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Global Knowledge and Local Inequality -
Industry Level Evidence∗
Ahmed S. Rahman
Department of Economics, United States Naval Academy
Annapolis, MD 21402
Tele: 410.293.6897
Email: [email protected]
July 2007
Abstract
This paper attempts to ascertain if skill-biased technologies developed in R&D-active coun-
tries diffuse to the rest of the world. First, using a model of international trade, I show
the effects of skill-bias knowledge diffusion. The theory suggests that skill-biased techno-
logical diffusion need not increase skill premia, as sectoral biases can exert countervailing
forces. Second, I test implications from the theory using United Nations industry data.
Skill-biased knowledge diffusion tends to be associated with rising local skill-premia more
in skill-intensive industries than unskill-intensive ones. Thus sectoral biases can help us see
the extent of such technological spillovers.
• Keywords: Technological Diffusion, Factor Bias, Sector Bias, Skill Premia, Inequality
• JEL Codes: O3, L6, F1
∗This work has greatly benefited from comments from participants in the U.C. Davis Macro/InternationalTrade Workshop and the Southern Economic Association Meetings in Charleston, SC. All errors are my own.
1
1 Introduction
How pervasive is skill-biased technological growth? The skill-bias inherent in the endogenous
technological developments in Western economies during the last few decades has been well
documented. For example, many studies have documented declining relative wages of less-skilled
workers in the United States and the simultaneous decline in their employment in manufacturing
industries (for example, Katz and Murphy 1992; Blackburn, Bloom and Freeman 1990). Further,
a number of papers have documented similar trends for wages, employment or unemployment in
other OECD countries (Freeman and Katz 1994; Katz, Loveman and Blachflower 1995; Machin
1996). While the literature has proposed a number of explanations for the declining demand
for unskilled labor, including both Stolper-Samuelson effects of increased exposure to trade and
outsourcing from developing countries (Feenstra and Hanson 1996), the overall consensus is that
increased demand for skilled labor in the OECD in the last few decades has been due to skill-
biased technological change.
Thus it appears that a few industrialized countries have lately been choosing from a menu of
best practices that includes an increasing proportion of skill-intensive technologies. But what
about the rest of world, particularly those countries that do not perform any original R&D
themselves? If we hold the view that nearly the entire R&D activity in the world economy is
concentrated in the industrial countries,1 then the world stock of knowledge has become more
and more skill-intensive in the last few decades as well. Can rising inequality between the skilled
and unskilled within particular countries be evidence of this knowledge diffusion?
As a first pass, the literature finds that R&D activity in high income countries is associated
with rising skill-premia and wage-shares paid to the skilled only in middle-income countries, not
in low -income countries.2 This tends to be taken as evidence of knowledge diffusion to middle-
but not low-income countries. (Berman and Machin 2000; Berman, Bound and Machin 1998).
There are however a number of limitations with this literature. Perhaps most importantly, a
world of trade can severely complicate the story. Specifically, if such skill-biased knowledge
grows in unskill-intensive sectors, skill premia can be positively correlated across countries even
without any knowledge diffusion, or it can be negatively correlated across countries with perfect
diffusion. Further, econometrics is often eschewed for these simple cross-country correlations.
Here the literature treats skill-biased knowledge as a pool from which all can draw, commenting
neither on the R&D-producers’ relative research efforts nor on the potential channels through
which the R&D-receivers acquire such knowledge.
This paper attempts to address some of these deficiencies by focusing on both the factoral
1In 1990, the industrial countries accounted for 96% of total world R&D expenditures (UNESCO 1993).2The middle income countries are defined in these studies as those with GDP per capita of between $2000 and
$10000.
2
and sectoral biases of these knowledge flows in an econometric framework. Measuring skill-bias
knowledge generated by industrialized nations active in industrial research and development, we
aggregate this knowledge to construct a “world stock” of skill-intensive technology. This stock
of knowledge may be accessible to nations to differing degrees, and may also influence national
income inequality to differing degrees.
Following are the main findings of the paper. Technological growth from countries highly active
in research and development tends to be skill biased. Innovation also tends to be more skill-biased
in skill-intensive industries than unskill-intensive industries. This R&D activity tends to widen
skill premia for other R&D-producing countries through diffusion, but does not appear to do so
for less developed countries. However, skill-biased diffusion widens the income distribution of
skill-intensive industries more dramatically than unskill-intensive industries, and this occurs both
in developed and developing countries. From this we conclude that the diffusion of technology
generated in the developed world plays an important role in the factoral distribution of income
across the whole world.
We should note that the effects of technological diffusion on relative wages should be of par-
ticular interest to developing countries. If increased demand for skills exacerbates the already
high income inequality in developing countries, the social and political implications may be quite
extreme for those nations whose political stability is already tenuous. Furthermore, understand-
ing the causes of increased demand for skill in developing countries may help explain not only
intra-national inequality, but also the role of technological biases in propagating international
inequality in income and human well-being.
As we will see, whether or not technological diffusion widens the skill premium in an industry
will depend both on the relatively skill-intensity of the technology and on the relative skill-
intensity of the industry. Thus the extant growth and labor literatures, which mostly ignore
the sector bias of technology flows, miss an important channel in analyzing the consequences of
knowledge diffusion.3
The remainder of this paper is organized as follows. Section 2 presents a simple model to
highlight the importance of sector-bias in skill-biased knowledge diffusion. Section 3 explains
our empirical strategy, describes the data that is used and reveals some of the results. Section 4
concludes.
3The sector bias of technology tends instead to be a focus of trade economists. Important works here includeBaldwin and Cain (1997), Krugman (2000), and Leamer (1998).
3
2 Theoretical Framework: Technological Growth, Diffu-
sion and Relative Wages
2.1 A Simple Model
In this section we highlight the fact that when both factor and sector biases are present,
technological diffusion arising from one region may ambiguously affect the relative factor prices
of another region. Here we present the 2x2x2 Heckscher-Ohlin model of international trade,
allowing for technological differences between the two countries.4 Consider two economies, Home
and Foreign, two goods, X and Y , and two factors of production, skilled labor and unskilled labor.
Each country has fixed endowments of skilled and unskilled labor, respectively labeled H and L
for Home, and H∗ and L∗ for Foreign. We will assume that good X is produced using a more
skill-intensive process than good Y . We also assume perfect inter-industry factor mobility within
each country, but no factor mobility between countries. As such there is one equilibrium national
wage for H and L (wh and wl), and for H∗ and L∗ (w∗h and w∗
l ).
Output in each sector is produced according to a CES production function with elasticity of
substitution σ:
X =[(AxLx)
σ−1σ + (BxHx)
σ−1σ
] σσ−1
X∗ =[(A∗
xL∗x)
σ−1σ + (B∗
xH∗x)
σ−1σ
] σσ−1
(1)
Y =[(AyLy)
σ−1σ + (ByHy)
σ−1σ
] σσ−1
Y ∗ =[(A∗
yL∗y
)σ−1σ +
(B∗
yH∗y
)σ−1σ
] σσ−1
(2)
Thus, factor-augmenting productivity parameters for sector i are Ai and Bi for home and A∗i
and B∗i for foreign. We will assume throughout that sector X is the more skill-intensive in both
countries; that is: Bx
Ax> By
Ay, and B∗
x
A∗x>
B∗y
A∗y. We will further assume that the home country is
technologically inferior to the foreign country; that is, Ax < A∗x, and Bx < B∗
x.
We choose good Y as the numeraire and set its price to one. Denote p as the price of good X
in home. Perfect competition leads to the following zero-profit conditions:
cx =
[(wl
Ax
)1−σ
+
(wh
Bx
)1−σ] 1
1−σ
= p (3)
cy =
[(wl
Ay
)1−σ
+
(wh
By
)1−σ] 1
1−σ
= 1 (4)
4Xu (2001) performs a similar exercise, without specifying actual functional forms.
4
Similar functions hold for foreign’s zero-profit conditions. We assume that the two countries
engage in free trade and production is diversified; that is, each country produces both goods. In
such an equilibrium, p = p∗, implying that:
(
wl
Ax
)1−σ
+(
wh
Bx
)1−σ
(wl
Ay
)1−σ
+(
wh
By
)1−σ
1
1−σ
=
(
w∗lA∗x
)1−σ
+(
w∗hB∗
x
)1−σ
(w∗lA∗y
)1−σ
+(
w∗hB∗
y
)1−σ
1
1−σ
(5)
Since our zero-profit conditions are homogenous to degree one in wl/Ai and wh/Bi, we can express
equation (5) in terms of relative factor prices. Thus equation (5) defines a relationship between
relative factor prices between countries that arise from the zero-profit conditions.
Next we turn to market clearing conditions. From Shepard’s Lemma, partially differentiating
the unit cost function with respect to wh and wl yields the demand for skilled and unskilled labor
per unit of output. For the home country, our full employment conditions then are:(∂cx∂wl
)X +
(∂cy∂wl
)Y = L (6)
(∂cx∂wh
)X +
(∂cy∂wh
)Y = H (7)
By Cramer’s Rule, we can solve for production in the unskill-intensive sector:
Y =
(∂cx
∂wl
)H −
(∂cx
∂wh
)L(
∂cx
∂wl
) (∂cy
∂wh
)−
(∂cy
∂wl
) (∂cx
∂wh
) (8)
Note that since the partial derivatives of the cost functions are homogenous to degree zero, we
can also express the production of Y in terms of relative factor prices.
Finally, we assume that individuals have Cobb-Douglas preferences in the two goods, so that
a fixed share of income is spent on each. Letting θ equal the share of income consumers spend
on good Y , the demand for good Y is given by: θ (wlL+ whH). The world market for good Y
clears when the world’s demand for good Y equals the world’s supply of good Y . That is,
θ (wlL+ whH) + θ (w∗l L
∗ + w∗hH
∗) = Y + Y ∗ (9)
We will be interested in relative factor costs, measured as skill premia. Given θ, technologies
and endowments for both economies, (5) and (9) are two equations with the two unknowns we
care about:(
wh
wl
)and
(wh
wl
)∗.
Equation (5) describes a positively sloped “supply” curve in(
wh
wl
)-(
wh
wl
)∗space. It is a direct
implication of the Stolper-Samuelson theorem, which implies that an increase in the relative world
5
price of goodX will increase the relative payments of the factor that producesX more intensively;
this happens for both countries. On the other hand, equation (9) describes a negatively sloped
“demand” curve. It suggests that a higher skill-premium at home (so higher p) means higher
demand for good Y (the unskill-intensive product has a lower relative price). For world markets
in goods to clear, the foreign country must produce more of good Y to sate the home country’s
excess demand, which would then drive down p∗, and thus drive down(
wh
wl
)∗. Figure 1 graphs
both curves, and depicts the equilibrium skill premium for both countries under the assumption
of free trade.5
2.2 Skill-Biased Technological Growth and Relative Factor Prices
One of the main points of this theoretical exposition is to highlight the complications associated
with figuring out how biased technological growth in one region can affect relative factor payments
in another. One difficulty is in separating the effects of innovation (technological growth enjoyed
in just one region) and the effects of diffusion (technological growth enjoyed in many regions).
Another is the separating factor-bias from sector-bias. Here we will focus strictly on increases
in skill -biased knowledge, either in the foreign region (increases in B∗x or B∗
y) or in the home
region (increases in Bx or By). We will also assume throughout that σ > 1, so that the factors
of production are grossly substitutable.6
We consider only skill augmenting technical development and diffusion for a number of rea-
sons. The skill-bias of technical change in the R&D-active world has been widely theorized and
documented, owning t these regions’ relative abundance of skilled labor. Thus we may consider
Foreign to be a proxy for those R&D-active countries which endogenously raise B∗x or B∗
y through
research. On the other hand, if the manufacturing industries in rich nations have developed more
and more skill-intensive techniques over time, then we should expect other economies to have
greater access to more skill-intensive technologies as well. Thus we may consider Home to be a
country where increases in Bx or By are positively related to the levels of B∗x and B∗
y . With this
basic setup, we now can model a few scenarios to motivate our empirical exercises.
First, consider this model in the context of perfect knowledge diffusion. That is, innovations
made in the foreign country diffuse perfectly to the home country, so that ultimately technological
parameters are the same for both regions. This assumption, along with free trade, implies a
perfectly integrated world economy; as such we can treat the world as one closed economy.
Thus, relative prices and wages are identical for both regions, and the relations between relative
factor prices and technologies can be determined from equation (9). By totally differentiating
5Analytical solution is not presented for economy’s sake. See Xu 2001 for further exposition.6With this assumption, “factor-augmenting” technical change and “factor-using” technical change are synony-
mous with each other. We will use these term interchangeably.
6
this equation, we can establish the following proposition:
Proposition 1 In an integrated world economy with Cobb-Douglas preferences and σ > 1, skill-
using technical change will raise the skill-premium in both regions, and labor-using technical
change will lower the skill-premium in both regions.
This case is illustrated in Figure 2. Notice that here the sector-bias of technical change is
irrelevant for wage distribution - all that matters is the factoral bias of technical change. This is
the implicit assumption used in works such as Berman and Machin (2000) in arguing that skill-
biased technical change diffuses only to middle income countries and not to poor countries. In
this case, skill-using knowledge growth correlates with rising premia for all those regions that can
use such knowledge. If skill-premia are not rising in certain regions, then, the natural assumption
is that this knowledge is not diffusing to these regions. However, this clear result stems from a
particular set of assumptions, the relaxing of which will change the implications somewhat.
Next consider what happens when skill-bias knowledge grows only in the foreign region - that
is, there is no diffusion of new technologies. Specifically, let us consider an increase in B∗y with no
concomitant rise in By, so that there is skilled-using knowledge growth in the unskilled -intensive
sector only for the foreign region. There are two general effects with this kind of change. One is
the direct effect of technical progress on relative factor prices holding relative commodity prices
constant. This will shift the zero-profit condition curve to the right - because the innovation
occurs in the unskilled-intensive sector in the foreign country, the foreign skill premium will be
lower for a given skill premium at home. But there is also the indirect effect of this technical
progress on relative factor prices that works through relative commodity prices. Specifically,
as the relative supply of the unskill-intensive product rises, its relative prices falls, implying a
rise in skill premia via the Stolper-Samuelson mechanism. This implies a rightward shift of the
full-employment condition curve,7 bringing us to our next proposition.
Proposition 2 In a world economy of trade with Cobb-Douglas preferences and σ > 1, skill-
using technical change in the unskill-intensive sector in just one region will raise the skill-premium
in the other region.
These effects are illustrated in Figure 3. Notice that while the total effect on the foreign
country’s skill-premium is unclear (as it is drawn, it rises somewhat), the home country’s skill-
premium unambiguously rises. The point here is to stress that skill-bias knowledge created in one
7Again, see Xu (2001) for further elaboration concerning these effects. Haskel and Slaughter (2002) also hasa more in-depth discussion of these direct and indirect effects.
7
country induces a rise in the skill premium of another country, even though no technological dif-
fusion occurs. Thus simple correlations of skill-premium need not indicate skill-using knowledge
diffusion. Relying on them as indicators of technical diffusion may mislead us.
To better analyze how the diffusion of skill-biased knowledge can affect local skill-premia, we
suggest looking at sectoral biases. In particular, sectoral biases matter a great deal for small-
open economies. For these economies, world prices are determined in the foreign country as if
it were a closed economy. The relative commodity price p∗ corresponds to the relative factor
price(
wh
wl
)∗. Let us now assume that Home is small relative to Foreign. Since the small home
economy is too small to affect p∗, it takes this relative price as given. Thus
(
wl
Ax
)1−σ
+(
wh
Bx
)1−σ
(wl
Ay
)1−σ
+(
wh
By
)1−σ
1
1−σ
= p∗ (10)
Furthermore, the goods market clearing condition must be a horizontally straight line - al-
though a higher skill-premium at home implies a lower demand for good X, this will not induce
a fall in p∗ like before since the home economy is so small; thus changes in home’s demand will
not affect the foreign country’s skill premium. From this, we can propose:
Proposition 3 In a small open economy, if local technological growth occurs in the skill-intensive
sector (X), the skill premium at home rises; if it occurs in the unskill-intensive sector (Y ), the
skill premium at home falls.
The case is illustrated in Figure 4. Observe that full employment condition lines are perfectly
flat. This is because Home is a price taker - whatever excess demand the home country has
can be provided by the foreign country without changing their relative prices. Because the
indirect effects of local technical growth on commodity prices are zero, all local technological
developments tend to induce only direct effects on local relative wages. Thus this case in essence
is the opposite of the case depicted in Figure 2. Here it matters not which factor the technology
uses more heavily. All that matters is which sector is affected.8
With this, we are ready to combine skill-biased knowledge growth in the foreign region with
skill-biased technological diffusion to the home region. Skill-biased knowledge growth abroad
can happen either in the skill-intensive sector or the unskill-intensive sector (growth in B∗x and
B∗y , respectively) and some of this growth can spillover into the small home country (growth in
Bx and By, respectively). Each of these cases are illustrated in Figure 5.
Note that because our foreign region is so large relative to the home region, it can be considered
a closed system. Thus, given our discussion of global skill-bias growth in an integrated system,
8Totally differentiating equation (10) allows us to get this proposition.
8
it must be that the full-employment condition line shifts upward, guaranteeing a rising skill-
premium in the foreign region. There also is the direct effect of this technical change, which
shifts the zero-profit condition line left if the the innovation is in the skill-intensive sector, and
to the right if it is in unskill-intensive sector. Without any technological diffusion to the home
country, equilibrium for both cases is marked by the intersection of (5’) and (9’). Finally, any
skill-using technological diffusion to the home country shifts the zero-profit condition in the other
direction. As mentioned in Proposition 2, movements in the home country’s skill premium are
all from the direct effect. With this diffusion, the final equilibrium is marked by the intersection
of (5”) and (9’).
The above analysis provides us with a number of key insights. First, when skill-biased in-
novation and diffusion occur simultaneously, the effects on local skill-premia become blurred.
Ultimately then, the net effects of skill-biased global knowledge and local skill-premia becomes
an empirical matter. However, a crucial lesson of this exercise is that the sectoral bias of skill-
biased knowledge diffusion greatly matters. In particular, we can judge the extent of skill-using
knowledge diffusion by observing the direction of the skill-premium - if knowledge diffusion is
great, then skill premia should rise more in more skill-intensive industries (an equilibrium such
as (5”) and (9’)). If on the other hand knowledge diffusion is limited, then skill premia should
rise more in less skill-intensive industries (an equilibrium such as (5’) and (9’)). Thus, if we can
construct a measure of global skill-biased knowledge, we may be able to empirically judge the
extent to which this knowledge flows to local economies.
From this theoretical exercise, we motivate an empirical test by conjecturing the following.
First, the greater the stock of skill-biased knowledge in the world (B∗x and B∗
y), the greater the
growth potential of skill-biased knowledge for a particular country (increases in Bx and By).
Next, the more skill-intensive the sector where technical growth occurs, the greater the effect
on(
wh
wl
), as demonstrated above. From these two points comes a testable implication - the
interaction of skill-biased knowledge stocks and skill-intensity levels should be positively related
to local skill premia. Such a positive relationship would be a strong indication of international
knowledge diffusion and the pervasiveness of skill-biased technological growth. To test this idea,
we turn to empirical evidence in world manufacturing.
9
3 Empirical Evidence on the Effects of Skill-Biased Tech-
nological Change
3.1 Empirical Strategy
In order to test some of the implications of the previous section, we make a number of gen-
eralizations. We now stray from the two sector approach of the Heckscher-Ohlin theory to a
multi-sector framework. We also allow for different labor types to be imperfectly substitutable
across sectors, so that relative factor payments can differ across the industrial sectors of a coun-
try. Finally, we assume that a few industrialized nations produce the bulk of R&D output, and
that this output tends to be skill-biased.
The basic specification on which we will focus will be:(wh
wl
)ic
= f
(SBTSic,
(H
L
)ic
)(11)
Here the left-hand side is the skill-premium in some industry i in some country c. This will be
a function of, among other things, the stock of skill-biased knowledge available to this country.
Thus SBTSic is the skill-biased technological stock available to firms in industry i in country c.
The skill-premium of course will also be a function of relative employment levels. We will be
particulary inte rested in the interaction of these two factors, predicting that skill-biased diffusion
should raise industry-specific skill premia more in more skill-intensive sectors.
We begin by constructing several different measures of skill-intensive technological levels for
countries active in research and development. We then aggregate these levels to construct various
pools of skill-intensive knowledge from which conceivably any country could draw. Finally,
we examine how such knowledge can affect the wage structures of industrial workers in both
developed and developing countries.
3.1.1 Skill-Biased Technology in Industrialized Countries
First, we define the wagebill share of skilled workers in industry i as
si ≡whiHi
whiHi + wliLi
(12)
where Hi and Li are the number of non-production and production workers (our proxies for
skilled and unskilled workers) employed in the industry, and whi and wli are their respective
wages. Skill-biased technical change in industry i would then be consistent with an increase in
si.9
9From the theory of the last section, an increase in Bi
Aiachieves precisely this.
10
To measure skill bias we use the standard approach of assuming that in each sector i, capital
is a quasi-fixed factor and that the industry minimizes the cost of skilled and unskilled labor
according to a translog cost function with constant-returns-to-scale production technology. This
type of cost function allows wide latitude to the specification of production.10 In each sector in
country k, cost-minimization leads to an equation such as
sik = α0 + α1log
(wh
wl
)ik
+ α2log
(K
Y
)ik
+ εik (13)
Here K is capital, Y is industrial output, and ε is an additive error term.
We measure skill-bias technological levels in two ways. The first is to estimate (13) for 15
industrialized countries.11 Variations in si not attributable to relative wages or capital intensities
are attributed to skill-bias technology. α0 measures the cross-sector and country average of skill-
bias technology, and so (α0 + εik) measures skill-bias technological levels in sector i in country k.
We will label this measure SBT1. This approach follows Berndt and Wood 1982 and Haskel and
Slaughter 2002, and has been used repeatedly in the empirical literature on skill-biased technical
change.
As these industrialized nations are active in research and development, we also have data on
industrial research expenditures. Thus we can also estimate
sik = γ0 + γ1log
(wh
wl
)ik
+ γ2log
(K
Y
)ik
+ γ3log
(R
Y
)ik
+ εik (14)
where R is industry-specific spending on research. Under the assumption that R&D causes skill-
bias technical change, we measure skill-bias technology as the research-intensity regressor times
its estimated coefficient, and label this measure SBT2.
Thus we have two alternative measures of skill-biased technological levels specific to industry
and country or origin:
SBT1ik = α0 + εik (15)
SBT2ik = γ3log
(R
Y
)ik
(16)
10See for example Adams )(1999).11These countries, Australia, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Nether-
lands, Norway, Spain, Sweden, the United Kingdom and the United States, are those for which sector-specificR&D expenditures are available.
11
3.1.2 Skill-Biased Technology Stocks for the World
To construct an international pool of skill-intensive knowledge, we must aggregate each in-
dustrialized nation’s implied skill-bias knowledge, making sure to weight each observation in an
informed way. We employ two types of weighting schemes. The first is to weight each industri-
alized nation’s skilled knowledge by the share of sector-specific imports that flow to a particular
country relative to all sector-specific imports from the R&D-producing nations. The underlying
assumption here of course is that international trade is an important carrier of technology. The
theory that focuses on this channel describes two general ways in which foreign trade boosts do-
mestic technology - by making available products that embody foreign knowledge and by making
available useful information that would otherwise be costly to acquire.12 In this vein, if there
are N industrialized countries active in research, a country c’s skill-biased technological stock
of foreign knowledge in industry i based on an trade-weighted (tr) approach, SBTStric , can be
described as
SBTS1tric =
N∑k=1
mikcSBT1ik (17)
SBTS2tric =
N∑k=1
mikcSBT2ik
mikc =Mikc∑N
k=1Mikc
where Mikc is the total amount of goods from industry i imported by country c from country
k. Thus for example, if Mexican imports predominantly come from the United States, we would
heavily weight the U.S.’s SBT in Mexico’s SBT tr.
We also acknowledge the possibility that spillovers to one industry may arise from imports
from other industries. As such we also construct a cross-industry spillover term, SBTSothic ,
defined as
SBTS1tr,othic =
N∑k=1
I∑j 6=i
mothikcSBT1jk (18)
SBTS2tr,othic =
N∑k=1
I∑j 6=i
mothikcSBT2jk
12See Coe and Helpman 1995, which initiates this literature. Coe et al. 1997 and Unel 2005 build on thisframework by focusing on North-South and sector-specific spillovers, respectively.
12
mothikc =
∑Ij 6=iMjkc∑N
k=1
∑Ij 6=iMjkc
where I is the total number of industries.
The other weighting scheme we use is simply to weight each skill-intensive knowledge level in
country k according to the amount of overall research in the industry performed in k relative
to all research-active countries. Then we can alternatively describe the international stock of
skill-intensive technology as
SBTS1rdic = SBTS1rd
i =N∑
k=1
rikSBT1ik (19)
SBTS2rdic = SBTS2rd
i =N∑
k=1
rikSBT2ik
rik =Rik∑N
k=1Rik
Note that while trade-weighted knowledge stocks will differ for each country c, research-
weighted knowledge stocks will be the same for all countries. Thus for example, if the U.S
spends an enormous amount on R&D spending in the professional and scientific equipment
sector relative to other R&D producers, we would heavily weight the U.S.’s SBT in Mexico’s
SBT rd. Finally, we construct a cross-industry spillover term for research-weighted stocks similar
to (18):
SBTS1rd,othi =
N∑k=1
I∑j 6=i
rothik SBT1jk (20)
SBTS2rd,othi =
N∑k=1
I∑j 6=i
rothik SBT2jk
rothik =
∑Ij 6=iRjk∑N
k=1
∑Ij 6=iRjk
3.1.3 Testing Factor and Sector-Bias in Knowledge Diffusion
We are finally ready to test for knowledge diffusion by looking at factor and sector biases in
technological spillovers. Our baseline framework is
13
log
(wh
wl
)ic,t
= αci + δt + β1log
(H
L
)ic,t−1
+ β2log
(K
Y
)ic,t−1
+ (21)
β3SBTSic,t−1 + β4SBTSothic,t−1 + εic,t
where αci are country-industry fixed effects,13 δt are year dummies, SBTS is skill-biased world
knowledge as measured by one of the approaches in section 3.1.2 (there are four such measures),
β3 captures the marginal effect of global same-industry skill-biased knowledge on local wages, and
β4 captures the marginal effect of global other-industry skill-biased knowledge on local wages. If
economies are more “globally integrated” (so that knowledge transfers instantly from one part
of the world to everywhere else), then the skill -bias of the stock of global technology should
positively influence local skill premia, irrespective of sector-bias. β3 should be positive in this
case.
If on the other hand economies are small and adoption of diffused technologies is localized, then
the sector -bias of this knowledge will affect local skill-premia. Specifically, skill-bias knowledge
diffusion should raise the skill premium more in skill-intensive industries than unskill-intensive
ones. To test the importance of sectoral bias then we also consider the following framework:
log
(wh
wl
)ic,t
= αci + δt + β1log
(H
L
)ic,t−1
+ β2log
(K
Y
)ic,t−1
+ β3SBTSic,t−1 + β4SBTSothic,t−1+
(22)
β5SBTSic,t−1
(H
L
)ic,t−1
+ εic,t
A positive β5 would imply that diffusion widens relative factor payments more in skill-intensive
industries, highlighting the relevance of sectoral bias.
3.2 Data and Measurement
The data are an imbalanced panel of 28 manufacturing industries across 56 countries (high-
lighted in Figure 6). The data are imbalanced due to sporadic coverage, particulary for less
developed countries. In general the period of coverage is 1973 - 1991. Although many of the
series are available during earlier periods, the creation of capital stocks precludes their use.
13Note that when SBTSrd are used as independent variables, only industry fixed effects (αi) are used. This isnecessary as SBTSrd does not vary across countries.
14
The final database is a match-merge of three primary data sets. The first and principal of these
is the United Nations General Industrial Statistics Database (United Nations 1992). It includes
manufacturing employment, wagebill, investment and output data for a large number of countries.
This rich data set reflects the unique capability of the United Nations in compiling data by
soliciting contributions from the statistical agencies of each country. It covers 28 manufacturing
industries at the 2 to 3-digit level. Because the main purpose of these data is to facilitate
international comparisons relating to the manufacturing sector, the data are consistently defined
across countries and years.
Like others who have used these data, our measure of skill is the classification into nonproduc-
tion and production workers (nonoperatives and operatives in UN terminology). A production
worker usually refers to employees directly engaged in production or related activities of the
establishment, including clerks or working supervisors whose job is to document or facilitate
any step in the production process. Also, employees who are engaged in activities ancillary to
the main activity of the establishment and those involved in truck driving, maintenance and
so on, are also considered to be operatives. While of course this is far from an ideal measure
of “skill,” which would explicitly include education and training, it is clear that nonproduction
workers have higher educational attainment than production workers, in part because nonpro-
duction workers are better paid than production workers in all industries and countries. Further,
highly industrialized nations in general have higher ratios of nonoperatives to operatives than
do less developed nations. An illustration of the various cross-industry skill-intensities for both
developed and developing countries is provided in Figure 7.
The identification of skill-biased technologies from (13) and (14) and the estimation of (21)
and (22) requires capital stocks. To this end we use a sum of T lagged investments for each
industry, depreciated and multiplied by estimated coefficients υT and ψT :
KTt = ψT It−1 + υT
T∑τ=1
(1− δ)τIt−τ (23)
We set δ to 0.95. The coefficients ψ and υ are estimated using investment and capital data
from the U.S. Annual Survey of Manufactures at the 2 digit level for twenty industries.14 Since
the available lag length T varies from country to country, the coefficients are estimated separately
for each lag length. Investment and output are deflated by country-specific Penn World Tables
investment and GDP deflators, respectively.
R&D expenditure data for 15 industrialized countries from 1973-1991 are provided by the
OECD’s Structural Analysis Database. Here research and development expenditure data in
PPP U.S. dollars is provided for 2-3 digit industry classifications. In general, while the United
14See Bartelsman and Gray 1994 for a detailed account of this data.
15
States and Japan provide the bulk of world R&D activity, relative research activity varies widely
across industries. Figure 8 provides a sample of this variability.
Finally, import shares are constructed using the International Trade Dataset made available
by the National Bureau of Economic Research and the United Nations (see Feenstra et al. for
documentation of this data). While this data is classified by 4-digit SITC Revision 2 trade
flows, our primary data is classified according to 3-digit ISIC codes. Maskus (1989) provides a
concordance between the two classifications, and we transform the NBER trade data into 3 digit
ISIC groups using this approach.15
3.3 Econometric Findings
Estimating (13) and (14) produce separate measures of skill-biased knowledge generated by
industrialized countries; these are respectively SBT1 and SBT2, which are charted on Figure
9. With the exception of Canada, all countries experience skill-intensive knowledge growth
as measured by (13), while all research-active countries experience skill-intensive knowledge
growth when proxied by R&D expenditures.16 The correlation between these two measures
across countries is roughly 0.5.
Figure 10 plots the relationship between these measures of skill-intensive knowledge and the
skill-intensity of the industry from where the innovation originates. Not surprisingly, we observe
a positive relationship between the two. Factor-specific endogenous growth models such as
Acemoglu (2002) and Xu (2001) highlight that researchers conceivably direct their innovative
efforts towards the more abundant factors of production. From an industry-perspective, then,
there appears to be some validity to their approach.
However, the effects of skill-bias knowledge from one country on the relative payments to
factors in other countries is not clear, as the theoretical section highlights. Skill-biased knowledge
flowing across regions can indeed induce locally rising skill-premia. Recall that this was the case
illustrated in Figure 2. However, if such knowledge does not readily flow across countries, then
rising local premia may simply be the effects of changes in the international flow of goods rather
than the international flow of technology. Recall that this was the case illustrated in Figure 3.
To shed some light on this problem we split the sample into the R&D-active economies (13
countries) and all other economies (43 countries), and estimate variants of (21) and (22) sepa-
rately for each group. Some of the results from these exercises are presented in Tables 1-3.
Table 1 shows equation (21) estimation results for R&D producing economies, both for the
pooled and fixed effects model. Coefficients of primary interest to us are marked in red. In
general skill-biased knowledge emanating from outside regions tends to widen local skill-premia.
15Given this laborious process, I feel compelled to stress that the editorial “we” really means “me.”16All estimates of the research-intensity coefficient are significant at the 99% level (not reported).
16
This finding however is not always statistically significant, and often depends both on which
model we assume and which skill-bias knowledge measure we use. Furthermore, there appears
to be a quite small negative effect on skill-premia from skill-intensive knowledge spillovers from
other industries.
Not reported are estimation results for the other non-R&D active countries, for these are all
insignificant. Hence, treating all industries equally, it would appear from this simple exercise
that while skill-biased knowledge may indeed spillover to industrialized nations, no such spillovers
exist for developing nations. This is the result that is echoed by Berman and Machin (2000).
However, as we have stressed, this may be a misleading conclusion due to the effects of trade
and specialization. Indeed, even if skill-biased knowledge diffused perfectly across the world,
these results make some sense in theory. Firstly, there are offsetting trade effects for developing
countries that developed countries do not face. Trade among industrialized nations tend to
be of the intra-industry variety, and not particularly motivated by Heckscher-Ohlin type factor
differences. Thus skill-intensive knowledge in one rich country, which raises its skill-premium, is
not very related to more skill-intensive imports for the other rich country, which would otherwise
lower its skill-premium. Between developed and developing countries, however, trade will pull
each country’s skill-premium in opposite directions. Furthermore, the developed world, as large
producers, tend not to be price takers in world markets. As such the sectoral-bias of skill-biased
technological change will matter less for these nations. Developing countries on the other hand
will behave more like price takers - as such if levels of SBTS are higher in the unskill-intensive
sectors, skill-premia will be lower, not higher.
For both these regions we shift our focus on the interaction between skill-intensity and skill-
biased knowledge diffusion. Table 2 provides some evidence of the effects of this interaction for
industrialized economies. In general, when the interaction term is included, the effect of skill-bias
knowledge alone tends towards insignificance. The coefficient on the interaction term however is
always positive and significant. From this we gather that the effects of skill-intensive knowledge
on wage dispersion tends to be higher in more skill-intensive industries. Given our discussion in
section 2, this implies that skill-bias knowledge does indeed diffuse across the developed world.
Further, this result holds no matter what measure of skill-bias knowledge we employ.
What about the rest of world? Here the results appear much weaker, but nevertheless indicate
the presence of sectoral biases. Table 3 present some of the findings for SBTC1; SBTC2 produce
similar effects (not reported). Specifications ii, iv, vi and viii exclude capital from the estimation
of (22), allowing us to roughly double the number of observations. While the low R2’s imply
that technologies from the “West” can not account for much of the variability in the factoral
distribution of income for less developed regions, we still detect more wage dispersion from
skill-bias technology in more skill-intensive industries. Indeed, once we include the cross term,
17
skill-bias world knowledge by itself seems to have a significantly negative effect. Again, this
points to skill-using knowledge diffusing to a wide range of countries. Ultimately, the study of
sectoral biases are crucial in analyzing the effects of knowledge diffusion in both developed and
developing countries.
4 Conclusion
There are a number of tentative lessons from this study. Developed countries not only generate
their own research output, but they also use and are influenced by the research output of other
countries. Comparing Table 2 with Table 3, the developing world appears far less influenced by
these factors; it seems then that we remain in strong need of theories concerning the barriers to
technical diffusion and adoption.
And yet evidence would suggest that the developing world is influenced by the skill-using
knowledge of the West, and this influence can be seen by observing the sectoral biases of this
technological progress. Indeed such biases in knowledge may mask their influence on relative
factor payments - if technologies grow in both skill- and unskill-intensive sectors, skill-premia
may gyrate within particular industries while the overall effect appears muted. Greater empirical
insight into these biases is needed, and studies that fail to acknowledge such biases do so at their
peril.
Another possible lesson is that a country may not be able to stem inequality arising from
external technology diffusion simply by closing its borders. Whether trade-weighted or R&D
weighted, it would appear that skill-bias knowledge widens the factoral distribution of income,
particularly in the developed world. Thus skill-bias knowledge may permeate to different coun-
tries irrespective of trade flows. No doubt there are many benefits from international trade, but
whether or not they are a primary conduit of knowledge flow is a far from settled question, and
not one that this study can answer.
18
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20
Figures and Tables
Figure 1: Determination of Relative Factor Prices
Figure 2: Skill-Biased Technological Growth with Perfect Diffusion in an Integrated Economy
(5)
(9)
(wh/wl)
(wh/wl)*
(9’)
Zero- Profit Condition Equation (5)
Full Employment Condition Equation (9)
(wh/wl)
(wh/wl)*
Figure 3: Foreign Skill-Biased Technological Growth in the Unskilled-Intensive Sector with No Diffusion
Figure 4: Skill-Intensive Knowledge Growth in the Small Home Region – Two Cases
(5)
(9)
(wh/wl)
(wh/wl)*
(9’)
(5’)
(wh/wl)* (wh/wl)*
(wh/wl) (wh/wl)
(5)
Case A – Skill-Using Tech Growth in Skill-Intensive Sector
Case B – Skill-Using Tech Growth in Unskill-Intensive Sector
(9)
(5)
(9)
(5’) (5’)
Figure 5: Skill-Intensive Knowledge Growth in the Foreign Region with Some Diffusion Home – Two Cases
(wh/wl)* (wh/wl)*
(wh/wl) (wh/wl)
(9)
(5) (5’)
Case A – Skill-Using Tech Growth in Skill-Intensive Sector
Case B – Skill-Using Tech Growth in Unskill-Intensive Sector
(9’)
(5’)
(5)
(9’)
(9)
(5’’) (5’’)
Figure 6: Countries Sampled
A total of 56 countries are used in this study, although the lack of investment data for some limits much of the regression analysis to 45 countries. Countries in red indicate where research and development takes place, orange are other developed regions without knowledge of R&D expenditures, and yellow indicates less developed countries.
Figure 7: Skill Intensity by Industry
0.2
.4.6
.8
300311313314321322323324331332341342351352353354355356361362369371372381382383384385390
0.2
.4.6
mea
n of
su
300311313314321322323324331332341342351352353354355356361362369371372381382383384385390
These industries are, from left to right, food products, beverages, tobacco, textiles, wearing apparel, leather products, footwear, wood products, furniture, paper, printing & publishing, industrial and other chemicals, petroleum and coal products, rubber products, plastics, pottery, glass, other mineral products, iron and steel, non-ferrous metals, fabricated metals, machinery, electrical equip., transport equip., scientific equip., and misc.
Skill and R&D Intensities for Industrialized Countries, 1990
Skill Intensities for Non-Industrialized Countries, 1990
.
Figure 8: Share of World R&D Expenditures, 1990 Food, Beverages and Tobacco Textile and Leather Products
Petroleum Products Iron and Steel
Non-electrical Machinery Professional & Scientific Equipment
The 15 countries documented by the OECD Structural Analysis Database active in research and development are Australia, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, Spain, Sweden, the United Kingdom, and the United States. Pie charts are presented with Australia on top, then going alphabetically moving clockwise.
U.S.
U.K.
Fra
Jpn
Ger
U.S.Ger
Jpn
U.S.
U.S.
U.S.
U.S.
Fra
Fra Fra
Ger
Ger
Jpn
Jpn
Jpn
Jpn
U.K.
U.K. Ire
Figure 9: Skill-Biased Technologies in Research Active Countries
Estimated from (α0 + εik) from equation (11) .3
.4.5
.6Skill-
Bia
sed
Tech
nica
l Lev
els
1975 1980 1985 1990year
Estimated from γ3 • log(R/Y)ik from equation (12)
.3.4
.5.6
Skill-
Bia
sed
Tech
nica
l Lev
els
1975 1980 1985 1990year
U.S.
Jpn
Den
Ger
Can
Esp
U.K
Aus
Fin
Jpn
U.S.
Ger
U.K.
Figure 10: Skill-Biased Technologies vs. Skill Intensity
0.2
.4.6
.81
H/L
0 .2 .4 .6 .8SBT1
0.2
.4.6
.81
H/L
0 .1 .2 .3 .4 .5SBT2
Pooled across all industries and years, the correlations between skill-intensity and SBT1 and SBT2 are 0.14 and 0.11, respectively.
Table 1: Evidence of Skill-Biased Knowledge Diffusion in R&D Active Countries
Coefficient Variable (i) (ii) (iii) (iv) (v) (vi) (vii) (viii)
β1 log (H/L) -0.188 -0.344 -0.16 -0.345 -0.185 -0.33 -0.16 -0.33 (0.023) (0.011) (0.025) (0.011) (0.031) (0.011) (0.03) (0.01)β2 log (K/Y) insig 0.038 insig 0.038 insig 0.03 insig 0.03 (0.008) (0.008) (0.009) (0.009)β3 SBTS1tr 0.554 0.143 --- --- --- --- --- --- (0.099) (0.051)β4 SBTS1oth,tr -0.025 insig --- --- --- --- --- --- (0.004)β3 SBTS2tr --- --- 0.321 insig --- --- --- --- (0.17) β4 SBTS2oth,tr --- --- -0.018 insig --- --- --- --- (0.007) β3 SBTS1rd --- --- --- --- 0.46 insig --- ---
(0.11)β4 SBTS1oth,rd --- --- --- --- insig insig --- ---
β3 SBTS2rd --- --- --- --- --- --- 0.52 0.81 0.26 (0.46)β4 SBTS2oth,rd --- --- --- --- --- --- insig insig
Time Effects Yes Yes Yes Yes Yes Yes Yes YesFixed Effects No Yes No Yes No Yes No Yes# of observations 5123 5123 5123 5123 4734 4734 4734 4734R2 0.255 0.18 0.2 0.18 0.19 0.15 0.18 0.16The dependent variable is log (wh / wl). Only statistically significant results are reported.Definitions: H/L is ratio of non-operatives to operatives; K/Y is capital-output ratio; SBTS1 is skill-biased tech stock as estimated byequation (11); SBTS2 is skill-biased tech stock as estimated by equation (12); superscript tr indicates that technologies are trade-weighted;superscript rd indicates that technologies are R&D-weighted.
Table 2: Evidence of Sector-Biased Knowledge Diffusion in R&D
Active Countries
Coefficient Variable (i) (ii) (iii) (iv) (v) (vi) (vii) (viii)β1 log (H/L) -0.27 -0.378 -0.255 -0.38 -0.31 -0.39 -0.31 -0.41 (0.023) (0.012) (0.027) (0.013) (0.03) (0.015) (0.03) (0.018)β2 log (K/Y) insig 0.039 insig 0.04 insig 0.03 insig 0.03 (0.008) (0.008) (0.009) (0.009)β3 SBTS1tr 0.259 insig --- --- --- --- --- --- (0.12) β4 SBTS1oth,tr -0.024 insig --- --- --- --- --- --- (0.004)β5 (H/L)•SBTS1tr 0.598 0.336 --- --- --- --- --- --- (0.151) (0.063) β3 SBTS2tr --- --- insig insig --- --- --- --- β4 SBTS2oth,tr --- --- -0.018 insig --- --- --- --- (0.006) β5 (H/L)•SBTS2tr --- --- 0.614 0.334 --- --- --- ---
(0.21) (0.065)β3 SBTS1rd --- --- --- --- 0.24 -0.225 --- --- (0.1) (0.07)β4 SBTS1oth,rd --- --- --- --- insig insig --- --- β5 (H/L)•SBTS1rd --- --- --- --- 0.69 0.43 --- ---
(0.13) (0.07)β3 SBTS2rd --- --- --- --- ---- --- 0.56 insig (0.24) β4 SBTS2oth,rd --- --- --- --- --- --- insig insig β5 (H/L)•SBTS2rd --- --- --- --- --- --- 0.67 0.48
(0.12) (0.09)Time Effects Yes Yes Yes Yes Yes Yes Yes YesFixed Effects No Yes No Yes No Yes No Yes# of observations 5123 5123 5123 5123 4734 4734 4734 4734R2 0.26 0.2 0.23 0.19 0.2 0.18 0.22 0.19The dependent variable is log (wh / wl). Only statistcially significant results are reported.Definitions: H/L is ratio of non-operatives to operatives; K/Y is capital-output ratio; SBTS1 is skill-biased tech stock as estimated byequation (11); SBTS2 is skill-biased tech stock as estimated by equation (12); superscript tr indicates that technologies are trade-weighted;superscript rd indicates that technologies are R&D-weighted.
Table 3: Evidence of Sector-Biased Knowledge Diffusion in Less Developed Countries
Coefficient Variable (i) (ii) (iii) (iv) (v) (vi) (vii) (viii)β1 log (H/L) -0.12 -0.16 -0.43 -0.38 -0.14 -0.13 -0.47 -0.37 (0.019) (0.017) (0.01) (0.007) (0.02) (0.02) (0.01) (0.009)β2 log (K/Y) -0.055 --- 0.013 --- -0.06 --- insig --- (0.01) (0.005) (0.01) β3 SBTS1tr -0.302 insig -0.16 -0.197 --- --- --- --- (0.105) (0.05) (0.04)β4 SBTS1oth,tr 0.026 0.02 0.008 0.01 --- --- --- --- (0.005) (0.005) (0.003) (0.002)β5 (H/L)•SBTS1tr 0.037 0.07 0.068 0.064 --- --- --- --- (0.017) (0.02) (0.01) (0.01) β3 SBTS1rd --- --- --- --- insig insig insig insig β4 SBTS1oth,rd --- --- --- --- insig insig insig insig β5 (H/L)•SBTS1rd --- --- --- --- 0.04 0.03 0.08 0.06
(0.01) (0.01) (0.01) (0.01)
Time Effects Yes Yes Yes Yes Yes Yes Yes YesFixed Effects No No Yes Yes No No Yes Yes# of observations 7018 13434 7018 13434 6571 9965 6571 9965R2 0.08 0.07 0.04 0.055 0.07 0.04 0.04 0.03The dependent variable is log (wh / wl). Only statistcially significant results are reported.Definitions: H/L is ratio of non-operatives to operatives; K/Y is capital-output ratio; SBTS1 is skill-biased tech stock as estimated byequation (11); superscript tr indicates that technologies are trade-weighted; superscript rd indicates that technologies are R&D-weighted.
