Discussion Papers In Economics
And Business

The Effects of International Linkages on Labor Demand by
Skill GroupHARUKA YANE

Discussion Paper 18-06

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Effects of International Linkages on Labor Demand by
Skill GroupHARUKA YANE

Discussion Paper 18-06

March 2018

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Effects of International Linkages on Labor Demandby Skill Group*
HARUKA YANE †Osaka School of International Public Policy (OSIPP)

Abstract
This study empirically investigates how backward linkages impact the skill
structure of domestic labor demand by estimating a system of factor equations
and measuring elasticities of factor demand. This was made possible byconstructing a dataset using data on labor compensation from the SocioEconomic Accounts (SEA) along with industry-level international trade data forthe period 1995 to 2009, covering 40 countries from the World Input-Output
Database (WIOD). Based on an input-output model, an indicator that measures
the pervasiveness of global value chains (GVCs), “foreign value-added inexports (FVAiX),” is calculated. Including this index in a translog cost function,
this study estimates a system of variable factor demand equations, using theiterative seemingly unrelated regressions (ISUR) method. Results reveal thatparticipating in GVCs has both positive and negative effects on demand for
different skill types of labor. On the one hand, there is a positive trend for highskilled workers, regardless of the region or GDP per capita of the country. On
the other hand, results indicate a widening gap between high- and low-skilledlabor, suggesting an increase in inequality, especially in low-income countries.
Consequently, it is becoming increasingly important to have policies that
support flexible and frictionless labor markets in order to emolliate the change indemand, while sustaining the positives firms can benefit from access to variety
of production options. Thus, these policies should facilitate the groups sufferingfrom losses to move along sectors, countries and transfer their skills to a newtask.
Keywords: International trade, Labor demand, Labor skills, International
linkage, Global value chains
JEL codes: F14, F15, F16* I would like to express my gratitude to Hiro Lee, Tsunehiro Otsuki and Shigeharu Nomura
for their helpful comments and suggestions to improve my analysis. I am also grateful to theparticipants at the 18th Annual Conference of the European Trade Study Group (ETSG 2016),
Helsinki, September 8-10, 2016, for providing me with ideas and technical insights.
†
Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama,
Toyonaka, Osaka 560-0043, Japan. Email: h-yane@osipp.osaka-u.ac.jp.

1. Introduction
In recent years we have witnessed the world economy rapidly integrating throughinternational trade. Behind this phenomenon is the international fragmentation of production
in global value chains (GVCs). A value chain can be simply defined as the full range ofactivities that firms and workers carry out in order to bring a product from its conception to its
end use and beyond (Gereffi and Fernandez-Stark, 2011). The term global comes from thefact that these activities are more and more spread over multiple countries. Innovations suchas the internet and container shipping have revolutionized trade and value chains.
This global integration of goods and services markets, however, has had diverse
effects on individuals, households and firms. Theoretically and empirically, it is now common
knowledge that trade liberalization is associated with both job destruction and job creation.
However, the debate continues regarding to what extent trade liberalization has impact on thelabor market, and what complementary policies may be effective to implement along withtrade liberalization in order to maximize (minimize) the benefits (costs).
Among other drivers such as education, migration, environment and the government,labor issues remain an important factor when deciding international trade policies. One of thereasons why it is becoming increasingly important to examine the causal relationship between
trade and labor is that the global integration of goods and services markets inevitably havedirect or indirect effects or both on the labor market. Decisions made by governments andfirms around the world that lead to tariff elimination or offshoring for instance will affect thelabor situation of the citizens, such as unemployment and inequality. Naturally, people worry
that if their country’s imports from their partner country increase due to a newly-signed free
trade agreement (FTA), the unemployment rate might rise and/or their wages may decrease.
Thus, with the world economy rapidly integrating through the fragmentation of production
processes, it is becoming increasingly important to measure the effects of trade liberalizationon the labor market.
This brings us to this paper’s research question: is there a link between firms’
increasing reliance on foreign production processes and the change in skill structure of thedomestic labor market? In other words, do GVCs have any connection with the labor market,
and if so, what kind? So as to investigate this, I examine the role of backward linkages in theskill structure of labor demand. This study uses data on labor compensation from the Socio-1-

Economic Accounts (SEA) along with industry-level international trade data for the period
1995 to 2009, covering 40 countries, from the World Input-Output Database (WIOD) 1.
WIOD’s SEAs used in this study cover low-, medium- and high-skilled labor
compensation as well as hours worked. Figure 1 depicts the change in the shares of hours
worked and compensation among skill types by sector. It clearly shows that during the period
1995-2009, hours worked by high-skilled workers have increased relative to hours worked bymedium- and low-skilled workers. Although hours worked by medium-skilled workers do notshow that much fluctuation, the decline in hours worked by low-skilled workers is striking.
This set of graphs signifies a possible replacement of low-skilled labor by either high-skilledlabor (skill-biased technical change) or the traditional foreign offshoring of less-skilled
activities.
The remainder of this paper is organized as follows. Section 2 provides theoretical andempirical literature and recent findings. Section 3 introduces the foreign value-added inexports (FVAiX) indicator, followed by description of the model and estimation techniques in
Section 4. Results are presented in Section 5. Section 6 concludes.

2. Literature and Recent Findings
This section sets out by examining the available theoretical arguments and empiricalevidence on the possible employment effects of international trade. I will first give a brief
overview of recent developments in the theoretical trade literature.
As mentioned, despite the fact that there is considerable agreement among economists
that fragmentation of production stages does affect wages and employment across countries
and industries, to date there has been little agreement on the expected direction of thesepotential effects. On the one hand, if globalization of value chains mainly involves tasks
carried out by low-skilled labor, as we have long seen, then theoretically, the relative demand

1 This World Input-Output Database is available at http://www.wiod.org/database/index.htm.
The core of the database is a set of harmonized national supply and use tables, linked together with bilateral
trade data in goods and services. These two sets of data are then integrated into a world input-output table.
See Timmer et al. (2015) for the detailed framework and calculations.

-2-

for high skills would cause relative wages to shift towards more-skilled workers, increasing
and contributing to a widening wage gap between skilled and unskilled labor (Feenstra andHanson, 1996a). On the other hand, however, if the associated cost reductions are particularly
strong in low-skilled labor intensive industries, international fragmentation may reduce thewage gap between skilled and unskilled labor as resources are reallocated towards low-skilledlabor intensive industries (Jones and Kierzkowski, 1990).
There has been some development in international trade theory towards linking
liberalization and production fragmentation with unemployment. The new movement wasmade possible when trade theory evolved into incorporating models with increasing returns toscale and imperfect competition (Krugman, 1980); and more recently, inter-firm differencesin productivity (Melitz, 2003) and propensity to export within industries (Roberts and Tybout,
1997; Bernard and Jensen, 2004). Among the theorists exploring the connection between
trade and employment is Matusz (1996), who embed theories of efficiency wages and job
search into trade models. In addition, Kasahara and Lapham (2013) extends Melitz (2003)’smodel to incorporate imported intermediate goods.
Following these works, Helpman et al. (2012) develop a model with heterogeneity in
fixed exporting costs and costs of screening worker abilities across firms. Estimating thisextended model, they find that overall wage inequality arises within sectors and occupations
across workers with similar observable characteristics. This within component is driven by
wage dispersion between firms, and wage dispersion between firms is related to firm
employment size and trade participation.
Costinot and Vogel (2010) implement a model that focuses on heterogeneous workers.
By taking different skill intensities into account, they examine the relationship between tradeliberalization and wage premium. They analyze the impact of North-South trade, which
results in an increased wage gap in the North, and a decrease in relative wages in the South.
They present this as an explanation for the shrinking of the middle class due to workers
leaving the medium-skilled industries in the North.
With the ongoing development in international trade theories, empirical literature has
also been increasing. However, empirical research to date has yet to offer any conclusive
evidence on the effects of trade liberalization on employment and wages. Hoekman andWinters (2005) suggest that this is in part due to the potential endogeneity problem when
dealing with trade policy. Trade policies can have a significant impact on the level and-3-structure of employment, wages, income inequality, labor market institutions and policies, andlabor and social policies can also influence the outcomes of trade policies in terms of growthof output, employment and the distribution of income. This makes it difficult to isolate theeffects of trade from other policies implemented simultaneously with trade reform.
Many of the recent empirical works tend to focus on offshoring and examine its
effects on the skill structure of labor demand. They analyze relative demand for labor by
estimating a translog cost function, which gives the variable costs in the respective industries,
and through conditional and unconditional labor demand functions, which are derived throughcost minimization and profit maximization for a given output respectively.
One of the studies that investigates how production offshoring affects skill structure
and wage inequalities is Hijzen, Görg and Hine (2005). They use a translog cost function to
study the impact of offshoring on the different types of workers in the United Kingdom. They
divide the education groups into three: high-, semi- and low-skilled workers by occupation.
Using data on United Kingdom’s manufacturing industries over the period 1982-1996, they
estimate a system of equations, one for each type of variable cost (the three different types oflabor and materials). Results indicate that increased intra-industry offshoring has a negative
impact on the demand for all three occupation groups. The magnitude of the effects, however,is different; the lower the level of skills, the stronger the impact of offshoring becomes. Their
paper concludes that offshoring as well as technological change through research anddevelopment are important factors in explaining the changing skill structure which the United
Kingdom has experienced.
Similarly, Feenstra and Hanson (1999), Falk and Koebel (2002) and Strauss-Kahn(2003) examine the impact of offshoring on the demand for skilled labor in the United States,
Germany and France, respectively. Their results also tend to indicate that offshoring has had anegative impact on the demand for unskilled labor. An exception is Falk and Koebel (2002)’s
study, whose results indicate that the increase in imported materials is driven by higher output
growth rather than input substitution for different types of labor.
More recently, Foster-McGregor, Stehrer and de Vries (2013) estimate the relationshipbetween offshoring and the skill structure of labor demand using the WIOD for 40 countries
over the period 1995-2009. They estimate a system of variable factor demand equations, andtheir results indicate that while offshoring has had a negative effect on all skill (low, medium
and high) levels, the largest impact has been observed for medium-skilled workers. This is-4-notable in a sense that it is consistent with the aforementioned recent trend towards theshrinking of the middle class (see Costinot and Vogel, 2010; Blanchard and Willmann, 2013).
As mentioned, much of the recent literature tend to consist of empirical studies that
focus on offshoring. These studies use measures such as share of imported intermediatepurchases in value-added to represent offshoring. When examining the effects of GVCs,
however, it is necessary to use a measure that can account for the increased interconnectivityof production processes.
The value of gross exports that are reported in official trade statistics exceeds thevalue-added actually created in a country in the production of its exports. Therefore, there
have been increasing amount of studies that attempt to capture the value-added created inforeign countries that is previously imported in the form of intermediates, and after someprocessing, is embodied in the country’s exports. Recent developments in multi-sector inputoutput model of the global economy include Foster-McGregor and Stehrer (2013)’s approachfor calculating factor content of trade that take both imports and exports into accountsimultaneously, which is based on Trefler and Zhu (2010)’s work. Johnson and Noguera(2012) introduce a measurement of value-added content of bilateral trade, called the ratio ofvalue-added to gross exports. Hummels, Ishii and Yi (2001) empirically define verticalspecialization, which measures the amount of imported inputs used to produce exports.
Modifying this measurement, Amador, Cappariello and Stehrer (2015) introduce the foreignvalue-added in exports (FVAiX) indicator. Their concept of foreign value-added embodied ina country’s exports improves on other indicators introduced above, by taking into account thecountry of origin of the value-added contained in the imported intermediate inputs used to
produce goods and services that are subsequently exported. In this study, I will be using theirindicator in order to focus on international linkages.

3. The FVAiX Index
Using trade data from the WIOD 2 , this study attempts to reveal the relationshipbetween participation in the GVC and change in domestic labor demand. In order to do so,

2 In this current study, out of the 35 industries available (Table A.1), I dropped Private Households with
Employed Persons due to its excessive amount of missing values.

-5-this study decomposes a country’s gross exports into foreign value-added that is used to
produce exports, which represents backward linkage in a GVC. For the calculation of thisFVAiX indicator, I follow Amador et al. (2015)’s input-output model.
FVAiX, again, captures backward linkages, which is foreign value-added that is used
to produce exports, in contrast to forward linkages, which represents domestic value-addedthat is exported, and in turn, subsequently used by the partner country to produce its own
exports. Naturally, this study chooses to focus on backward linkages, since the main purpose
here is to estimate the impact on the domestic labor market.
FVAiX is calculated using matrices of value-added, intermediate inputs, gross output
and gross exports statistics. The ratio of value-added to gross output in country s is denoted by
𝑣𝑣 𝑠𝑠 , a 1 × 𝑁𝑁𝑁𝑁 vector, where 𝑁𝑁 is the number of sectors and 𝐶𝐶 the number of countries. Thisaccounts for the value-added shares of the partner country s. The Leontief inverse of the
global input-output matrix is expressed as 𝐿𝐿 = (𝐼𝐼 − 𝐴𝐴)−1 , with dimension 𝑁𝑁𝑁𝑁 × 𝑁𝑁𝑁𝑁, where 𝐴𝐴

denotes the global intermediate input coefficients matrix. It represents total requirements ingross output from country s for a one unit increase in country r’s demand. Country 𝑟𝑟’s grossexports is denoted by a vector 𝑒𝑒 𝑟𝑟 with dimension 𝑁𝑁𝑁𝑁 × 1 . Pre-multiplying the Leontief

inverse by the vector of value-added coefficients and post-multiplying with exports vector
gives us foreign value-added content in gross exports:(1)

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑠𝑠𝑠𝑠 = 𝑣𝑣 𝑠𝑠 𝐿𝐿𝑠𝑠𝑠𝑠 𝑒𝑒 𝑟𝑟

Summing up over all partner countries:(2)

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑟𝑟 = Σ𝑠𝑠,𝑠𝑠≠𝑟𝑟 𝑣𝑣 𝑠𝑠 𝐿𝐿𝑠𝑠𝑠𝑠 𝑒𝑒 𝑟𝑟

Hence, this indicator captures the value-added content in country 𝑟𝑟 ’s exports in which

imported intermediate inputs are used. Taking the share of this value over gross exports willthen give us the share of foreign value-added in gross exports. Using this as a measurement-6-

for GVC prevalence, this study assesses the effect GVCs, or international linkages, have on
the skill structure of labor demand. The summary statistics for FVAiX is shown in Table 1.3

4. Model and Estimations Techniques
The next step is to create a cost function, and the transcendental logarithmic, ortranslog function, is the most frequently used flexible function in empirical work. This isbecause by fitting the system of equations, the translog cost function can fit itself to the costand input price data. By doing so, it enables us to directly estimate the parameters for the costshares. This setup of the model closely follows Foster-McGregor et al. (2013) and theirpredecessors. I assume that the cost functions can be approximated by a translog function andis twice differentiable, linearly homogenous, increasing and concave in factor prices. Anotherassumption here is that labor inputs and intermediate inputs are flexible whereas other inputs
are quasi-fixed. Time subscripts are omitted for simplicity.

1
2

1
2

𝐽𝐽

𝑙𝑙𝑙𝑙𝐶𝐶𝑖𝑖 (𝑤𝑤, 𝑥𝑥, 𝑧𝑧) = 𝛼𝛼0 + ∑𝑗𝑗=1 𝛼𝛼𝑗𝑗 𝑙𝑙𝑙𝑙𝑤𝑤𝑖𝑖𝑖𝑖 + ∑𝐿𝐿𝑙𝑙=1 𝛽𝛽𝑙𝑙 𝑙𝑙𝑙𝑙𝑥𝑥𝑖𝑖𝑖𝑖 + ∑𝑅𝑅𝑟𝑟=1 𝛾𝛾𝑟𝑟 𝑧𝑧𝑖𝑖𝑖𝑖 +1

1∑𝐽𝐽𝑗𝑗=1 ∑𝐽𝐽𝑘𝑘=1 𝛼𝛼𝑗𝑗𝑗𝑗 𝑙𝑙𝑙𝑙𝑤𝑤𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙𝑤𝑤𝑖𝑖𝑖𝑖 + ∑𝐿𝐿𝑙𝑙=1 ∑𝐿𝐿𝑚𝑚=1 𝛽𝛽𝑙𝑙𝑙𝑙 𝑙𝑙𝑙𝑙𝑥𝑥𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙𝑥𝑥𝑖𝑖𝑖𝑖 + ∑𝑅𝑅𝑟𝑟=1 ∑𝑅𝑅𝑠𝑠=1 𝛾𝛾𝑟𝑟𝑟𝑟 𝑧𝑧𝑖𝑖𝑖𝑖 𝑧𝑧𝑖𝑖𝑖𝑖 +
1

2

1

2

∑𝐽𝐽𝑗𝑗=1 ∑𝐿𝐿𝑙𝑙=1 𝛿𝛿𝑗𝑗𝑗𝑗 𝑙𝑙𝑙𝑙 𝑤𝑤𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙𝑥𝑥𝑖𝑖𝑖𝑖 + ∑𝐽𝐽𝑗𝑗=1 ∑𝑅𝑅𝑟𝑟=1 𝛿𝛿𝑗𝑗𝑗𝑗 𝑙𝑙𝑙𝑙𝑤𝑤𝑖𝑖𝑖𝑖 𝑧𝑧𝑖𝑖𝑖𝑖 + ∑𝐿𝐿𝑙𝑙=1 ∑𝑅𝑅𝑟𝑟=1 𝛿𝛿𝑙𝑙𝑙𝑙 𝑙𝑙𝑙𝑙𝑥𝑥𝑖𝑖𝑖𝑖 𝑧𝑧𝑖𝑖𝑖𝑖

(3)

2

2

Where 𝐶𝐶𝑖𝑖 depicts the total variable cost in sector 𝑖𝑖 = 1, … , 𝑁𝑁; 𝑤𝑤𝑖𝑖𝑖𝑖 denotes factor prices

for factor 𝑗𝑗 = 1, … , 𝑘𝑘, … , 𝐽𝐽; 𝑥𝑥𝑖𝑖𝑖𝑖 denotes quasi-fixed inputs and outputs for 𝑙𝑙 = 1, … , 𝑚𝑚, … 𝐿𝐿;

and 𝑧𝑧𝑖𝑖𝑖𝑖 is the technological change or the demand shifter for 𝑟𝑟 = 1, … , 𝑠𝑠, … 𝑅𝑅. The variablefactors are low-skilled, medium-skilled and high-skilled labor, 4 and intermediate inputs. The
five necessary constraints for the aforementioned assumptions to hold are:

3 The majority of countries with FVAiX of over 0.9 are low and lower-middle income EU countries.

Europe has a high value of FVAiX compared to other regions. This shows the high degree of integration
especially in the EU, and that the lower income countries export goods and services that have already beenmostly finished when they imported them.
4 Based on educational attainment. For details see Table 2.

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(4)

∑𝐽𝐽𝑗𝑗=1 𝛼𝛼𝑗𝑗 = 1

and∑𝐽𝐽𝑗𝑗=1 𝛼𝛼𝑗𝑗𝑗𝑗 = ∑𝐽𝐽𝑘𝑘=1 𝛼𝛼𝑗𝑗𝑗𝑗 = ∑𝐽𝐽𝑗𝑗=1 𝛿𝛿𝑗𝑗𝑗𝑗 = ∑𝐽𝐽𝑗𝑗=1 𝛿𝛿𝑗𝑗𝑗𝑗 = 0

These restrictions ensure that changes in one variable cannot lead to changes in the sum of thecost shares, and that an identical change in the input prices does not change the usage ofresources and yield for all cost shares. In addition, there needs to be symmetry restrictions.
The cross-price effects need to be symmetric among the different skill groups, which implies
that 𝛼𝛼𝑗𝑗𝑗𝑗 = 𝛼𝛼𝑘𝑘𝑘𝑘 .

First derivatives of the cost function with respect to the log of each factor price(𝑤𝑤𝑖𝑖𝑖𝑖 : 𝑃𝑃𝐿𝐿 , 𝑃𝑃𝑀𝑀 , 𝑃𝑃𝐻𝐻 , 𝑃𝑃𝐼𝐼 ) will yield the cost share 𝑆𝑆𝑖𝑖 of factor 𝑗𝑗 in total variable costs.
𝜕𝜕𝜕𝜕𝜕𝜕𝐶𝐶𝑖𝑖

(5)𝜕𝜕𝜕𝜕𝜕𝜕𝑤𝑤𝑖𝑖𝑖𝑖

𝑤𝑤𝑖𝑖𝑖𝑖𝐽𝐽= 𝐿𝐿𝑖𝑖𝑖𝑖 𝐶𝐶 = 𝑆𝑆𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑗𝑗 + ∑𝑘𝑘=1
𝛼𝛼𝑗𝑗𝑗𝑗 𝑙𝑙𝑙𝑙𝑤𝑤𝑖𝑖𝑖𝑖 + ∑𝐿𝐿𝑙𝑙=1 𝛿𝛿𝑗𝑗𝑗𝑗 𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 + ∑𝑅𝑅𝑟𝑟=1 𝛿𝛿𝑗𝑗𝑗𝑗 𝑧𝑧𝑖𝑖𝑖𝑖
𝑖𝑖

𝑤𝑤𝑖𝑖𝑖𝑖𝜕𝜕𝐶𝐶

where 𝐿𝐿𝑖𝑖𝑖𝑖 = 𝜕𝜕𝑤𝑤 𝑖𝑖 , which equals the demand for input 𝑗𝑗. Following this, 𝐿𝐿𝑖𝑖𝑖𝑖 𝐶𝐶 is the payments
𝑖𝑖𝑗𝑗

𝑖𝑖

to factor 𝑗𝑗 relative to total costs.

Rather than estimating a single cost share equation, this study estimates a system of

demand equations by adopting the Iterative Seemingly Unrelated Regressions (ISUR) method,and controlling for time-invariant sector-specific fixed effects. A set of equations that hascontemporaneous cross-equation error correlation is called an SUR system. At first look, theequations seem unrelated, but the equations are related through the correlation in the errors.
(see Baum and Linz, 2009) As discussed in Greene (2008, p.277), consistent estimates can bederived by estimating any three of the four factor share equations in the system, since they
sum up to unity. Dropping the fourth share equation for intermediate inputs, we can normalize
the factor prices by 𝑃𝑃𝐼𝐼 (factor price of intermediate inputs).

Finally, the elasticity of factor demand with respect to factor-biased technologicalchanges or demand shifters (𝑧𝑧𝑖𝑖𝑖𝑖 ) (all other factors kept constant) can be given by:𝛿𝛿𝑗𝑗𝑗𝑗𝜃𝜃𝑗𝑗𝑗𝑗 = 𝑆𝑆

𝑖𝑖𝑖𝑖

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(6)

Constructing a panel dataset for period 1995-2009, this study focuses on the relationshipbetween international linkage and variable cost shares. Table 1 shows the summary
statistics. 5
5. Estimation Results
First, the Breusch Pagan test of independence is used to test the assumption that theerrors across equations are contemporaneously correlated. The null hypothesis here is that theerrors are not correlated, and thus the equations can be estimated separately by ordinary leastsquares (OLS). The test statistics are reported for all regressions in the Notes section under
Tables 3-7. All of them reject the null hypothesis, indicating that the equations are notindependent, providing support for using the ISUR model. This implies that the residuals
from each ISUR regressions are significantly positively or negatively correlated with eachother. Correlation matrices of residuals are available upon request.
The results for the pooled sample are shown in Table 3. The variable of interest,
FVAiX, is statistically significant at the 1% level for all three skills’ cost shares. The
magnitude, however, is very minimal, and this can be said for all of the following estimation
results. However, the signs of the coefficients can still give us an idea regarding the directionof impacts GVCs have on the labor demand. The pooled results do reflect Figure 1, that is,cost shares for low-skilled labor decreases while the demand for medium- and high-skilledlabor groups is increasing. In order to seek the context and possible explanation behind theseresults, next, coefficients are estimated by goods sector and services sector.

5 The mean of the original raw values for compensation are intuitive and consistent with the literature that

there is a very strong correlation between skill levels and wages; that higher skill levels have higher wages
and lower skill levels have lower wages. However, the mean of the natural logarithm of high-skilled
compensation is not, with its mean value being the lowest of the three skill types. The swap is not thatsurprising, nonetheless, since in the process of taking the logarithm, the individual higher values of highskilled compensation fell below those of low-skilled compensation. Simple and clear evidence of this isshown by assuming the dataset consists of only two observations, the minimum and maximum. If you takethe average of the two values for the original compensation values, the mean wage for high-skilled workers(511.92) is greater than that for low-skilled workers (377.38). Then if you compare the averagecompensation between the two skill groups for the respective log values, high-skilled labor compensation(4.82) has become smaller than low-skilled labor compensation (5.36).

-9-

Table 4 shows the results for the estimation of the system of cost share equations bysector. The coefficients of FVAiX are all statistically significant at the 1% level, albeit thevalues are almost negligible. What is interesting here is that the only cost share that is affected
negatively is the medium-skilled in the goods sector. In contrast, an increase in FVAiX has apositive or at least non-negative impact on all three skill groups in the services sector, and on
low- and high-skilled labor cost shares in the goods sector.
Now the sample is further grouped into three levels of technology, by sector. Here I
strictly follow the categorization by Foster-McGregor et al. (2013), and the detailed list isprovided in Table A.1. There are six types in total: low-, medium-, high-tech for each of thetwo sectors. The estimation results are shown in Table 5. The goods sector’s results show thatin all three tech-types, medium-skilled labor cost shares decrease with an increase in FVAiX.
The magnitude of the effect is worse for the low-tech industries. Moreover, in theseindustries, all three labor cost shares are affected negatively, low-skilled labor suffering themost. We see the “shrinking” of the middle class, as been found in recent literature, in thestatistics prominent in medium-tech goods industries.
As we have been seeing from the other results, services sector does not have manystatistically significant or nonzero results. Nevertheless, they are all positive, which indicatesthat services sector is not affected that much by increasing FVAiX, and if anything, FVAiX
has a positive effect for some labor cost shares.
In Table 6, we compare the effects of FVAiX among different income groups 6. The
results indicate that the only country group to experience a negative effect on the mediumskilled labor cost share is the high-income countries. FVAiX has a positive and significant
effect on high-skilled labor cost shares across all four country groups, and the effectattenuates as income increases. It is also important to note that out of the four income groups,
lower-middle income countries have the highest FVAiX values, followed by low-income
countries. In the low-income country group, low-skilled labor cost share is affected negatively
by an increase in FVAiX.

6 Using the values for GDP per capita (in current USD) in 2007 from the World Development Index, the 1stquantile is 11,155.83; the 2nd is 27,478.21; the 3rd is 44,654.56 (and the 4th is 107,098.7). Countries of eachgroup are listed in Table A.2.

- 10 -

Lastly, results when the sample is divided into three big regions is shown in Table 7.
The regions are Asian, European and American. The only statistically significant effect on themedium-skilled labor cost shares is in Europe, and it is positive. In the European region, theFVAiX coefficients are also positive and statistically significant for low- and high-skilledlabor cost shares. This could reflect the success of the European Union despite the recent
controversies.
Although when we look at the big picture, participation in international linkage seems
to only affect cost shares for low-skilled labor negatively, the results indicate the following
points. First, medium-skilled workers in high income countries tend to suffer from a negative
effect from an increase in FVAiX, even though the magnitude of that effect is very small.
Medium-skilled labor cost shares shrink in the goods sector, especially in the low-techindustries. These results hint that the crowding out effect of the middle class may be themedium-skilled workers in the goods sector who live in high-income countries. Second, allthree cost shares decline in low-tech goods industries. Third, the overall negative effect on
low-skilled labor can be seen in low-income countries. Fourth, high-skilled labor cost shares
increase in all four country groups, and the effects attenuate as GDP per capita increases.
Ultimately, Table 8 reports estimates of elasticity of the cost share variables withrespect to the FVAiX indices. Elasticities are calculated according to Eq.(6). Now it ispossible to make inferences about how the labor demand would change in number of workers.
These elasticities of factor demand reveal that in the full sample, for example, a 1% increase
in FVAiX would increase medium-skilled labor by 0.08% and lower low-skilled labordemand by 0.1%. All the values here are very small, but the sign which shows the direction
the labor demand will head with an increase in FVAiX may be informative.
The demand for all three labor groups increases in the services sector, whereas we
witness a crowding out of the medium-skilled labor demand in the goods sector. In addition,
the demand for medium-skilled labor increases in the low- and lower-middle income countrygroups, which have the highest FVAiX in the sample. For upper-middle and high income
countries, the effect on medium-skilled labor demand is statistically insignificant. For all techtypes in the goods sector, medium-skilled labor demand decreases. Demand for all three skill
groups decline in the low-tech goods sector.
The elasticity of demand for low-skilled labor in the medium-tech goods sector has thehighest value of 1.1%. This effect seems to be represented in upper-middle income countries.
- 11 -

This is surprising at first because according to the classical Stolper-Samuelson theorem,
lower-skilled labor is expected to shrink in higher-skill abundant countries when opening up
to trade. Given that “it is difficult to generate the same direction of movements in relative
wages across countries from opening trade in the Heckscher-Ohlin model” (Feenstra, 2004,
p.117), Feenstra and Hanson (1996b) provide an explanation of this phenomenon, which “actsas a type of ‘endogenous technical change’ biased in favor of the skilled factor” (p.4).
Basically, production activities that are transferred from the North to the South tend to be
more higher-skill intensive than those formerly produced in the South, but less skill intensive
than those now produced in the North. Consequently, relative demand for high-skilled labor
can increase in both the North and the South.
As for regions, Europe enjoys an increase in demand for all three types of labor. In
Asia it is merely for low- and high-skilled labor, but the strength of elasticity for low-skilledlabor is greater. It is even bigger in the American region, although the sign is negative: a 1%
increase in FVAiX would decrease low-skilled labor demand in North and South America byapproximately 0.46%.

6. Concluding Remarks
In this study I calculate an indicator that measures foreign value-added share in grossexports, following Amador et al. (2015)’s input-output model. Applying this to a system ofvariable factor demand equations, by adopting the ISUR method, this study attempts to clarify
the link between international linkage and the skill structure of labor demand.
As a whole, an increase in FVAiX lowers the low-skilled labor demand, while
increasing demand for medium- and high-skilled labor. Demand for low-skilled labor is
negatively affcected in both the low-tech and high-tech goods sector. The negative impact on
low-skilled labor can be seen in low-income countries, where high-skilled labor demand
increases. This implies a widening gap between high- and low-skilled labor, suggesting an
increase in inequality, in low-income countries. In addition, labor demand for all three skills
decline in low-tech goods industries. These results seem to be inconsistent with the initial
hypothesis based on theoretical literature, which is that they would either be higher-skillbiased or lower-skill-biased, depending on their relative factor endowments. Feenstra and- 12 -

Hanson (1996b) show that due to a shift in production activities to the south, relative demandfor high-skilled workers can increase both in the North and the South.
Results show a hint of the crowding out effect of the middle class. Medium-skilledlabor demand shrinks in the goods sector, especially in the low-tech industries. The crowding
out itself can be observed in the medium-tech goods industries. Contrarily, high-skilled labordemand increases in all four country groups, which are classified based on income level, andthe effects attenuate as GDP per capita increases. Demand for high-skilled labor rises in all
three regions as well, ranging from 0.11% to 0.18%. Medium-tech goods and services sectors
also witness a rise in demand for high-skilled labor.
An increase in FVAiX has a greater effect in the goods sector, compared to theservices sector, and in both sectors, demand goes up for all skill types, except for mediumskilled labor in the goods sector. The fact that the impact on services sector is much smaller ortend to be statistically insignificant, along with the summary statistics of FVAiX (mean ofservices is 0.126, as opposed to mean of goods, which is 0.144) suggest that, more detailed
data on production of services as well as trade in services is needed to accurately capture theweight services sector has in GVCs.
In conclusion, in this new age of production fragmentation, participating in GVCs has
both positive and negative effects on demand for different skill types of labor. On one hand,
there is a positive trend for high-skilled workers, regardless of the region or GDP per capita of
the country. On the other hand, there seems to be an overall negative effect on low-skilledlabor demand, especially in the American region. Therefore, it is becoming increasingly
important to have policies that support flexible and frictionless labor markets in order toemolliate the change in demand, while sustaining the positives firms can benefit from accessto variety of production options. Thus, these policies should facilitate the groups sufferingfrom losses to move along sectors, countries and transfer their skills to a new task.

- 13 -

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- 16 -

Tables and Figures
Figure 1: Hours worked and compensation shares by sector (year 1995=100)
Agriculture
160
140
120
100
80
60
40
20
0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Hours worked by low-skilled labor

Hours worked by medium-skilled labor

Hours worked by high-skilled labor

Low-skilled labor compensation

Medium-skilled labor compensation

High-skilled labor compensation

Manufacturing
180
160
140
120
100
80
60
40
20
0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Hours worked by low-skilled labor

Hours worked by medium-skilled labor

Hours worked by high-skilled labor

Low-skilled labor compensation

Medium-skilled labor compensation

High-skilled labor compensation

Services
200
180
160
140
120
100
80
60
40
20
0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Hours worked by low-skilled labor

Hours worked by medium-skilled labor

Hours worked by high-skilled labor

Low-skilled labor compensation

Medium-skilled labor compensation

High-skilled labor compensation

Source: Author’s calculation based on WIOD.

- 17 -

Table 1: Descriptive statistics
Variable

Obs

Mean

Std. Dev.

FVAiX (z)TC
𝑤𝑤𝐿𝐿𝑆𝑆𝑤𝑤𝑀𝑀𝑆𝑆
𝑤𝑤𝐻𝐻𝑆𝑆lnTC
𝑙𝑙𝑙𝑙𝑤𝑤𝐿𝐿𝑆𝑆
𝑙𝑙𝑙𝑙𝑤𝑤𝑀𝑀𝑆𝑆
𝑙𝑙𝑙𝑙𝑤𝑤𝐻𝐻𝑆𝑆
𝑙𝑙𝑙𝑙𝑤𝑤𝐼𝐼𝐼𝐼

18,01318.01318.01318.01318.01318,01318,01318,01318,01318,01318,01318,0130.134
6310.842
242.270305.448308.388
8.290
5.302
5.515
5.214
8.037
6.508
8.709

0.198
0.000
0.999
6207.789 150.966 25034.260
157.705
65.593689.162171.947
20.045643.544286.975
15.185 1008.656
0.997
5.017
10.128
0.608
4.183
6.535
0.710
2.998
6.467
1.098
2.720
6.916
1.148
3.915
10.030
1.409
2.989
9.339
1.063
5.379
10.673

lnK
lnY

Min

Max

Table 2: Definition of skills in WIOD SEA
Skill-type 1997 ISCED level
1
Low
2
Low
3
Medium4
Medium
5
High
6
High

Description
Primary education or first stage of basic education
Lower secondary or second stage of basic education(Upper) secondary education
Post-secondary non-tertiary education
First stage of tertiary education
Second stage of tertiary education- 18 -

Table 3: ISUR estimation results (whole sample)

𝑤𝑤𝐿𝐿𝐿𝐿𝑤𝑤𝑀𝑀𝑀𝑀
𝑤𝑤𝐻𝐻𝐻𝐻𝐾𝐾
𝑌𝑌

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_cons

Observations
R-squared(1)
𝑆𝑆𝐿𝐿𝐿𝐿0.002***(0.00)-0.000(0.00)-0.002***(0.00)
0.102***(0.00)-0.181***(0.00)-0.006***(0.00)
1.087***(0.01)
17,9930.955(2)
𝑆𝑆𝑀𝑀𝑀𝑀-0.005***(0.00)
0.004***(0.00)
0.002***(0.00)-0.173***(0.00)
0.026***(0.00)
0.006***(0.00)
0.546***(0.01)
17,9930.924(3)
𝑆𝑆𝐻𝐻𝐻𝐻-0.004***(0.00)
0.004***(0.00)
0.002***(0.00)-0.117***(0.00)-0.003*(0.00)
0.005***(0.00)
0.480***(0.01)
17,9930.935

Notes: The set of equations are estimated by ISUR. Breusch-Pagan test of independence: 𝜒𝜒 2 (6)=17581.999, 𝑝𝑝=0.0000. Standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Fullset of year, country and sector FE dummies are included in the estimation.

- 19 -

Table 4: ISUR estimation results (by sector)

Goods Sector
𝑤𝑤𝐿𝐿𝐿𝐿𝑤𝑤𝑀𝑀𝑀𝑀
𝑤𝑤𝐻𝐻𝐻𝐻𝐾𝐾
𝑌𝑌

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_cons

Observations
R-squared

𝑆𝑆𝐿𝐿𝐿𝐿0.011***(0.00)
0.007***(0.00)-0.009***(0.00)
0.041***(0.00)-0.087***(0.00)
0.013***(0.00)
0.595***(0.01)
8,156
0.803

𝑆𝑆𝑀𝑀𝑀𝑀

0.001***(0.00)
0.000**(0.00)-0.000***(0.00)
0.023***(0.00)-0.047***(0.00)-0.002***(0.00)
0.321***(0.00)
8,156
0.951

Services Sector
𝑆𝑆𝐻𝐻𝐻𝐻
0.001***(0.00)
0.000***(0.00)-0.001***(0.00)
0.022***(0.00)-0.036***(0.00)
0.002***(0.00)
0.202***(0.00)
8,156
0.806

𝑆𝑆𝐿𝐿𝐿𝐿
0.000**(0.00)-0.000(0.00)
0.000(0.00)
0.029***(0.00)-0.055***(0.00)
0.000**(0.00)
0.349***(0.00)
9,837
0.990

𝑆𝑆𝑀𝑀𝑀𝑀

0.000(0.00)-0.000(0.00)
0.000(0.00)
0.079***(0.00)-0.140***(0.00)
0.002***(0.00)
0.770***(0.00)
9,837
0.988

𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)-0.000(0.00)
0.000(0.00)
0.041***(0.00)-0.081***(0.00)
0.001**(0.00)
0.526***(0.00)
9,837
0.981

Notes: The set of equations are estimated by ISUR. Breusch-Pagan test of independence: [goods] 𝜒𝜒 2(6)= 6942.175,
𝑝𝑝 =0.0000; [services] 𝜒𝜒 2 (6)= 6795.555, 𝑝𝑝 =0.0000. Standard errors are reported in parentheses (*** p<0.01, **p<0.05, * p<0.1). Full set of year, country and sector FE dummies are included in the estimation.

- 20 -

Table 5: ISUR estimation results (by sector tech-level)

GOODS SECTOR
Medium-tech

Low-tech
𝑤𝑤𝐿𝐿𝐿𝐿𝑤𝑤𝑀𝑀𝑀𝑀
𝑤𝑤𝐻𝐻𝐻𝐻𝐾𝐾
𝑌𝑌

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹
_cons

Obs.
R-squared

𝑆𝑆𝐿𝐿𝐿𝐿0.024***(0.00)
0.019***(0.00)-0.024***(0.00)
0.034***(0.01)-0.078***(0.01)-0.024***(0.01)
0.573***(0.02)
3,682
0.482

𝑆𝑆𝑀𝑀𝑀𝑀

0.001***(0.00)
0.001***(0.00)-0.001***(0.00)
0.022***(0.00)-0.046***(0.00)-0.003***(0.00)
0.314***(0.00)
3,682
0.948

𝑆𝑆𝐻𝐻𝐻𝐻

0.002***(0.00)
0.001***(0.00)-0.002***(0.00)
0.023***(0.00)-0.036***(0.00)-0.001**(0.00)
0.202***(0.00)
3,682
0.817

𝑆𝑆𝐿𝐿𝐿𝐿-0.001(0.00)-0.008***(0.00)
0.016***(0.00)
0.046***(0.01)-0.106***(0.01)
0.071***(0.01)
0.735***(0.02)
2,403
0.474

𝑆𝑆𝑀𝑀𝑀𝑀

0.000**(0.00)-0.000(0.00)
0.000(0.00)
0.021***(0.00)-0.045***(0.00)-0.001*(0.00)
0.316***(0.00)
2,403
0.953

High-tech
𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)-0.001***(0.00)
0.001***(0.00)
0.025***(0.00)-0.040***(0.00)
0.006***(0.00)
0.224***(0.00)
2,403
0.825

𝑆𝑆𝐿𝐿𝐿𝐿

0.000***(0.00)
0.000*(0.00)-0.000**(0.00)
0.021***(0.00)-0.039***(0.00)-0.002***(0.00)
0.250***(0.00)
2,071
0.963

𝑆𝑆𝑀𝑀𝑀𝑀

0.000(0.00)
0.000(0.00)-0.000(0.00)
0.034***(0.00)-0.059***(0.00)-0.001*(0.00)
0.361***(0.00)
2,071
0.961

𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)-0.000*(0.00)
0.000**(0.00)
0.012***(0.00)-0.021***(0.00)
0.000(0.00)
0.136***(0.00)
2,071
0.953

Notes: The set of equations are estimated by ISUR. Breusch-Pagan test of independence: [low] 𝜒𝜒 2 (6)= 2876.742, 𝑝𝑝=0.0000; [medium] 𝜒𝜒 2(6)= 2294.008, 𝑝𝑝=0.0000; [high]𝜒𝜒 2(6)= 2617.565, 𝑝𝑝=0.0000. Standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Full set of year, country and sector FE dummies are included in
the estimation.

21

Table 5: ISUR estimation results (by sector tech-level) – continued

SERVICES SECTOR
Medium-tech

Low-tech
𝑤𝑤𝐿𝐿𝐿𝐿𝑤𝑤𝑀𝑀𝑀𝑀
𝑤𝑤𝐻𝐻𝐻𝐻𝐾𝐾
𝑌𝑌

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹
_cons

Obs.
R-squared

𝑆𝑆𝐿𝐿𝐿𝐿

0.000(0.00)
0.000(0.00)-0.000(0.00)
0.029***(0.00)-0.056***(0.00)
0.000(0.00)
0.349***(0.00)
1,651
0.990

𝑆𝑆𝑀𝑀𝑀𝑀

0.000(0.00)-0.000(0.00)
0.000(0.00)
0.079***(0.00)-0.139***(0.00)
0.001(0.00)
0.770***(0.00)
1,651
0.988

𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)-0.000(0.00)
0.000(0.00)
0.040***(0.00)-0.081***(0.00)
0.000(0.00)
0.526***(0.00)
1,651
0.981

𝑆𝑆𝐿𝐿𝐿𝐿

0.000(0.00)-0.000(0.00)
0.000(0.00)
0.029***(0.00)-0.055***(0.00)
0.000(0.00)
0.349***(0.00)
4,304
0.990

𝑆𝑆𝑀𝑀𝑀𝑀

0.000(0.00)-0.000(0.00)
0.000(0.00)
0.079***(0.00)-0.140***(0.00)
0.002***(0.00)
0.770***(0.00)
4,304
0.988

High-tech
𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)-0.000(0.00)
0.000(0.00)
0.041***(0.00)-0.081***(0.00)
0.001**(0.00)
0.526***(0.00)
4,304
0.981

𝑆𝑆𝐿𝐿𝐿𝐿

0.000(0.00)-0.000(0.00)
0.000(0.00)
0.029***(0.00)-0.056***(0.00)
0.000**(0.00)
0.349***(0.00)
3,882
0.990

𝑆𝑆𝑀𝑀𝑀𝑀

0.000(0.00)-0.000(0.00)
0.000(0.00)
0.079***(0.00)-0.140***(0.00)
0.002***(0.00)
0.770***(0.00)
3,882
0.988

𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)-0.000(0.00)
0.000(0.00)
0.041***(0.00)-0.081***(0.00)
0.001(0.00)
0.526***(0.00)
3,882
0.981

Notes: The set of equations are estimated by ISUR. Breusch-Pagan test of independence: [low] 𝜒𝜒 2 (6)= 1138.048, 𝑝𝑝=0.0000; [medium] 𝜒𝜒 2(6)= 2981.426, 𝑝𝑝=0.0000; [high]𝜒𝜒 2(6)= 2676.014, 𝑝𝑝=0.0000. Standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Full set of year, country and sector FE dummies are included intheestimation.

22

Table 6: ISUR estimation results (by income group)

𝒘𝒘𝑳𝑳𝑳𝑳𝒘𝒘𝑴𝑴𝑴𝑴
𝒘𝒘𝑯𝑯𝑯𝑯𝑲𝑲
𝒀𝒀

𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭
_cons

Obs.
R-squared

𝑆𝑆𝐿𝐿𝐿𝐿0.006***(0.00)-0.002**(0.00)-0.003***(0.00)
0.016***(0.00)-0.053***(0.00)-0.012***(0.00)
0.427***(0.01)
4,286
0.403

Low Income
𝑆𝑆𝑀𝑀𝑀𝑀-0.000(0.00)
0.001***(0.00)-0.000(0.00)
0.056***(0.00)-0.098***(0.00)
0.006***(0.00)
0.571***(0.00)
4,286
0.876

𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)
0.001***(0.00)-0.000(0.00)
0.065***(0.00)-0.092***(0.00)
0.012***(0.00)
0.451***(0.00)
4,286
0.892

𝑆𝑆𝐿𝐿𝐿𝐿0.004***(0.00)
0.009***(0.00)-0.004***(0.00)
0.015***(0.00)-0.049***(0.00)
0.002(0.00)
0.413***(0.01)
5,167
0.430

Lower-middle Income
𝑆𝑆𝑀𝑀𝑀𝑀
𝑆𝑆𝐻𝐻𝐻𝐻-0.000-0.000(0.00)(0.00)
0.001*
0.001***(0.00)(0.00)
0.001**
0.001***(0.00)(0.00)
0.055***
0.064***(0.00)(0.00)-0.099***-0.093***(0.00)(0.00)
0.003***
0.008***(0.00)(0.00)
0.584***
0.460***(0.00)(0.00)
5,167
5,167
0.880
0.894

Upper-middle Income
High Income
𝑆𝑆𝐿𝐿𝐿𝐿
𝑆𝑆𝑀𝑀𝑀𝑀
𝑆𝑆𝐻𝐻𝐻𝐻𝑆𝑆𝐿𝐿𝐿𝐿
𝑆𝑆𝑀𝑀𝑀𝑀
𝑆𝑆𝐻𝐻𝐻𝐻
0.003***-0.0000.000
0.005***-0.000-0.001***𝒘𝒘𝑳𝑳𝑳𝑳(0.00)(0.00)(0.00)(0.00)(0.00)(0.00)
0.004***
0.001
0.001***-0.0010.001
0.002***
𝒘𝒘𝑴𝑴𝑴𝑴(0.00)(0.00)(0.00)(0.00)(0.00)(0.00)-0.001*0.000
0.000-0.0010.001*
0.001***
𝒘𝒘𝑯𝑯𝑯𝑯(0.00)(0.00)(0.00)(0.00)(0.00)(0.00)
0.012***
0.056***
0.064***
0.015***
0.056***
0.065***
𝑲𝑲(0.00)(0.00)(0.00)(0.00)(0.00)(0.00)-0.049***-0.099***-0.093***-0.051***-0.099***-0.094***𝒀𝒀(0.00)(0.00)(0.00)(0.00)(0.00)(0.00)
0.029***
0.002
0.008***-0.003-0.001***0.007***𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭(0.01)(0.00)(0.00)(0.00)(0.00)(0.00)_cons
0.422***
0.581***
0.459***
0.419***
0.583***
0.461***(0.01)(0.00)(0.00)(0.01)(0.00)(0.00)
Obs.
4,283
4,283
4,283
4,257
4,257
4,257
R-squared
0.396
0.878
0.893
0.397
0.879
0.894
Notes: The set of equations are estimated by ISUR. Breusch-Pagan test of independence: [low] 𝜒𝜒 2 (6)= 3635.721,
𝑝𝑝 =0.0000; [lower-middle] 𝜒𝜒 2 (6)=4467.928, 𝑝𝑝 =0.0000; [upper-middle] 𝜒𝜒 2 (6)=3665.356, 𝑝𝑝 =0.0000; [high]𝜒𝜒 2(6)=3602.589, 𝑝𝑝=0.0000. Standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Full set ofyear, country and sector FE dummies are included in the estimation.

23

Table 7: ISUR estimation results (by region)

Asia
𝑤𝑤𝐿𝐿𝐿𝐿𝑤𝑤𝑀𝑀𝑀𝑀
𝑤𝑤𝐻𝐻𝐻𝐻𝐾𝐾
𝑌𝑌

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹
_cons

Obs.
R-squared

𝑆𝑆𝐿𝐿𝐿𝐿0.006***(0.00)
0.001(0.00)-0.000(0.00)
0.013***(0.00)-0.048***(0.00)
0.017***(0.01)
0.420***(0.01)
3,969
0.403

𝑆𝑆𝑀𝑀𝑀𝑀-0.000(0.00)
0.002***(0.00)-0.001**(0.00)
0.056***(0.00)-0.098***(0.00)-0.001(0.00)
0.573***(0.00)
3,969
0.877

Europe𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)
0.002***(0.00)-0.000(0.00)
0.064***(0.00)-0.092***(0.00)
0.012***(0.00)
0.452***(0.00)
3,969
0.893

𝑆𝑆𝐿𝐿𝐿𝐿0.004***(0.00)
0.001**(0.00)-0.003***(0.00)
0.016***(0.00)-0.052***(0.00)
0.004**(0.00)
0.417***(0.00)
11,9900.407

𝑆𝑆𝑀𝑀𝑀𝑀-0.000(0.00)
0.000(0.00)
0.001***(0.00)
0.056***(0.00)-0.099***(0.00)
0.003***(0.00)
0.584***(0.00)
11,9900.879

Americas
𝑆𝑆𝐻𝐻𝐻𝐻

0.000(0.00)
0.000(0.00)
0.001***(0.00)
0.065***(0.00)-0.094***(0.00)
0.007***(0.00)
0.461***(0.00)
11,9900.893

𝑆𝑆𝐿𝐿𝐿𝐿

0.003***(0.00)
0.002(0.00)-0.005***(0.00)
0.016***(0.00)-0.055***(0.00)-0.029**(0.01)
0.437***(0.01)
2,034
0.386

𝑆𝑆𝑀𝑀𝑀𝑀-0.000(0.00)
0.001(0.00)
0.000(0.00)
0.055***(0.00)-0.098***(0.00)
0.003(0.01)
0.575***(0.00)
2,034
0.876

𝑆𝑆𝐻𝐻𝐻𝐻-0.000(0.00)
0.000(0.00)
0.001(0.00)
0.064***(0.00)-0.093***(0.00)
0.011**(0.00)
0.455***(0.00)
2,034
0.892

Notes: The set of equations are estimated by ISUR. Breusch-Pagan test of independence: [Asia] 𝜒𝜒 2(6)= 3397.848, 𝑝𝑝=0.0000; [Europe] 𝜒𝜒 2(6)= 10241.881, 𝑝𝑝=0.0000;[Americas] 𝜒𝜒 2 (6)= 1717.434, 𝑝𝑝=0.0000. Standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Full set of year, country and sector FE
dummies are included in the estimation.

24

Table 8: Elasticities of FVAiXAllSectorGoods
Services
Income group
Low
Lower-middle
Upper-middle
High
Tech-typeLow-tech goods
Medium-tech goods
High-tech goods
Low-tech services
Medium-tech services
High-tech services

𝑺𝑺𝑳𝑳

𝑺𝑺𝑴𝑴

-0.096***(0.01)

0.081***(0.01)

0.070***(0.01)

0.197***(0.06)
0.005**(0.00)-0.023***(0.00)
0.020***(0.00)

0.025***(0.01)
0.011**(0.00)-0.182***(0.07)
0.036(0.04)
0.450***(0.09)-0.053(0.07)

0.081***(0.02)
0.038***(0.01)
0.020(0.03)
0.007(0.02)

0.183***(0.02)
0.115***(0.01)
0.116***(0.03)
0.104***(0.02)-0.373***(0.09)
1.110***(0.11)-0.031***(0.01)
0.006(0.01)
0.002(0.00)
0.008**(0.00)-0.037***(0.01)-0.012*(0.01)-0.013*(0.01)
0.015(0.01)
0.019***(0.01)
0.022***(0.01)-0.018**(0.01)
0.096***(0.01)
0.004(0.00)
0.005(0.01)
0.014**(0.01)
0.009(0.01)

RegionAsia

𝑺𝑺𝑯𝑯

0.264***-0.0080.184***(0.09)(0.03)(0.03)
Europe0.065**
0.037***
0.106***(0.03)(0.01)(0.01)
Americas-0.456**0.042
0.174**(0.21)(0.07)(0.07)
Notes: The elasticities are based on the estimates from the baseline regression results reported in Tables 37. Standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

25

Appendix
Table A.1: List of industries and their corresponding sector and tech-type classifications(35 industries)
Abbrev.
AHFF
MNQR
FBAT
TATP
LLAF
WPWC
PPPP
CPNF
CACP
RUPL
ONMM
BMFM
MNEC
ELOE
TREQ
MNRE
EGWS
CNST
SMRM
WTCT
RETR
HORE
INTR
WATR
ARTR
OSAT
PTEL
FINI
REEA
RENT
PASS
EDUC
HESS
OCSP
PHEP

Industry
Agriculture, Hunting, Forestry and Fishing
Mining and Quarrying
Food, Beverages and Tobacco
Textiles and Textile Products
Leather, Leather and Footwear
Wood and Products of Wood and Cork
Pulp, Paper, Paper , Printing and Publishing
Coke, Refined Petroleum and Nuclear Fuel
Chemicals and Chemical Products
Rubber and Plastics
Other Non-Metallic Mineral
Basic Metals and Fabricated Metal
Machinery, Nec
Electrical and Optical Equipment
Transport Equipment
Manufacturing, Nec; Recycling
Electricity, Gas and Water Supply
Construction
Sale, Maintenance and Repair of Motor Vehicles Retail Sale of Fuel
Wholesale Trade and Commission Trade, Except of Motor Vehicles
Retail Trade, Except of Motor Vehicles ; Repair of Household Goods
Hotels and Restaurants
Inland Transport
Water Transport
Air Transport
Other Supporting and Auxiliary Transport Activities; Activities of Travel
Agencies
Post and Telecommunications
Financial Intermediation
Real Estate Activities
Renting of M&Eq and Other Business Activities
Public Admin and Defense; Compulsory Social Security
Education
Health and Social Work
Other Community, Social and Personal Services
Private Households with Employed Persons

26

SectorAgriculture
Agriculture
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Services
Services
Services
Services
Services
Services
Services
Services
Services
Services

Tech
Low
Med
Low
Low
Low
Low
Med
Med
High
Med
Low
Low
High
High
High
Med
Med
Low
Low
Med
Med
Low
Med
Med
High
Med

Services
Services
Services
Services
Services
Services
Services
Services
Services

Med
High
Med
High
High
High
High
High
High

Table A.2: Alphabetical country codes and list of countries by income group
CodeCountry

CodeCountry

Australia
Austria

Gro
up
3
4

AUS
AUT

EST
FIN

BEL
BRA

BelgiumBrazil

3
1

BGR
CAN
CHN

Bulgaria
Canada
China

CYP
CZE

CyprusCzech
Republic
Denmark

DNK

CodeCountry

Estonia
Finland

Gro
up
2
4

Gro
up
3
2

Code

Country

JPN
LVA

Japan
Latvia

RUS
SVK

2
4

SVN
KOR

2
1
4

ESP
SWE
TWN

POL
PRT

Lithuania
Luxembourg
Malta
MexicoNetherlands
PolandPortugal

RussiaSlovakRepublic
Slovenia
South
Korea
Spain
SwedenTaiwanFRA
DEUFranceGermany

3
3

LTU
LUX

1
3
1

GRC
HUN
IND

GreeceHungary
India

2
2
1

MLT
MEX
NLD

3
2

IDN
IRL

Indonesia
Ireland

1
4

2
2

TUR
GBR

4

ITA

Italy

3

ROU

Romania

1

USA

TurkeyUnitedKingdom
UnitedStates

Gro
up
1
2
2
2
3
4
2
1
4
4

Notes: The categorization is based on the values for GDP per capita (in current USD) in 2007 from theWorld Development Index. The 1st quantile is 11,155.83, the 2nd is 27,478.21, the 3rd is 44,654.56,
and the 4th is 107,098.7.

27