Trade, Technology, and the Rise of the Service Sector: The
Effects on US Wage Inequality∗
Bernardo S. Blum
†
June 2007
Abstract
This paper uses a multi-sector version of the Ricardo-Viner model of international
trade to quantify empirically the effects of technological changes, international trade,
changes in the sectoral composition of the economy, and other factors on the US wage
premium. The main finding of the paper is that changes in the sectoral composition
of the economy were the most important force behind the widening of the wage gap,
accounting for about 60% of the relative increase in wages of skilled workers between
1970 and 1996. In essence, capital was reallocated from manufacturing sectors, where
it is relatively complementary to unskilled workers, to services, retail, and wholesale
trade sectors, where it is relatively complementary to skilled workers. The existing
literature misses this point because it focuses on reallocations within sectors or among
traded sectors. The paper also finds that economy-wide capital deepening and skillbiased changes in the demand for labor, stemming from either technological changes or
outsourcing, play a role explaining inequality in the period.
JEL classification: F1, F11, D33
Keywords: International Trade, Ricardo-Viner, Income Inequality.
∗
I am very grateful to Ig Horstmann. I also thank Ed Leamer, Dan Trefler, Diego Restuccia, Gustavo
Bobonis, Will Strange, Joe Hotz, Matthew Slaughter, Arnold Harberger, Naomi Lamoreaux, Sebastian
Edwards, and participants of the trade workshop at UCLA, NYU Stern, Purdue University, and University
of Toronto. All errors are mine.
†
Contact: Bernardo Blum, University of Toronto, 105 St. George Street, Toronto Ontario M5S 3E6,
Canada. e-mail to: [email protected]
1
Introduction
The literature on income and wage inequality is now very extensive. Because the observed
rise in the wage premium for skilled workers in the US happened at the same time as
increases in the supply of skilled workers, it is generally accepted that changes in labor
supply cannot explain the data patterns.1 If what happened was a rise in the demand for
skills, the question then becomes what caused this rise. Economists have two likely suspects:
international trade and skill-biased technological changes.
International trade is generally thought to raise the demand for skills in two ways: 1)
through changes in prices of tradable goods, as proposed by the Heckscher-Ohlin (henceforth HO) model2 and 2) through outsourcing. In the HO mechanism, the demand for
skills goes up if prices of skilled intensive tradable products increase relative to the price of
unskilled intensive products (see Slaughter (1999) for a survey of this literature). Leamer
(2001) shows that this mechanism was important in the 1970s, a decade where wage inequality did not increase significantly, but it was not important in the 1980s and 1990s. In
the outsourcing mechanism, the demand for skills rises if unskilled intensive intermediate
inputs are outsourced to poorer countries. Feenstra and Hanson (1999) estimate that the
outsourcing effect accounts for 15%–24% of the observed increase in the demand for skilled
workers that occurred between 1970 and 1996.
An alternative explanation for the rising US inequality is skill-biased technological
change. In common with the outsourcing mechanism, but different from the HO mechanism, skill-biased technological change raises the demand for skills within sectors. Berman
et al. (1994) shows that two-thirds of the observed change in employment of skilled workers
and more than half of the observed change in the relative earnings of skilled workers occurs
within industry. In an attempt to link within-industry changes in relative earnings and
technological changes, Autor et al. (1998) and Feenstra and Hanson (1999) estimate the
share of the change in relative earnings of skilled workers that can be explained by changes
in industry computerization. They find that between 8% and 36% of the within-industry
1
It should be noted, however, that Card and Lemieux (2001) show that the rise in the college/high school
wage gap for young relative to old men reflects changes in the relative supply of educated workers across age
groups.
2
Acemoglu (2003) proposes a new mechanism through which trade would affect the degree of skill bias
of technological changes. There isn’t, however, direct empirical evidence supporting this channel.
1
change in wage inequality can be accounted for by increasing computerization. However,
Leamer (2001) discusses reasons why evidence of within-industry changes in relative wages
may not be taken as support for the hypothesis of skill-biased technological changes, and
Card and DiNardo (2002) show evidence that skill-biased technological change cannot explain important features of the data.
A third and yet unexplored source of the increased demand for skills is the structural
shift in the US economy from manufacturing to services and other non-tradable sectors.3
Beginning in 1979, we observe a fall in employment levels and capital accumulation in
manufacturing and a sharp rise in these measures in non-manufacturing sectors. If capital
is relatively complementary to skills outside of manufacturing, such a structural shift would
have caused increases in the wage premium. The fact that the large rise in US wage
inequality occurred in the late 1970s makes it crucial to investigate this mechanism. Note
that previous conclusions that sectoral shifts are not important have either only looked
at shifts within manufacturing, not between manufacturing and non-manufacturing (e.g.
Berman et al (1994), or looked at the effects of employment composition changes and
missed the mechanism I propose in this paper.4
The purpose of this paper is to estimate the relative importance of these mechanisms in
determining the observed changes in the wage premium. The paper builds a multi-sector
general equilibrium model that is then used to estimate the effects of international trade,
technological changes, sectoral reallocation, and other factors on the observed wage premium
changes in the US. The model developed is a generalized version of the Ricardo-Viner model
(henceforth RV) that allows for tradable and non-tradable sectors. In the RV specification,
prices of internationally traded products and total factor productivity changes affect the
wage premium, just as in the HO model. However, the RV model has several useful features
not found in the HO model. First, it is much easier to handle non-tradable sectors in the
RV model. Second, in the RV framework changes in labor supply, capital accumulation, and
changes in the relative demand for skills within industry, either motivated by skill-biased
3
Harrigan and Balaban (1999), Harrigan (2000), and Kumar (2000) highlight the importance of the
services sector in explaining the observed rises in income inequality. The mechanism they propose relates
the skill premium with price changes in skilled and unskilled intensive non-traded goods and is therefore
very different than the one developed here.
4
See Juhn, Murphy, and Pierce (1993), Bound and Johnson (1992), and Valletta (1997) for example.
2
technological change or by outsourcing, also can have a direct impact on wages.5 Therefore,
the RV model allows me to examine the trade, technological change, and factor supply
change explanations within a single specification. By being able to accommodate nontradable sectors, the RV model also can examine the role of sectoral reallocation between
manufacturing and non-manufacturing. In this way, the RV specification allows me to
assess, in a unified framework, the various explanations for the observed pattern of wage
inequality.
The main finding of the paper is that changes in the sectoral composition of the economy
were the most important force behind the experienced rise in wage inequality. Over the
period 1970–1996, sectoral changes account for approximately 60% of the observed changes
in the wage premium. In essence, what happened was that capital reallocated from manufacturing sectors, where it is relatively complementary to unskilled workers, to services,
retail, and wholesale trade sectors, where it is relatively complementary to skilled workers.
Several other conclusions emerge. First, price competition due to international trade
was a source of inequality in the 1970s but not in the 1980s and 1990s. In absence of
other factors that worked to reduce inequality, during the mid-1970s, trade alone would
have resulted in approximately a 4% rise in the wage premium. This confirms previous
results in the literature (see Leamer (2001)). Second, capital deepening contributed to a
higher wage premium, also confirming findings in the literature (see Krusell et al. (2000)).
However, when looking at it decade by decade, changes in capital stock and in labor supplies
contributed to inequality in the 1970s and 1990s but not in the 1980s, the decade when
inequality rose most significantly. Third, and also consistent with previous results in the
literature, skill-biased changes in the demand for labor, stemming from either technological
change or outsourcing, played a role in explaining the rising inequality (see Berman et al.
(1994), Autor et al. (1998), and Feenstra and Hanson (1999)). In the 1970s it pushed for
a smaller wage premium. In that decade, without those changes the wage premium would
have been up to 20% larger than the observed. In the 1980s and 1990s, skill-biased changes
contributed significantly to wage inequality, accounting for as much as 50% of the observed
5
This is not the case in the HO model. Because the standard HO model assumes perfect factor mobility
across sectors, changes in factor supplies and in the relative demand for skills within sectors do not affect
factor returns.
3
patterns. However, when looking at the overall period 1970–1996, these changes cannot
explain the observed widening of the wage gap. Had they not occurred, the widening of the
skill premium would have been pretty much the same as that observed in the data.
Overall, I find that even though for different decades different mechanisms played important roles for the 1970–1996 period as a whole, sectoral reallocation out of manufacturing
and into services, retail, and wholesale trade sectors was the main contributor to the observed rise in wage inequality.
The outline of the paper is as follows. Section 2 reviews the time-series evidence on
sectoral reallocation out of manufacturing; Section 3 builds a generalized empirical version
of the Ricardo-Viner model; Section 4 presents the new data set that was constructed for
use in this paper; Section 5 discusses some estimation issues; Section 6 presents the results
and Section 7 discusses some final considerations and conclusions.
2
An economy moving away from manufacturing and towards inequality
By now the evidence suggesting that international trade and skill-biased technological
changes might be linked to the rise in wage inequality is well known. The evidence pointing
to a connection between trade and inequality is mostly circumstantial. After a long period
of stability, the gap between wages of skilled and unskilled workers started to widen at a
time when the United States was experiencing a rapid increase in trade dependence. The
share of merchandising trade in GDP that had been around 7% since the Second World
War started increasing in the mid-1970s, reaching almost 20% by the mid-1980s. In addition, there is anecdotal evidence that in the 1990s unskilled jobs have been “outsourced”
to low-wage countries, reducing the demand for skills in the US.6
The evidence connecting technological change to the wage gap is less circumstantial.
As Leamer (1998) points out, it is widely believed that technological changes have been
altering the nature of work. Functions have been downgraded to require a minimal level
of education, ultimately reducing the demand for skills. These changes, it is argued, are
6
See Feenstra and Hanson (1999) for a detailed discussion about the evidence on the connections between
international trade, technological changes, and inequality. In this article, special attention is placed on the
effects of outsourcing. See Zhu and Trefler (2004) for evidence from a poor-country perspective.
4
reflected in the data by the fact that the skill premium rose despite the increasing relative
supply of skills for the entire post-war period. This pattern would indicate that skill-biased
technological changes have been increasing the demand for skills. More concrete evidence
on the effects of new technology was obtained by linking computerization levels and relative
earnings by industry.7
Less widely known is the fact that the observed rise in wage inequality happened suspiciously at a time when the US economy was going through an acceleration of a “structural
change.” Since the beginning of the last century and until the 1970s, the US economy
had been shifting away from agriculture and towards manufacturing and service sectors.8
Since the late 1970s, however, the expansion of service sectors has occurred at the expense
of manufacturing jobs and capital. If until then the “structural change” characterized the
transition of the US economy from agrarian to industrialized, since then it characterizes an
acceleration of the transition from an industrial to a post-industrial economy. At the same
time, the wage structure of the economy changed as well. The return to skills increased dramatically, causing a significant change in wage inequality. The above patterns are depicted
in figures 1–3.9
Figure 1 shows employment numbers in manufacturing and non-tradable sectors10 over
the twentieth century. The vertical line in this figure and in the figures that follow mark
the year 1979. The data show that it is not until the late 1970s that the US economy starts
to move away from manufacturing and towards non-tradable sectors. Figure 2 confirms this
by showing that it is not until the late 1970s that capital started to accumulate faster in
non-tradable sectors than in manufacturing.
The US transition from an industrial to a post-industrial economy coincided with the
observed increase in wage inequality. Figure 3 shows ratios of the average wage of skilled
7
See Autor et al. (1998).
The term “structural transformation” refers to the pattern of reallocation of resources across broad
sectors that accompanies economic development. Resources tend to move from agriculture to manufacturing
and then to services. Such a reallocation of resources has been documented at least since Clark (1940) and
Kuznets (1957). Indeed, Kuznets included the process of structural transformation as one of the six main
stylized facts of development. See Kongsamut et al. (2001), Ngai and Pissarides (2006), and Rogerson (2005)
for modern approaches.
9
A full description of the data displayed in these figures and used in the remaining of the paper can be
found in the online appendix available at www.rotman.utoronto.ca/bblum/personal/front.htm .
10
Services, wholesale trade, and retail trade sectors are used as a proxy for non-tradable sectors.
8
5
Figure 1: Employment (1000 of workers).
and unskilled workers in manufacturing and non-tradable sectors over the period 1964 to
1996.11 The figure reveals three main patterns. First, wage inequality is much higher in
non-manufacturing sectors than in manufacturing. Second, changes in the wage premium
in manufacturing and non-tradable sectors are highly correlated. Indeed, the correlation
coefficient between these two series over the period 1964 to 1996 is 0.86. This suggests
a high degree of labor mobility between these two sectors, despite differences in the way
skilled and unskilled are defined in those sectors. Third, and more importantly, it is not
until the late 1970s that wage inequality in the US started to rise. This, by the way, is a
well-documented phenomenon in the literature (see Acemoglu (2003)).
3
The Model
This section builds a multi-sector general equilibrium model that is then used to estimate the
effects of international trade, technological change, sectoral reallocation, and other factors
on the observed changes in the wage premium. The model developed is a generalized version
of the RV model that allows for tradable and non-tradable sectors. In the RV specification,
11
In manufacturing, non-production and production workers are used as a proxy for skilled and unskilled
workers, while supervisory and non-supervisory workers are used as a proxy for skilled and unskilled workers
in the non-tradable sectors. For a full description of the occupations included in each of these categories see
the online appendix available at www.rotman.utoronto.ca/bblum/personal/front.htm.
6
Figure 2: Capital accumulation rate (3-year moving-average % change).
Figure 3: Ratio of average wages of supervisory over non-supervisory workers.
7
prices of internationally traded products and total factor productivity changes affect the
wage premium, just as in the HO model. Unlike the HO model, changes in labor supply,
capital accumulation, and changes in the relative demand for skills within industry can also
have a direct impact on wages in the RV framework. Therefore, the model developed in
this section allows me to examine the trade, technological change, factor supply change,
and sectoral reallocation explanations within a single specification.
The vast literature on wage inequality has not yet used a model with the properties described above. Most of the literature uses, either explicitly or implicitly, partial equilibrium
or one-sector models. Work that has used a multi-sector general equilibrium framework
elected to use the standard HO model.12 This model cannot address the issues proposed
in this paper for a variety of reasons. First and most importantly, in the standard HO
model, changes in labor supply, capital accumulation, factor-biased technological changes,
and outsourcing cannot have any impact on wages.13 Because of the assumption that
every production factor is perfectly mobile across sectors of the economy, changes in those
variables will be fully absorbed by output-mix changes.14 In addition, the standard HO
model deals only with tradable goods. Even though the model can be easily extended to
incorporate non-tradable goods, such extended versions do not lend themselves to empirical
implementations. When accounting for non-tradable goods, the HO model has multiple
equilibria; in some of the equilibria, the demand for non-tradable goods affects wages while
in others it does not.
3.1
The 3-factor N -good Ricardo-Viner model
Suppose the economy is able to produce N goods, M of which are internationally tradable,
using capital and two types of labor, unskilled and skilled. Markets are competitive, the
economy is always in full-employment, technologies exhibit constant returns to scale, and
capital is assumed to be sector specific in the short-run. In other words, in the short-run,
a sewing machine cannot be converted into a car assembly robot. In addition, assume that
aggregate demands for non-tradable goods are derived from Cobb-Douglas preferences.
12
See Slaughter (1999) for a survey of this literature. Two exceptions are Feenstra and Hanson (1997) and
Zhu and Trefler (2005) who develop models that are Ricardian in spirit.
13
Unless indirectly through changes in product prices.
14
See Leamer (1995) for a discussion on this property of the HO model.
8
In this setting, exogenously given prices of tradable goods, factor supplies, sectoral capital allocation, preferences, and technologies determine endogenously factor returns, prices of
non-tradable goods, sectoral employment allocations, and sectoral output. The equilibrium
is characterized by the following set of equations:
aui wu + asi ws + aki ri = pi
N
X
aui xi = U
i=1
N
X
asi xi = S
i = 1, 2, ..., N
i=1
ci =
κi I
pi
i = M + 1, M + 2, ..., N
The first equation is a zero-profit condition. pi is the price of product i, wu and ws are wages
of unskilled and skilled workers, ri is the return on capital in sector i, and aij is the inverse
productivity of production factor j in sector i.15 The next two equations are full-employment
conditions for unskilled (U ) and skilled workers (S). The last equation is the aggregate
demand for non-tradable goods, where ci is the quantity demanded of the non-tradable
good i, I is the country’s aggregate income, and κi is a constant. Throughout the analysis,
tradable goods are indexed by i = 1, ..., M and non-tradable goods by i = M + 1, ..., N .
Adapting Jones’ (1965) model to the case of fixed capital stocks and two mobile factors,
I express factor intensities as functions of factor prices and the state of technology: aij =
aij (wu , ws , ri , ti ).16 Therefore growth in each aij can be decomposed as âij = dˆij − b̂ij ,
∂aij 1
where b̂ij = −
captures the effects of technological changes. Defining θij to be
∂ti aij
the cost-share of factor j in sector i, total factor productivity in sector i can be written
as T Fˆ P i =
X
θij b̂ij . Further, the skill bias of technological change in sector i can be
j
ˆ s = b̂is − b̂ik and CAP
ˆ u = b̂iu − b̂ik .
expressed as the difference between CAP
i
i
Totally differentiating the equilibrium conditions above and imposing that the markets
for non-tradable goods clear, we obtain the link between changes in the wage premium
and its determinants. This link is given by the system of equations (1) below. It is this
system of equations that will be taken to the data in order to decompose the observed
i
aui = U
where xi is value added in sector i.
xi
Note that the return on capital in sector i depends, among other things, on sector’s i capital stock and
product price.
15
16
9
changes in the economy’s wage premium into components attributable to international
trade, technological changes, capital accumulation, labor supply changes, and changes in
the sectoral composition of the economy. The first equation gives the direct effect of changes
in the exogenous variables of the model (prices of tradable goods, technology, capital stock,
and the supply of skilled and unskilled workers), and in prices of non-tradable goods, which
are endogenously determined, on the wage premium. These are the effects that do not work
through changes in the endogenously determined prices of non-tradable goods. The second
equation (actually a set of equations) shows implicitly how the direct effects mentioned
above affect prices of non-tradable goods.17
(ŵs − ŵu ) = Φs
N
X
− Φu
i=1
N
X
+ Φu
i=1
N
X
i
i
(ηss
+ ηsu
)λsi (p̂i + T Fˆ P i ) −
i
i
(ηuu
+ ηus
)λui (p̂i + T Fˆ P i ) − Φu Û + Φs Ŝ +
s
i
ˆ ) − Φs
λui ηuk
(K̂i − CAP
i
i=1
N
X
u
i
ˆ )
λsi ηsk
(K̂i − CAP
i
i=1
u
(1)
s
ˆ K̂, CAP
ˆ
ˆ , Û , Ŝ, ŵs , ŵu
p̂i = g p̂, TFP,
, CAP
i = M + 1, ...N
i and η i are the own-price and cross-price elasticities
In the system of equations above, ηuu
us
i and η i can be defined
of demand for unskilled labor in sector i. The parameters ηss
su
i and η i are the unskilled and skilled labor demand elasticities with respect
analogously. ηuk
sk
to capital accumulation in sector i, λui is the share of unskilled workers in sector i, and p
is the vector of prices of tradeable goods. As discussed above, T Fˆ P i is the rate of total
ˆ u and CAP
ˆ s measure the bias of
factor productivity improvement in sector i, and CAP
i
i
technological change.
Φu and Φs are the wage premium elasticity with respect to the supply of unskilled
and skilled workers. In general equilibrium, these elasticities are combinations of sectoral
demand elasticities and shares of unskilled and skilled labor employed in each sector18 .
The next section discusses in detail how the system of equations (1) links changes in the
wage premium to international trade, technological changes, capital accumulation, labor
17
A detailed derivation of the system of equations (1) is provided in the online appendix, available at:
http://www.rotman.utoronto.ca/bblum/personal/front.htm.
P
P
N
18
The precise definition of Φu is given by Φu = PN
(
i=1
10
i λ
ηss
si
PNi=1
i=1
i λ +
ηss
si
N
Pi=1
N
i λ −
ηuu
ui
i=1
i λ
ηsu
si
i λ
ηsu
si
PN
i=1
i λ )
ηus
ui
supply changes, and changes in the sectoral composition of the economy.
3.2
The determinants of wage inequality
The system of equations (1) captures the effects of trade, technological change, labor supply changes, and changes in the sectoral composition of the economy on relative wages.
Throughout this section, it is assumed that labor demands are more responsive (elastic) to
i | > |η i | and |η i | > |η i |). This
own-price variations than to cross-price variations (i.e. |ηuu
us
ss
su
is a sufficient condition for having the wage premium elasticities with respect to unskilled
s
ŵ
and skilled correctly signed as ( ∂(ŵ∂−
Û
u)
= −Φu > 0) and
(∂ ŵs −ŵu )
∂ Ŝ
= Φs < 0). This
condition always holds in the data.
s
ŵ
Increases in the supply of skilled workers will decrease the wage premium ( ∂(ŵ ∂−
Ŝ
u)
<
0), while increases in the supply of unskilled workers will increase the wage premium
s
ŵ
( ∂(ŵ∂−
Û
u)
> 0). As mentioned before, unlike in the HO model, the labor demands are
downward sloped in the general equilibrium model developed here. In addition to that, the
effects of labor supply changes on wages will trigger changes in the supply of and demand
for non-tradable goods, potentially changing the price of these goods and therefore the wage
premium. When taking the model to the data, both the direct effect and the effect through
changes in the demand for non-tradable goods will be taken into account.
In the model developed, international trade can affect wages through different channels.
The first channel has competition with low-wage foreign sources of supply affecting wages
by changing the international prices of tradable goods. This is the same channel through
which trade affects wages in the HO model, and it is the most commonly studied in the
trade literature. The expression linking product price and wage premium changes is given
∂(ŵs − ŵu )
i
i
i
i
by
= Φs (ηss
+ ηsu
)λsi − Φu (ηuu
+ ηus
)λui .
∂ p̂i
A decrease in product price in sector i will tend to increase the wage premium if the
demand for unskilled labor is more elastic than the demand for skilled labor in this sector,
and if the fraction of unskilled labor employed in this sector is larger than the fraction of
skilled labor. In general equilibrium, the effects of a product price change on wages will
also depend on the elasticity of the skill premium with respect to the supply of skilled and
unskilled workers in the other sectors of the economy. Given that labor is mobile across
sectors, workers released from a sector will put pressure on the overall labor market.
11
The mechanism through which traded goods’ prices affect a country’s wage distribution
here is different from the Stolper-Samuelson effect. A price fall in industry j will increase
the ratios
wu
rj
and
ws
rj
because the immobile factor bears most of the cost. As a consequence,
industry j will substitute away from both types of labor. The type of labor with the most
elastic demand is the one that has its demand decreased proportionally the most. This
i + η i )λ and (η i + η i )λ
effect is captured by the terms (ηss
su si
uu
us ui in the expression above.
The wage premium elasticity with respect to labor supply (Φs and Φu ) captures the impact
of such demand changes on the economy’s wage premium. Price decreases in sectors where
the unskilled labor demand is relatively more elastic than the skilled labor demand leads
to increases in the skill premium, and vice-versa.19
A second way that international trade can affect wages is through outsourcing. If unskilled labor intensive parts of the productive process are now performed abroad, that will
show as a factor-biased change in the demand for skills, and it will be captured by the CAPi
variables.20
Two types of technological change can affect the economy’s wage premium: Hicks-neutral
sectoral productivity improvements and factor-biased technological changes. Hicks-neutral
technological changes affect factor returns in the same way as product price changes. Factorbiased technological changes that make industries use higher ratios of skilled over unskilled
workers will generate an excess supply of unskilled workers in the economy. Given that
output-mix changes cannot fully absorb the displaced unskilled workers, an increase in the
wage premium ends up being the outcome.
Finally, changes in the sectoral composition of the economy will affect the wage premium
to the extent that capital accumulates at different rates in sectors where it is relatively more
complementary/substitutable for unskilled or skilled workers. If capital accumulates faster
i > η i ) and in
in sectors where it is relatively more complementary to skilled workers (ηsk
uk
19
This link could be alternatively stated in terms of the elasticity of substitution between unskilled labor
and capital and skilled labor and capital in production. In this case, price decreases in sectors where unskilled
labor is relatively more substitutable for capital than skilled labor lead to increases in the skill premium,
and vice-versa
20
In the RV model developed, trade can possibly affect wages through two additional channels that are
not explored in this paper. First, it might affect the aggregate demand for non-tradable goods. Large and
persistent imbalances in the current account is one example of how trade can affect the aggregate demand for
non-tradable goods. Second, higher competition induced by international trade can affect firms’ behavior,
thereby also affecting labor and capital demand elasticities (see Slaughter (2001)).
12
sectors that employ a larger share of skilled workers (λsi > λui ), the wage premium will
increase. This effect is distinct from the effects of economy-wide capital accumulation, which
might also affect the wage premium if capital is relatively complementary/substitutable to
skilled workers in the economy as a whole (see Krusell et al (2000)). The effect of changes
∂(ŵs − ŵu )
i
i
in sectoral capital stocks is given by
= Φu λui ηuk
− Φs λsi ηsk
.
∂ K̂i
4
Data
The data on manufacturing sectors used in this paper come from the NBER Manufacturing
Productivity Data Base. The fact that this database has been extensively used in the wage
inequality literature exempts me from providing a detailed description of it.21
The data on non-manufacturing sectors was constructed to be used in this paper, and
a detailed description of it is provided in the online appendix22 . The data contains annual
information for the period 1964–1996 on three sectors: Retail Trade, Wholesale Trade, and
Services.23 These three sectors, together with manufacturing, represent 65% of the US
non-farm labor force, 80% of the private sector labor force, and 55% of GDP.
Table 2 in the online appendix24 describes the main characteristics of the data set.
Throughout the analysis, production workers and non-production workers are used as a
proxy for skilled and unskilled labor in manufacturing sectors.
Berman et al (1994),
Sachs and Schatz (1994), and Leamer(1998), among others, discuss the appropriateness
of these proxies when distinguishing between skilled and unskilled workers. For the nonmanufacturing sectors, workers in non-supervisory or non-managerial occupations are defined as unskilled and workers in managerial or supervisory positions are defined as skilled.
The usefulness of those proxies in non-manufacturing sectors has not been assessed yet.
Based on the definition of occupations that are considered as non-supervisory or nonmanagerial provided in the online appendix25 , it is expected that, for the Wholesale Trade
and Retail Trade sectors, this classification should do no worse than for the manufacturing
21
For detailed information, see Bartelsman and Gray (1994).
Available at www.rotman.utoronto.ca/bblum/personal/front.htm
23
A second definition of the Service sector excluding Health Services and Legal Services (labeled Services
excluding H&L) is also available starting at 1972.
24
Available at www.rotman.utoronto.ca/bblum/personal/front.htm.
25
Available at www.rotman.utoronto.ca/bblum/personal/front.htm.
22
13
sectors. For the Services sector, things might be different. Since physicians, lawyers, and
teachers may be considered as non-supervisory workers, this classification may be quite
misleading when applied in Services industries. To deal with that, a sub-sector of Services
excluding Health and Legal Services is constructed.26
5
Estimation of the Demand Elasticities
In order to perform the decomposition proposed by the system of equations (1), all the
i , η i , η i , η i , η i , and
information required except for the sectoral demand elasticities ηuu
ss
us
su
uk
i are available in the industry level data set discussed above. In this section, I discuss
ηsk
issues related to the definition and measurement of these elasticity parameters.
When defining the elasticities, it is important to notice that they are “short-run” elasticities; that is, they measure unskilled and skilled labor demand changes when the capital
stock is fixed. Moreover, these elasticities are not the typical constant output factor demand
ones that are usually obtained from cost functions. In the model presented in the previous
section, whenever a sector is hit by a shock, it responds by changing its demand for labor,
i , η i , η i , η i , η i , and η i are
its capital-labor ratio, and its output. For those reasons, ηuu
ss
us
su
uk
sk
obtained from industry variable profit functions. To be more precise, for a given production
function yi = f (.), at every point in time I define the short-run variable profit function of
industry i as:
π(pi , wu , ws , Ki , Ti ) = max{Ui ,Si }
pi yi − (wu Ui + ws Si )
where Ti represents the technological state in sector i. The derived demand function for
∂πi
unskilled labor, for example, is then given by Ui = − u , and the own-price elasticity of
∂w
∂Ui
∂ 2 πi
demand is given by ηuu = − u =
. Both the cross-price elasticity of demand and
∂w
∂(wu )2
the elasticity with respect to capital accumulation can be similarly calculated.
When taken to the data, the variable profit functions will be approximated by translog
functions.27 The online appendix discusses in detail the translog variable profit function.
26
Lack of data prevents the exclusion of Educational Services as well. Nevertheless, this sub-sector is
relatively small, employing only about 2 million workers of the 40 million or so in the Services sector and
the 10 million or so in the Health Services sector.
27
See Diewert (1974) for a theoretical discussion and McKay et al. (1983) for an empirical implementation.
14
The demand elasticities are derived from the share equations, which are obtained as an
application of Sheppard’s lemma:
Hitu
Hits
wtu
wts
Uit wtu
= −
= αu + γuu log
+ γsu log
+ γku log (Kit ) + γtu log (Tit ) + εuit
Πit
pit
pit
u
s
wt
wt
Sit ws
= −
= αs + γus log
+ γss log
+ γks log (Kit ) + γts log (Tit ) + εsit
Πit
pit
pit
where Πit is the variable profit in industry i at time t defined as the difference between
variable cost and value added and, as discussed in the online appendix, γus = γsu .
The demand elasticities in industry i are then given by:
γuu
−1
Htu
γss
= Htu + s − 1
St
γku
ηuk,t = 1 + u
Ht
ηuu,t = Htu +
ηss,t
γsu
Htu
γus
ηsu,t = Htu + s
Ht
γks
ηsk,t = 1 + s
Ht
ηus,t = Hts +
The demand elasticities can be estimated for each 4-digit industry in manufacturing and
for each sector within non-manufacturing using only time-series variation. In this case,
however, the estimates would be based on at most 27 observations, given the number of
years available in the sample. Since the trade-off between using cross-sectional and time
series variation is unclear, I pool together industries in the same 2-digit SIC classification
or in non-manufacturing sectors and make the assumption that within these the elasticities
are the same. I also assume that technological changes are common to industries in the
same 2-digit SIC classification, or in non-manufacturing sectors, but are otherwise unconstrained.28 Their effects on the demand for factors can then be captured by time and 2-digit
industry-specific fixed effects. Since the available measures of product prices are indexes,
cross-industry comparisons can only be made using time-differences and not levels.29 In
28
I also estimated the demand elasticities assuming a general form of exponential rate of output- and
input-augmenting technical change that are common within 2-digit SIC or non-manufacturing sectors. The
qualitative results of the estimation and of the decomposition are the same as the ones reported, and are
available upon request.
29
Estimating the system of share equations in differences has multiple advantages. First, it takes care
of industry-specific fixed effects. Second, it minimizes potential problems derived from differences in the
classification of production and non-supervisory workers and non-production and supervisory workers. As
long as such differences are constant over time, they are eliminated when differences are taken. It should be
noted, however, that if the data is subject to measurement errors, using time-differences is likely to aggravate
the problem, and the estimates may become inconsistent. To deal with that problem, I use longer differences
(the results reported are for 3-year differences but they hold as well when shorter differences are used).
15
particular, let i index for industries within a given 2-digit manufacturing sector or nonmanufacturing sector. Then the following share-equations in differences apply for each of
these broad-industry categories:
∆
t
Hitu
∆t Hits
wtu
wts
=
+ γuu ∆ log
+ γsu ∆t log
+ γku ∆t log (Kit ) + uit
pit
pit
u
s
wt
wt
t
t
t
= δs + γus ∆ log
+ γss ∆ log
+ γks ∆t log (Kit ) + sit
pit
pit
δut
t
(2)
where the time and broad industry-specific fixed effects control for technological changes,
biased or not.
The equations above are estimated using the average annual returns of production and
non-production workers as proxies for unskilled and skilled wages in manufacturing, and the
average annual returns of supervisory and non-supervisory workers as proxies for unskilled
and skilled wages in non-manufacturing sectors. Nominal average wages are deflated by the
sectoral price deflator in order to represent wu and ws .
Before presenting the estimated parameters, I will briefly present and discuss evidence
that the statistical conditions under which the parameters in the system of equations 2 can
be consistently estimated are satisfied. These conditions are, in addition to the structure
imposed by the translog approximation, that the right-hand side variables are contemporaneously uncorrelated with the residuals.30 Notice that the right-hand side variables can be
correlated with lagged values of the residuals, as long as these are not serially correlated.
I test for the contemporaneous correlation between the right-hand side variables and the
residuals in the system of equations 2 using the Hausman Endogeneity test. I used lagged
dependent variables in levels31 and changes in the global prices on manufacturing goods.32
as instruments.
The null hypothesis of no contemporaneous correlation between any of the right-hand
side variables and the residuals is accepted for 90% of the sectors at the 5% significance
level, and for over 96% of the sectors at the 1% level.33
30
It is also required that the matrix with right-hand side variables is of full-rank. See Wooldridge (2002),
pages 170–171.
31
As discussed in detail in Arellano and Bond (1991), lagged values of the dependent variable are good
candidates to instruments as long as there is no serial correlation in the residuals at the lags used. Tests for
serial correlation of the residuals for lags higher than the one used to difference the data indicate that, for
over 95% of the sectors one cannot reject the null of no serial correlation at the 5% significance level.
32
These are proxied by f.o.b. prices of US imports of manufactured goods at the four-digit sic level.
33
As a robustness check, I also estimate the system of equation 2 using only the sectors for which the
16
Table 1 shows the unskilled and skilled labor demand elasticities with respect to capital accumulation in every sector. As discussed in section 3, these are key measures for
understanding the effects of changes in the sectoral composition of the economy on the
wage premium. Note that elasticity parameters indicate that capital accumulation in nonmanufacturing sectors such as services, retail trade, and wholesale trade leads to higher
relative demand for skilled workers, while capital accumulation in manufacturing leads to
higher relative demand for unskilled workers. Therefore, in periods where the economy
moves towards manufacturing, we should expect a decreasing wage premium, and in periods where the economy moves away from manufacturing and towards services, we should
expect an increasing wage premium34 .
6
Results
In this section, I combine the data set described in section 4, the demand elasticities estimated in section 5, and the system of equations (1) to decompose the changes in skill
premium observed in the US economy into parts due to international trade, labor supply
changes, capital accumulation and reallocation, and technological changes. Following the
assumptions of the model, I assume that capital is immobile among sectors of the economy
and fixed at the observed levels within one-year periods. I then use the system of equations
(1) to calculate the effects on the skill premium caused by the observed changes in prices of
tradable goods, TFP changes, and changes in labor supply. Even though capital is assumed
sector specific, it still accumulates over time. Therefore I allow the capital stock of different
sectors to change as observed in the data between the year in question and the following
year, and I use the equations of the model to infer the effect of such changes on the skill
premium. Finally, I attribute the changes in skill premium unaccounted for by the observed
factors of the model to the unobserved component, the factor-biased changes in the demand
null hypothesis of no contemporaneous correlation between any of the right-hand side variables and the
residuals is accepted. All the results of the paper continue to hold in this case. IV estimates of the system
of equations 2 are presented in section 4 of the online appendix. The IV estimates are very similar to the
GLS ones presented before. Moreover, no clear pattern in the differences between the IV and GLS estimates
is to be found.
34
Estimates of own- and cross-price demand elasticities for unskilled and skilled workers are presented
in the online appendix. The estimated values are well in accordance with previous findings surveyed by
Hamermesh (1993).
17
Table 1:
Labor demand elasticity with respect to capital accumulation - 1980 values.
Industry
Manufacturing
Non-Manufacturing
Manufacturing Sectors
Primary Metal
Machinery
Transp. Equipment
Printing
Misc. Manuf.
Petroleum
Textile
Furniture
Apparel
Instruments
Rubber
Paper
Elect. Equipment
Lumber
Stone, Clay, Glass
Food
Chemicals
Tobacco
Leather
Fabricated Metal
Unskilled Workers
(ηuk )
1.26(0.10)
0.96(0.01)
Skilled Workers
(ηsk )
0.93(0.09)
1.45(0.01)
1.40(0.65)
0.98(0.21)
1.05(0.88)
0.55(0.25)
1.04(0.18)
0.57(0.65)
1.41(0.57)
1.54(0.26)
1.20(0.16)
1.15(0.15)
1.09(0.14)
2.38(0.61)
0.96(0.15)
1.27(0.34)
1.14(0.29)
1.33(0.19)
1.24(0.56)
1.00(0.97)
4.60(0.39)
0.15(0.12)
1.70(1.05)
1.29(0.26)
1.33(0.99)
0.81(0.15)
1.21(0.19)
0.69(0.61)
1.46(0.52)
1.58(0.24)
1.16(0.20)
1.11(0.17)
1.04(0.18)
2.33(0.72)
0.89(0.14)
1.16(0.32)
1.01(0.27)
1.16(0.16)
1.04(0.36)
0.66(1.42)
3.13(0.41)
-2.46(0.24)
Standard errors in parenthesis.
The manufacturing sectors are ranked according to the difference between ηsk and ηuk .
18
Figure 4: The effects of changes in sectoral composition on wage premium (percentage
change).
for skills component. In this way, I have a decomposition of the observed changes in skill
premium between year t and year t + 1. The exercise is then repeated for every year of the
sample.
I next discuss the effects of each of the determinants of the skill premium in the US
economy between 1970 and 1996.
6.1
The effects of changes in the sectoral composition of the economy on
the wage premium
Changes in the sectoral composition of the economy are measured as differences between
sectoral and aggregate capital accumulation rates. The effects of changes in the sectoral
composition of the economy on the wage premium are shown in figure 4. This figure shows
that sectoral reallocation started to push strongly (reaching the order of 2% per year) for
increases in the wage premium in the late 1970s, coincidently the time when the wage gap
started to widen.
Figure 5 shows the observed wage premium (normalized to be 1 in 1970) and the result of
a counter-factual exercise where I calculate the wage premium if no changes in the sectoral
composition of the economy had occurred. Over the 1970–1996 period, absent any sectoral
19
Figure 5: The observed wage premium and the wage premium except for changes in the
sectoral composition of the economy (normalized to be 1 in 1970).
capital reallocation in the US economy, the wage premium would have increased by about
60% less than it did. As we will see in the next sections, no other determinant of the wage
premium affected it in such a strong way.
6.2
The effects of labor supply changes and capital accumulation on the
wage premium
Figure 6 shows the effects of changes in labor supply and capital accumulation on the wage
premium35 . The figure confirms that for most of the period the combined effects of labor
supply changes and capital accumulation were working in favor of a rising wage premium (see
the line labeled combined effect in figure 6). Labor and capital, however, pushed in opposite
directions. Changes in labor supply worked in favor of reducing the wage premium, what
should be expected given that the relative supply of skilled workers increased throughout
the period36 . Capital accumulation worked in favor of increasing the wage premium. That
35
See section 3 for the expressions of the marginal effects of labor supply changes and capital accumulation
on the wage premium.
36
It also confirms previous findings in the literature. Bound and Johnson (1992) find that, between 1973
and 1979, changes in the supply of college relative to high school graduates pushed for a 11.7% decrease in
the college/high school wage premium. Between 1979 and 1988 the changes pushed for a 10% decrease in the
same wage premium. For the same periods, I find that changes in the supply of non-production relative to
production workers pushed for a 13% and 21% decrease in the non-production/production wage premium.
20
happened because capital is relatively complementary to skilled workers in the economy
as a whole, a result that also confirms previous findings in the literature (see Krusell et
al. (2000))37 . The magnitude of the capital deepening effect that I found is also similar
to the one found in Krusell et al. (2000), although the timing of the change is somewhat
different. Figure 8 in Krusell et al.’s paper shows that, using their model and data, capital
deepening starts pushing the skill premium up in 1975 and that, from 1975 to 1990, when
their sample ends, it lead to an almost 20% increase in the skill premium. I find that capital
deepening pushes the skill premium up in the early 1970s, with another push starting in
the early 1980s. Indeed, between 1982 and 1996, I found capital deepening had pushed
the skill premium up by about 18%. I attribute the difference in timing to the fact that
Krusell et al. uses a capital series that is adjusted for the quality of capital. In this series
the increase in capital accumulation initiates in the mid-1970s while in the one available in
the National Accounts it starts in the early 1980s. Such an adjusted series is not available
for the disaggregated sectors of the economy that I am working with.
Although labor supply changes and capital accumulation have affected the US wage
premium over the last 30 years, figure 6 suggests that these factors do not account for the
rapid rise in wage inequality observed since the late 1970s. Indeed, the combined effects of
labor supply changes and capital accumulation around that period were quite small.
6.3
The effects of international trade on the wage premium
The increasing competition with low-wage sources of supply that US firms have faced since
the early 1970s is captured by changes in prices of tradable products. Given the sector
specificity of capital, lower product prices will induce the sector to substitute away from
both types of labor in favor of capital. The type of labor with the most elastic demand is the
one that has its demand proportionally decreased the most. Such a mechanism is captured
∂(ŵs − ŵu )
i
i
i
i
i is the
in the expression
= Φs (ηss
+ ηsu
)λsi − Φu (ηuu
+ ηus
)λui . As before, ηss
∂ p̂i
One important difference between Bound and Johnson’s paper and my own is that they did not use data on
capital stocks.
37
Berman et al. (1994) studied the effects of capital-skill complementarity on the wage premium and
found that it played no significant role. The results obtained in my paper differ for two main reasons.
First, it addresses the mechanism through capital-skill complementarity using non-manufacturing as well as
manufacturing data. As I show, reallocation towards non-manufacturing sectors plays a key role. Second,
it uses a general equilibrium framework where the interaction among sectors may produce very different
results than those obtained in partial equilibrium analysis.
21
Figure 6: The effects of labor supply changes and capital accumulation on wage premium
(percentage change).
own-price elasticity of demand for skilled workers in sector i, λsi is the share of unskilled
workers employed in sector i, and Φs is the elasticity of the wage premium with respect to
i +η i )λ and (η i +η i )λ capture
changes in the supply of skilled workers. The terms (ηss
su si
uu
us ui
the change in demand for skilled and unskilled workers derived from a price change, and
the terms Φs and Φu capture the impact of such a change on the economy’s wage premium.
The effects of trade through changes in product prices on the wage premium are shown in
figure 7. Confirming other studies (see Leamer (2001)), trade pushed for inequality in parts
of the 1970s, but not since then.38 At least through the price mechanism, the evidence
shows that international trade cannot account for the significant rise in wage premium
experienced in the US since the late 1970s. The effects of outsourcing on the wage premium
are captured together with the effects of skill-biased technological change and are discussed
in the next section.
Table 2 shows the marginal impact of a price change on the economy’s wage premium
38
I find that during the 1970s changes in product prices led to a 4% increase in the skill premium. Leamer
(2001) finds that, depending on whether one assumes a 100% or a 0% pass-through rate from TFP to prices,
product price changes led to either a 6% increase or a 7% decrease in the skill premium. Note, however, that
the mechanisms through which trade affects wages studied in Leamer’s paper differ from the mechanisms
studied in this paper. To the best of my knowledge, there is not another paper in the literature that
investigates the effect of product price changes on the skill premium using the Ricardo-Viner framework.
22
Figure 7: The effects of trade on wage premium (percentage change).
for each 2-digit manufacturing industry. For most of the manufacturing sectors, the wage
premium effects of trade are very small in magnitude and not statistically different than
zero. Only for the four or five sectors at the bottom of the table (higher elasticity values)
can the trade effect possibly be of any economic relevance.
Figure 8 shows product price changes (deflated by the GDP deflator) for the four sectors with the largest wage premium elasticities with respect to prices. These sectors are
Electronic Equipment, Printing, Machinery, and Transportation Equipment. If trade had
in fact caused the observed rise in inequality, product prices in these sectors should have
increased after the late 1970s. But that is not the case. These sectors had price increases
in the mid-1970s but not after that.
6.4
The effects of technological change and outsourcing on the wage premium
Hicks-neutral productivity gains in sectors where the demand for unskilled labor is relatively
more elastic than the demand for skilled labor, as well as in sectors that employ large shares
of unskilled workers, operate in favor of increasing the wage premium. Factor-biased changes
that increase the relative demand for skills, either as a consequence of technological changes
or outsourcing, will also work in favor of increasing the wage premium.
23
Table 2:
Wage premium elasticity with respect to product price changes (standard errors in
parenthesis).
Industry
Textile
Apparel
Tobacco
Lumber
Leather
Furniture
Misc. Manuf.
Paper
Rubber
Stone, Clay, Glass
Primary Metal
Fabricated Metal
Chemicals
Food
Instruments
Elect. Equipment
Printing
Machinery
Transp. Equipment
Elasticity
-0.02(.008)
-0.01(.004)
0.00(.002)
0.00(.004)
0.00(.002)
0.01(.003)
0.01(.002)
0.01(.006)
0.02(.004)
0.02(.004)
0.02(.012)
0.04(.011)
0.04(.011)
0.06(.004)
0.08(.01)
0.09(.008)
0.12(.008)
0.21(.016)
0.21(.062)
Figure 8: GDP-deflated product prices for selected sectors (percentage change).
24
Although measures of sectoral total factor productivity changes are readily available,
the same cannot be said of factor-biased changes in the relative demand for skills.39 In this
paper, I follow the bulk of the literature on wage and income inequality by recognizing the
great difficulty involved in isolating the effects of outsourcing and skill-biased technological
changes on the relative demand for skills. I treat them, therefore, as the unobservable
component that makes the system of equations (1) hold as an identity.
The effects of Hicks-neutral technological changes and of factor-biased changes in the
demand for skills on the wage premium are shown in figure 9. Hicks-neutral TFP changes
did not have a significant impact on the wage premium, contributing mostly to equality,40
while the factor-biased changes have contributed to inequality since the late 1970s41 .
Figure 10 shows the observed wage premium (normalized to be 1 in 1970) and the
result of a counterfactual exercise where I calculate the wage premium that would have
happened had Hicks-neutral technological changes and skill-biased changes in the demand
for skills not happened. In absence of these changes the wage premium would have been
between 10% and 20% higher than that of the 1970s. In the 1980s and 1990s, these TFP
and skill biased demand changes account for about 50% of the observed widening of the
wage premium.42 However, figure 10 makes it clear that for the overall period 1970–1996,
factor-biased changes in the demand for skills and Hicks-neutral technological changes had
no net effect on the US wage premium.
39
Autor et al. (1998) builds a measure of computerization levels by industry that might capture a fraction
of the skill-biased technological changes. Feenstra and Hanson (1999) builds a measure of outsourcing by
industry that might capture some of the effects of outsourcing on the relative demand for skills. Both
these measures have been used in regression analysis in order to capture cross-industry correlations between
wage inequality, outsourcing, and technological changes. They are not appropriate, however, for use in a
decomposition like the one performed in this paper. For one thing, they measure only a small share of
the technology and outsourcing effects. In addition to that, they do not capture the relationship between
outsourcing and technological changes. It can be argued that outsourcing has become possible because of
technological changes, and that cannot be captured by these measures.
40
Likewise, Leamer (2001) also studies the effects of sectoral TFP changes on the nonproduction/production wage premium. He also finds no significant effect (table 7 in Leamer (2001)), the
exception being the 1980s (if it is assumed that there is no pass-through from TFP changes to prices).
41
This result confirms previous findings in the literature. See Acemoglu (2003), Feenstra and Hanson
(1999), and Bound and Johnson (1992) for example.
42
Feenstra and Hanson (1999) also find that skill-biased changes in the demand for skills, including changes
due to computerization and outsourcing, explain roughly 50% of the observed changes in non-production
wage shares between 1979 and 1990 (see table 4.4 in Feenstra (2004)).
25
Figure 9: The Hicks-neutral technological changes and the factor-biased effects on the wage
premium (percentage change).
Figure 10: The observed wage premium and the wage premium except for the effects of Hicks-neutral technological changes or skill-biased changes on the demand for
skills(normalized to be 1 in 1970).
26
7
Final Considerations and Conclusions
This paper uses a multi-sector version of the RV model of international trade to quantify the
effects of changes in the sectoral composition of the economy, Hicks-neutral technological
changes, skill-biased changes in the demand for workers, international trade, labor supply
changes, and capital accumulation on wage changes.
The main finding of the paper is that changes in the sectoral composition of the economy
were the most important force behind the widening of the wage premium. In fact, capital
reallocation from manufacturing sectors (where it is relatively complementary to unskilled
workers) to services, retail, and wholesale trade sectors (where it is relatively complementary
to skilled workers) has strongly contributed to inequality since 1979. Indeed, figure 5 shows
that sectoral changes account for about 60% of the widening of the wage premium observed
between 1970 and 1996.
A logical question to ask is what drove such a capital reallocation towards non-tradable
sectors? In the macro/development literature, there are two classes of models that generate
the structural transformation that economies go through with economic development. In
the first class the reallocation is driven by non-homotheticities in preferences.43 In the
second class, it is uneven technological progress across sectors that ultimately drives the
sectoral reallocation.44 If the observed capital reallocation is driven by non-homotheticities
in preferences, one may be concerned that the model developed in this paper ignores this
feature of the world, and that the decomposition performed might be mis-specified. In
the online appendix, I show how non-homotheticities can be added to the model and to
the data analysis. I also show that the results of the paper are robust to allowing for
non-homotheticities in the demand for non-tradable goods.
If, however, the observed capital reallocation was driven by uneven technological progress
across sectors, the results of the paper might be incorrectly measuring the effects of technological changes on the skill premium. In this case, the decomposition performed in the paper
misses the part of the TFP effect that works through capital reallocation.45 I investigate this
43
See Kongsamut, Rebelo, and Xie (2001) for a recent example.
See Baumol (1967) and Ngai and Pissarides (2006).
45
Because of adjustment costs, TFP changes may affect capital accumulation in subsequent periods as
well. In this paper, I do not deal with these dynamic effects. In the decompositions performed here, these
effects would be captured by the measured effects of capital accumulation.
44
27
possibility by looking at TFP growth differential in manufacturing and non-manufacturing
sectors over the 1970-1996 period. I find no evidence that the pattern of capital reallocation is related to changes in TFP growth differential in favor of non-manufacturing sectors
during this period. Although uneven technological progress has been shown to be important in explaining the process of sectoral reallocation away from agriculture throughout the
twentieth century,46 it does not seem to have played a critical role in the reallocation away
from manufacturing and towards non-tradable sectors in the 1970-1996 period.
Finally, even though the likely candidates for drivers of the observed process of sectoral
reallocation are the ones discussed in the previous paragraphs, anything that unevenly affects the rate of return of production factors in different sectors can affect the reallocation
of resources. For the purpose of the exercise performed in this paper, trade policy and other
trade-related sources of changes in the prices of tradable goods are particularly relevant. If
trade affected the observed sectoral reallocation, the results of the paper would be missing
this component of the trade effect.47 Even though I cannot completely rule out this possibility, a number of papers investigate the link between trade and the process of structural
change and find no evidence of such a link.48
As a final note, while this paper focuses on the effects of capital reallocation between
tradable and non-tradable sectors, Harrigan and Balaban (1999), Harrigan (2000), and
Kumar (2000) look at how price movements of non-tradable goods affected the skill premium
in the US economy. It may well be that these two mechanisms are connected via incentives
for investment. That opens up an important area for further research.
46
See Duarte and Restuccia (2006), among others.
For the reasons discussed in footnote 44, price changes also can affect capital accumulation in subsequent
periods. In the decompositions performed here, these dynamic effects would be captured by the measured
effects of capital accumulation.
48
See, for example, Rowthorn and Ramaswamy (1997) and Kongsamut (1998).
47
28
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