as pervasive SBTC, and similar results are achieved using individual period dummies. As a robustness check of the first three projections in Table 1 in the body of the paper, Table A1 presents specifications that use alternative measures of trade openness.

Table A1:Additional Projections of Inequality on Education, Trade and Time for Developing Countries
Dependent Variable:1EHIIEHIIEHII
Countries:LICLMICUMICLICLMICUMICLICLMICUMIC
Skilled Proportion-0.179*0.0602-0.0634-0.236*0.0574-0.0564-0.221*0.0557-0.0604
Standard error[0.0971][0.0709][0.0959][0.119][0.0762][0.0899][0.107][0.0769][0.0937]
Trend0.120.210***0.327**0.1390.213***0.337**0.179**0.212***0.328**
Standard error[0.106][0.0714][0.132][0.086][0.067][0.137][0.0788][0.0695][0.13]
Trade/GDP share (Goods, Services)-4.619**0.07832.15
Standard error[1.987][1.2][3.968]
Export/GDP share (Goods)-5.905*0.391.172
Standard error[2.84][1.673][2.906]
Export/GDP share (Goods, Services)-5.686*0.2790.727
Standard error[2.71][1.45][1.91]
Constant-171-375.2**-614.3**-211.5-381.1***-629.2**-288.9*-379.6**-612.9**
Standard error[213.8][140][264.4][177.6][132.2][270.7][163.3][136.6][256.5]
R20.150.270.250.220.270.240.180.270.24
N386260386260386260
Countries152015152015152015

EHII is index of household income inequality and projected on the log of the proportion of the population who have completed at least primary school, the log of trade openness, and a five-year trend term using country fixed effects in a panel. The three alternative measures of trade openness are the ratio of goods and services trade to GDP, the ratio of goods exports to GDP, and the ratio of goods and services exports to GDP. Projections are for Upper-Middle-Income (UMIC), Lower-Middle-Income (LIMIC) and Low-Income (LIC) countries. *, ** and *** denote significance at 10%, 5% and 1% based on robust standard errors in brackets.

EHII is index of household income inequality and projected on the log of the proportion of the population who have completed at least primary school, the log of trade openness, and a five-year trend term using country fixed effects in a panel. The three alternative measures of trade openness are the ratio of goods and services trade to GDP, the ratio of goods exports to GDP, and the ratio of goods and services exports to GDP. Projections are for Upper-Middle-Income (UMIC), Lower-Middle-Income (LIMIC) and Low-Income (LIC) countries. *, ** and *** denote significance at 10%, 5% and 1% based on robust standard errors in brackets.

Table 1 also presents two linear projections on the skill premium as an alternative to household inequality. The skill premium is the ratio of wages received by skilled relative to unskilled workers. Using the constructed proportions of skilled and unskilled workers, this paper calculates the appropriately weighted skill premium based on Mincerian earnings regression coefficients taken from Behar (2009). As an alternative to the trade-to-GDP ratio, a widely adopted measure of openness is taken from Sachs & Warner (1999).12 The projections rely on a cross-sectional dataset for 43 developing countries from 1985 onwards. While the data coverage is more limited than for the EHII-based analysis, it offers a closer match to the wage premium in the formal modeling. The estimation method is Ordinary Least Squares with robust standard errors, but estimation based on Least Absolute Deviations yields similar results. More details on the methodology are available in Behar (2009).

References

    AcemogluD (1998). Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality. Quarterly Journal of Economics113(4):10551089.

    • Search Google Scholar
    • Export Citation

    AcemogluD (2002a). Technical Change, Inequality and the Labor Market. Journal of Economic Literature40(1):772.

    AcemogluD (2002b). Directed Technical Change. Review of Economic Studies69(4):781809.

    AcemogluD (2003). Patterns of Skill Premia. Review of Economic Studies70(2): 199230.

    AcemogluD & FZilibotti (2001). Productivity Differences. Quarterly Journal of Economics116(2):563606.

    AlmeidaR (2009). Openness and Technological Innovation in East Asia: Have They Increased the Demand for Skills?IZA Working Paper 4474.

    • Search Google Scholar
    • Export Citation

    AndersonE (2005). Openness and Inequality in Developing Countries: A Review of Theory and Recent Evidence. World Development33:10451063.

    • Search Google Scholar
    • Export Citation

    Atkinson. A & JStiglitz (1969). A New View of Technological Change. Economic Journal79:573578.

    BarroR & JLee (2001). International Data on Educational Attainment: Updates and Implications. Oxford Economic Papers53(3):541563.

    • Search Google Scholar
    • Export Citation

    BarroR & XSala-i-Martin (2004). Economic GrowthSecond Edition. MIT PressCambridge, MA.

    BeharA (2007). Are Skilled and Unskilled Labor Complements or Substitutes?Doctoral Thesis PublishedbyVerlagDr. Muller.

    BeharA (2009). Directed Technical change, the Elasticity of Substitution and Wage Inequality in Developing Economies. Oxford Department of Economics Discussion Paper 467.

    • Search Google Scholar
    • Export Citation

    BeharA (2011). Skill-biased Technology Imports, Increased Schooling Access, and Income Inequality in Developing Countries. Journal of Globalization and Development2(2):129.

    • Search Google Scholar
    • Export Citation

    Ben SalhaO (2012). Economic Globalization. Wages and Wage Inequality in Tunisia: Evidence from the ARDL Cointegration Approach. mimeo.

    • Search Google Scholar
    • Export Citation

    BermanEJBound & SMachin (1998). Implications of Skill-biased Technical Change: International Evidence. Quarterly Journal of Economics113(4):12451279.

    • Search Google Scholar
    • Export Citation

    BermanE & SMachin (2000). Skill-biased Technology Transfer: Evidence of Factor-biased Technical Change in Developing Countries. mimeo.

    • Search Google Scholar
    • Export Citation

    BursteinAJCravino & JVogel (2012). Importing Skill-Biased Technology. mimeo.

    BustosP (2011). Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms. American Economic Review101(1): 30440.

    • Search Google Scholar
    • Export Citation

    CampanteF & DChor (2012). Why Was the Arab World Poised for Revolution? Schooling, Economic Opportunities, and the Arab Spring. Journal of Economic Perspectives26(2):167188.

    • Search Google Scholar
    • Export Citation

    CaselliF & JColeman (2006). The World Technology Frontier. American Economic Review96(3):499522.

    CaselliF & DWilson (2004). Importing Technology. Journal of Monetary Economics51(1):132.

    ChusseauNMDumont & JHellier (2008). Explaining Rising Inequality: Skill-biased Technical Change and North-South Trade. Journal of Economic Surveys22(3):409457.

    • Search Google Scholar
    • Export Citation

    ConteA & MVivarelli (2011). Imported Skill-biased Technological Change in Developing Countries. The Developing Economies49(1):3665.

    • Search Google Scholar
    • Export Citation

    DavisD. (1996). Trade Liberalization and Income Distribution. NBER Working Paper 5693.

    De GregorioJ & JLee (2002). Education and Income Inequality: New Evidence From Cross-Country Data. Review of Income and Wealth48(3):395416.

    • Search Google Scholar
    • Export Citation

    EatonJ & SKortum (2001). Trade in Capital Goods. European Economic Review45(7):11951235.

    EspositoP & RStehrer (2009). The Sector Bias of Skill-biased Technical Change and the Rising Skill Premium in Transition Economies. Empirica36(3):351364.

    • Search Google Scholar
    • Export Citation

    FajnzylberP. and AnaFernandes2004. “International Economic Activities and the Demand for Skilled Labor: Evidence from Brazil and China.”World Bank Policy Research Working Paper 3426.

    • Search Google Scholar
    • Export Citation

    FeenstraR & GHanson (1997). Foreign Direct Investment and Relative Wages: Evidence from Mexico’s Maquilladoras. Journal of International Economics42:371393.

    • Search Google Scholar
    • Export Citation

    FedderkeJ KayembaSHendersonMMarrioti & PVaze (2001). Changing Factor Market Conditions in South Africa: the Capital Market – a Sectoral Description. Development Southern Africa18(4):493511.

    • Search Google Scholar
    • Export Citation

    FransmanM (1985). Conceptualising Technical Change in the Third World in the 1980s: An Interpretive Survey. Journal of Development Studies21(4):572652.

    • Search Google Scholar
    • Export Citation

    FreemanR (1986). Demand for Education in OAshenfelter & RLayard (eds) Handbook of Labor EconomicsVol1. Elsevier / North Holland.

    GoldbergP & NPavcnik (2007). Distributional Effects of Globalization in Developing Countries. Journal of Economic Literature45(1):3982.

    • Search Google Scholar
    • Export Citation

    GourdonJ (2011). Wage Inequality in Developing Countries: South–South Trade Matters. International Review of Economics58:125.

    GrilichesZ (1969). Capital Skill Complementarity. Review of Economics and Statistics51(4):465468.

    HabbakukJ (1962). American and British Technology in the Nineteeth Century: Search for Labor Saving Inventions. Cambridge University PressCambridge, United Kingdom.

    • Search Google Scholar
    • Export Citation

    HansonG (2012). The Rise of Middle Kingdoms: Emerging Economies in Global Trade. Journal of Economic Perspectives26(2):4164.

    HayamiY & VRuttan (1970). Factor Prices and Technical Change in Agricultural Development: The United States and Japan: 1880-1960. Journal of Political Economy78(5):11151141.

    • Search Google Scholar
    • Export Citation

    HicksJ (1963). The Theory of Wages (2nd edition). MacMillan & Co Ltd.

    KatzL & DAutor (1999). Changes in the Wage Structure and Earnings Inequality in OAshenfelter & DCard (eds.) Handbook of Labor Economics Vol 3ANorth-Holland.

    • Search Google Scholar
    • Export Citation

    KileyM (1999). The Supply of Skilled Labor and Skill-biased Technical Progress. Economic Journal109:708724.

    KnightJ (1979). Black Wages and Choice of Techniques in South Africa. Oxford Bulletin of Economics and Statistics44(2):117144.

    KnightJ & RSabot (1987). Educational Expansion, Government Policy and Wage Compression. Journal of Development Economics26(2):201221.

    • Search Google Scholar
    • Export Citation

    KrusellPLOhanianJRios-Rull & GViolante (2000). Capital-skill Complementarity and Inequality: a Macroeconomic Analysis. Econometrica68(5):10291054.

    • Search Google Scholar
    • Export Citation

    MachinS & AManning (1997). Can Supply Create its Own Demand? Implications for Rising Skill Differentials. European Economic Review41(3-5):507516.

    • Search Google Scholar
    • Export Citation

    MartinsP & PPereira (2004). Does Education Reduce Wage Inequality? Quantile Regression Evidence From 16 Countries. Labor Economics11(3):355371.

    • Search Google Scholar
    • Export Citation

    MatsuyamaK (2007). Beyond Icebergs: Towards a Theory of Biased Globalization. Review of Economic Studies74(1):237253.

    MeschiE & MVivarelli (2009). Trade and Income Inequality in Developing Countries. World Development37(2):287302.

    MokyrJ (1990). The Levers of Riches: Technological Creativity and Economic Progress. NY: Oxford University PressOxford, United Kingdom.

    • Search Google Scholar
    • Export Citation

    NelsonR & EPhelps (1966). Investment in Humans, Technological Diffusion and Economic Growth. American Economic Review56(2):6775.

    O’SuilvanM & KersleyR (2011). From Spring to Renewal: Regime Change and Economic Transformation. Credit Suisse Research Institute.

    • Search Google Scholar
    • Export Citation

    PsacharopoulosG & HHinchliffe (1972). Further Evidence on the Elasticity of Substitution Among Different Types of Educated Labor. Journal of Political Economy80(4):786792.

    • Search Google Scholar
    • Export Citation

    RobbinsD. (1996). HOS Hits Facts: Facts Win; Evidence on Trade and Wages in the Developing World. Harvard Institute for International Development Discussion Paper 557.

    • Search Google Scholar
    • Export Citation

    RobbinsD. (2003). The Impact of Trade Liberalization Upon Inequality in Developing Countries – A Review of Theory and Evidence. ILO Working Paper 13.

    • Search Google Scholar
    • Export Citation

    RomerP (1990). Endogenous Technological Change. Journal of Political Economy98(5):S71S102.

    RosenbergN (1969). The Direction of Technological Change: Inducement Mechanisms and Focusing Devices. Economic Development and Cultural Change18(1):124.

    • Search Google Scholar
    • Export Citation

    SachsJ & AWarner (1999). The Pig Push, Natural Resource Booms and Growth. Journal of Development Economics59(1):4376.

    SavvidesA & MZachariadis (2005). International Technology Diffusion and the Growth of Total Factor Productivity in the Manufacturing Sector of Developing Economies. Review of Development Economics9(4):482501.

    • Search Google Scholar
    • Export Citation

    SchmooklerJ (1966). Invention and Economic Growth. Harvard University PressCambirdge, MA.

    TinbergenJ (1975). Income Distribution Analysis and Policies. North-Holland Publishing CoAmsterdam.

    ThoenigM & TVerdier (2003). A Theory of Defensive Skill-Biased Innovation and Globalization. American Economic Review93(3):709728.

    • Search Google Scholar
    • Export Citation

    WoodA (1994). North-South Trade. Employment and Inequality. Oxford University PressOxford, United Kingdom.

    WoodA (1997). Openness and Wage Inequality in Developing Countries: The Latin American Challenge to East Asian Conventional Wisdom. World Bank Economic Review11(1):3357.

    • Search Google Scholar
    • Export Citation

    WoodA & JMayer (2011). Has China De-industrialised Other Developing Countries?. Review of World Economics147(2):325350.

*The author would like to thank Paul Cashin, Sanaa Farid, Camelia Minoiu, Kia Penso, Adrian Peralta-Alva, Adrian Wood and participants at the conference on “Inequalities, Skills and Globalization,” Lille, France, June 2012, for helpful comments.
2Readers interested in the relative employment fortunes of skilled and unskilled workers in developing countries could refer to Conte & Vivarelli (2011).
3From a theoretical perspective, the positive coefficients in columns 4 and 5 can be problematic because most derivations would imply a negative elasticity of substitution between the factors in a constant elasticity of substitution production function (Behar, 2009).
5A distinct issue is an increase in the inequality of access to education, which has been observed in the Middle East (Salehi-Isfahani, Hassine & Assaad, 2012).
6Further alternative perspectives are offered in Matsuyama (2007) and Feenstra & Hanson (1997).
7In South Africa, for example, accelerated SBTC was observed despite a fall in the pace of investment in physical capital (Fedderke et al, 2001).
9It is for modeling convenience that we make trade in intermediates. One could alternatively replace equation (1) with a utility function in two final goods. Furthermore, it is now well documented that the majority of the world’s goods trade is actually in intermediates.
10Acemoglu (1998) argues that σ is sufficiently large for the directed technical change effect to outweigh the substitution effect in the United States and other advanced countries, and that this explains the simultaneous increase in educational attainment and wage premia. Freeman (1986) offers a consensus estimate of between 1 and 2 for developed countries but some estimates are substantially higher.
11In particular, Behar (2009) argues the endogenous SBTC framework used here provides a more robust basis for estimating σ. On the assumption that k = 0 in a closed economy, multiple estimates point to an estimate of σ = 2. This is an overestimate to the extent that k > 0 but an underestimate to the extent that economies are open. Furthermore, this approach offers an improvement over tradtional methods, which produce highly volatile estimates, and it theoretically rationalizes the positive coefficients in columns 4 and 5 of Table 1.
12A country is not open if (i) average tariffs exceed 40 percent; (ii) nontariff barriers cover more than 40 percent of imports; (iii) it has a socialist economic system; (iv) the black market premium on the exchange rate exceeds 20 percent; or (v) exports are controlled by a state monopoly.

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