International Relocation of Production and Growth

International Relocation of Production and
Growth
Francisco Alcalá⇤and Marta Solaz†‡
November 2015. Preliminary draft
Abstract
The process of international relocation of production from high to lowincome countries is a central feature of economic globalization and, potentially, an important determinant of the recent dynamics of output and
employment across countries. So far, studies of this phenomenon focus
on particular products and regions or countries. This paper contributes
to the analysis in two ways. First, using very disaggregated trade data,
the paper examines the direction and intensity of this relocation process
across all sectors between 1995 and 2007. Second, it analyzes how this
process has a↵ected growth across countries. We find that countries that
were specialized in 1995 in products that, on average, relocated towards
low-income (high-income) economies over the following years exhibited significantly lower (greater) growth over the 1995-2007 period. This impact
is statistically significant and economically important: a di↵erence of one
standard deviation in the country’s relocation impact index resulted in a
di↵erence of about 0.7 percentage points in the country’s average annual
growth.
Keywords: trade; export sophistication; o↵shoring; growth.
JEL Classification: F14; F43; O47.
⇤
Universidad de Murcia and Ivie. Contact: [email protected]
Universitat de València. Contact: [email protected]
‡
Financial support by the Spanish Ministerio de Economı́a y Competitividad, project
ECO2014-53419-R is gratefully acknowledged. Marta Solaz also thanks the Spanish Ministerio de Educación for the FPU grant AP2010-0596.
†
1
1
Introduction
Over the last decades, the process of the international relocation of production
from higher to lower-income countries has been a central feature of the increase
in economic globalization. This process is likely to have considerably influenced
the dynamics of output and employment across countries. The importance of
this phenomenon has motivated numerous studies on specific sectors, regions,
and countries (e.g., Lall, Albaladejo and Zhang (2004); Marin (2006); Sturgeon,
Van Biesebroeck and Gereffi (2008); Ebenstein et al. (2014), Timmer et al.
(2015)). However, to the extent of our knowledge, this phenomenon and its
impact has not been analyzed systematically across all sectors and countries
for which the relevant data are available. How important is the international
relocation of production across the di↵erent sectors? What is the growth impact
of this process across countries? This paper studies the process of international
relocation of production between 1995 and 2007, the direction and intensity with
which each sector has been a↵ected, and its impact on cross-country growth.
An early analysis of the dynamics of the reorganization of production across
countries with di↵erent levels of development is the product life-cycle theory put
forward in Vernon (1966). According to this theory, new products are invented
and developed in the advanced economies, from which they are initially exported.
Then, as the production process becomes increasingly standardized, the lessdeveloped countries become attractive locations for their production because they
o↵er competitive advantages in terms of cost-saving. At this later stage of the
product life-cycle, part or all of their production shifts to less developed countries. These dynamics lead to a continuous process of international relocation
of production that has been reinforced in recent times by production fragmentation and o↵shoring (see, for example, Feenstra (1998), Hummels, Ishii and Yi
(2001), Yi (2003), Koopman, Wang and Wei (2014), among a large and growing
literature).1 However, although the literature tends to contemplate international
production relocation as unidirectional (from rich countries to low-wage coun1
The analysis of the product life-cycle and the nature and limitations of o↵shoring has been
extended in numerous directions, among others, by Krugman (1979), Dollar (1986), Jensen and
Thursby (1986), Grossman and Helpman (1989 and 1991a), Antràs (2005), Acemoglu et al.
(2012), and Baldwin and Evenett (2015).
2
tries), this is a bidirectional process as we observe it in the international trade
statistics: while some product categories (at the 6-digit level) exhibit a relocation
trend towards low-wage countries, other product categories experience an overall
relocation trend towards more advanced economies. These upward relocations are
the likely result of increasing innovation and technical sophistication in particular industries and product categories. This paper’s approach deals symmetrically
with the relocation movements in either direction.
Our analysis builds on the work by Hausmann, Hwang, and Rodrik (2007)
(henceforth HHR). These authors argue that specializing in some goods is more
growth promoting than specializing in others (what you export matters). More
sophisticated products generate more knowledge externalities (as in Hausmann
and Rodrik, 2003) and provide better opportunities for growth. Sophisticated
products are identified by being produced by more advanced economies. HHR
associate to each product an index of sophistication (called P RODY ), which is
defined as a weighted average of the per capita GDPs of the countries exporting
the product. Then, they associate to each country an index of export sophistication (called EXP Y ), which is defined as a weighted average of the P RODY s
of the products exported by the country. They find that the countries’ EXP Y
help predict their future growth.2
However, as we explain in detail below, the HHR’s EXP Y indices capture
two elements: countries’ initial sophistication and shocks to products in the sub2
Another approach to the countries’ export sophistication and its implications for growth is
the one based on the concepts of complexity and the product space (Hidalgo, Klinger, Barabasi &
Hausmann (2007), Hidalgo & Hausmann (2009), Hidalgo (2009)). According to this approach,
goods are produced with collective-coordinated capabilities, knowledge, and skills. Using the
ubiquity of each good’s production and the diversity of each country’s exports, the authors
construct an index of goods’ complexity and an index the economies’ complexity. Given initial
per capita GDP, more complex economies have better opportunities to grow. This approach
has a number of specific virtues, such as the identification of key signs of a country’s economic
capabilities (such as diversification) and the provision of a diagnosis of each country’s development prospects on the basis of the product space and the families of connected goods. Still,
the complexity approach’s measures and results are highly correlated with those of the HHR’s
approach (Hidalgo, 2009) and reaches these results at the cost of loosing some of the simplicity and intuitive appeal of the latter. It is also unclear how the complexity approach can be
reinterpreted to analyze the international relocation of production; for example, if o↵shoring
reduces the diversification of advanced economies, does that mean that it also reduces their
complexity? In most respects, the HHR (2007) and the complexity approaches do not appear
to be substitutes but complementaries.
3
sequent period. In this paper, we develop and reinterpret HHR’s indices by
separating these two elements and isolating the relocation shocks from the latter
shocks to assess their growth impact. More specifically, in this paper we use the
change over time in the P RODY index as a measure of the product’s international
relocation across countries at di↵erent stages of development. An increase (reduction) in the P RODY of a product implies a relocation of its production towards
more advanced (less developed) countries. Thus, we use the rate of variation of
the P RODY s to study the aggregate dynamics of the international relocation
of production between 1995 and 2007 as well as the direction and intensity with
which each sector has been a↵ected by this process. Then, we construct country
measures of the intensity with which the relocation process has a↵ected the particular export basket of each country. Using these country measures, we estimate
the impact of international relocation on the countries’ economic growth. We find
that countries that were specialized in 1995 in products that, on average, experienced a relocation process towards lower-income (higher-income) economies over
the following years, exhibited lower (greater) growth over the 1995-2007 period.
The impact is statistically significant and economically important.
Before this latter analysis, we set forth a simple theoretical framework that
helps guiding the empirical analysis and interpreting the results. The model
explores shocks simultaneously leading to international production relocation and
income changes. As long as the production of each good involves product-specific
knowledge and skills, the model predicts that countries initially specialized in
the goods that relocate towards higher-income countries will tend to experience
income increases. The opposite will be true for countries initially specialized
in the goods experiencing standardization and relocation towards lower-income
countries.
The rest of the paper is organized as follows. Section 2 analyzes the dynamics
of the relocation process at the sector and 6-digit product level. Section 3 sets out
our simple theoretical framework. Section 4 studies the impact of the relocation
process on cross-country growth. Section 5 concludes.
4
2
International Relocation of Production
2.1
Measuring product relocation
HHR define the P RODY measure of good k’s sophistication at period t as:
P RODYkt
=
J
X
j=1
RCAtkj
P
GDP pctj ,
t
j RCAkj
where RCAtkj is country j’s revealed comparative advantage in product k at
period t, J is the number of countries, and GDP pctj is country j’s per-capita
GDP. Thus, the P RODY index is a weighted average of the exporting countries’
GDP per capita, where the weights are given by the countries’ specialization
in the product. We also consider the sophistication index of sector s, which is
defined as the weighted average of the 6-digit products’ P RODY s included in
sector s, using the value-shares of each product in each sector as weights. Thus,
we define:
P RODYst =
X
k2s
P RODY
t
!kW
,
t
!sW
(1)
t
t
where !kW
and !sW
are the value-shares of product k and sector s, respectively,
in world trade.
Note that if the production of a good moves from rich countries to developing countries,this good’s P RODY will decrease. Conversely, an increase in
a good’s P RODY indicates that its average exporter is now a more developed
country. Thus, we use the rate of variation in each good’s P RODY to measure
the international relocation of its production across countries at di↵erent stages
of development. Note that all the P RODY s tend to grow over time as GDPs
per capita tend to increase. However, the phenomenon we want to capture is
the bidirectional migration of production between richer and poorer countries.
Thus, we define good k’s (annual average) relocation index between periods 0
and T , R0,T
k , as the di↵erence between the growth of product k’s P RODY and
the weighted average growth for all the products, using the value-shares of each
5
product in world trade as weights:
R0,T
k
1
= log
T
✓
P RODYkT
P RODYk0
◆
✓
◆
K
1 X !k00 W + !kT0 W
P RODYiT
log
.
T k0 =1
2
P RODYi0
Hence, a positive R0,T
indicates that the average exporter of k at time T has
k
relatively higher income than at time 0, whereas a negative value indicates the
opposite. In turn, we define the relocation index of sector s as the weighted
average of the 6-digit products’ R0,T
indices included in the sector:
k
R0,T
=
s
X
k2s
P
R0,T
k
0
T
!kW
+ !kW
.
0
T
k2s (!kW + !kW )
(2)
Note that the change over time in a product’s P RODY has two components:
the potential change in the RCA of the exporting countries (e.g., lower income
countries increase their RCA in the product, while higher income countries decrease their RCA), and the change in these exporting countries’ per capita GDP.
The first component can be interpreted as the pure relocation e↵ect because it
only depends on the shift of production across countries with di↵erent income levels, whereas the second component does not involve a migration of production.
To measure this first component, we first define a variant of the P RODY index that we call the new shares-P RODY , nsP RODY , which is computed using
initial-period per capita GDPs and final-period RCAs:
nsP RODYk0,T
=
J
X
j=1
RCATkj
P
GDP pc0j .
T
j RCAkj
By keeping constant per capita GDPs, the di↵erences between the nsP RODY s
and the P RODY s are exclusively due to changes in RCA, that is, to changes in
the location of production. Second, we define the good k’s (annual average) pure
relocation index P R0,T
as:
k
P R0,T
k
1
= log
T
nsP RODYk0,T
P RODYk0
!
K
1 X !k00 W + !kT0 W
log
T k0 =1
2
6
nsP RODYi0,T
P RODYi0
!
.
The P R0,T
is positive or negative depending only on changes in the RCAk across
k
exporters with di↵erent initial incomes. A negative (positive) P R0,T
means that
k
production is moving from previously richer (poorer) countries, which are decreasing (increasing) their RCA in product k, to poorer (richer) countries whose
RCAk is increasing.
The intensity of the international production relocation process at a given
point in time can then be assessed by measuring the dispersion of the R0,T
or
k
P R0,T
indices. To measure this dispersion, we use the mean absolute deviak
tion (MAD) of the relocation indices, using as weights the average share of each
product in world trade (the formula is analogous for the P R0,T
indices):
k
M AD
⇣
R0,T
k
⌘
=
K
X
R0,T
k
k=1
0
T
!kW
+ !kW
.
2
(3)
A higher dispersion of the relocation indices reflects a more intense relocation
across the exporting countries’ income groups.
2.2
Data
To construct the P RODY indices, we use the data in the BACI (Base pour
l’Analyse du Commerce International, Gaulier and Zignago (2010)), which is
a database provided by CEPII (Centre d’Études Prospectives et d’Informations
Internationales). The original data of BACI come from the United Nations Statistical Division (COMTRADE database), over which an harmonization procedure
is applied for reconciling the data reported by the exporting and importing countries in order to generate a single figure consisting of each bilateral flow in FOB
values. We use the Harmonized System (HS)-1992 classification, which comprises
more than 5,000 goods. Data on GDP per capita, measured in 2005 prices PPP,
come from the World Bank’s World Development Indicators (WDI). The trade
data used for the calculations of the P RODY indices corresponds to a group
of 136 countries. This group responds to a consistent sample of the countries
o↵ering trade information over all the reference period (1995-2007) and having
7
a population of at least 500,000 inhabitants.3 The P RODY s are calculated using average trade data of three years to attenuate the potential distorting e↵ect
of atypical values that may arise from unusual exports in a given year. Therefore, we calculate initial P RODY s averaging trade data for 1995-1997 and final
P RODY s averaging data for 2005-2007. Our analysis ends in 2007 to avoid the
impact of the Great Recession.
The list of 6-digit products for which we use their P RODY s is reduced to
4,873 products from the original list of 5,036 products in the HS92 classification.
This is because we look for a consistent sample of products that were exported
every year by at least one country over the whole reference period (i.e., we exclude
the products that do not appear in the statistics of world trade in one or more
years between 1995 and 2007). These 4,873 products represent the 98.4% of all
the world trade during these years. In turn, the 18 sector classification that
we consider results from amending the 21 sections in the HS92 classification as
follows in order to break into two sectors some sections that are quantitatively
very important while merging other sections that encompass a very small share
of international trade. Specifically, we split section 6 into pharmaceuticals and
the rest of the chemicals; section 15 into iron and steel on the one hand and
the rest of metals and its manufactures on the other; section 17 (machinery) into
electrical equipment and mechanical appliances; section 17 (transport equipment)
into motor vehicles and the rest of transport equipment. Conversely, we group
together sections 8, 11 and 12 (leather, textiles and footwear); sections 9 and
10 (wood and paper); sections 13 and 20 (furniture and other manufactures and
stones); and sections 3, 14, 19 and 21 (fats and oils, pearls, arms and works of
art). We call the last sector miscellanea.
2.3
Relocation dynamics over 1995-2007
We now analyze the dynamics of the international relocation of production over
the period 1995-2007 using the relocation indices. Figure 1 compares the growth
3
As emphasized in HHR (2007), it is essential to use a consistent sample of countries to avoid
index changes due to a changing composition of the sample. Moreover, since non-reporting is
likely to be correlated with income, constructing P RODY for di↵erent countries could introduce
serious bias into the index.
8
rate of the P RODY s, World GDP per capita, and World trade, and Figure 2
shows the evolution of the intensity of the international production relocation
over the period as measured by the MAD of the annual relocation indices (see
expression (3)). Although the analysis in this paper is based primarily on trade
data at the 6-digit level of disaggregation, Figure 2 also shows the evolution over
time of the MAD of the relocation indices calculated at lower levels of disaggregation. This highlights the importance of using highly disaggregated data in
order to capture the intensity of the relocation process. The 1-digit lines are obtained after computing the P RODY s for the 18 sectors defined in Subsection 2.2.,
whereas the calculations for the 2-digit and the 6-digit lines use the P RODY s of
the 96 industries and the 4,873 products, respectively, corresponding to the HS92 classification.4 The intensity of the relocation process calculated at the 6-digit
level more than triples, on average, the intensity at the 2- and 1-digit level. This
is due to the heterogeneity of the dynamics of the 6-digit products within each
sector, which is likely to increase as a consequence of production fragmentation.
As numerous products within a the same sector move in opposite directions along
the exporters’ income ladder, their movements cancel out and disappear when we
use data at the sector level to measure international relocation. Thus, using data
at the sector level to measure the intensity of the relocation of production misses
most of the process. It may also be noted that the intensity of the relocation
process appears surprisingly constant over the 1997-2006 period.
Table 1 shows the relocation indices together with the initial P RODY s (the
average for 1995-1997). The sectors are ordered according to the P R index. Negative indices indicate sectors that are moving downwards in the exporters’ income
ladder. Omitting the miscellanea group, the sectors with the greatest relocation
towards lower-income countries over the period 1995-2007 are textiles/footwear,
electrical equipment, metals and manufactures excluding iron, and motor vehicles. Some of these sectors are well-known examples of industries that have
experienced intense production fragmentation and o↵shoring processes such as
electrical equipment. On the other side of the spectrum, the sectors showing the
greatest relocation towards high-income exporters are pharmaceuticals, transport
4
Figure 2 also depints the evolution of the nsP RODY s at the 6-digit level, which exhibit
values almost identical to those of the corresponding P RODY indices.
9
equipment excluding motor vehicles, and chemicals. These sectors are moving upwards along the exporters’ income ladder and are, thus, likely to be experimenting
relatively intense innovation.
The relocation indices R show very similar patterns. The correlation between
the relocation indices R and the pure correlation indices P R of the 6-digit products is extremely high: 0.985. The rankings of sectors according to these two
indices is also very similar (see Table 1). The main di↵erences between the two
indices occur for the minerals and textile sectors. Note that the P RODY index
of minerals is the most exposed to to changes in commodity prices (which would
a↵ect the exporters’ income and, therefore, the R index) without the production
of minerals undergoing any reorganization across countries (which is the only
possible source of changes in the P R index). This can explain that the greatest
di↵erence between the RI and the R indices corresponds to the minerals sector.
In turn, the P RODY index of some sectors such as textiles could increase as
well without any geographic relocation of production if low-income countries are
relatively specialized in this sector and there is income convergence across countries over the period (i.e., low-income countries grow relatively more than rich
countries). This again could explain the discrepancy between the P R and the R
indices.
Can we predict the sign of the future international relocation of each product or sector? The future direction of the international relocation, both at the
sector and at the 6-digit product level, appear largely unpredictable on the basis
of their export sophistication at the beginning of the period. Among the sectors
with high initial P RODY we can find both industries with very positive P R
indices (e.g., pharmaceuticals and chemicals) and industries relocating towards
lower-income countries (e.g., machinery and motor vehicles). Conversely, among
the sectors with low initial P RODY s, we find industries moving upwards along
the exporters’ income ladder (e.g., food and beverages) as well as industries moving downwards (e.g., textiles) (see Figure 3). At the 6-digit level, production
relocation is only very weakly negatively correlated with the initial P RODY (see
Figure 4). The relationship is statistically significant but the coefficient is very
small, and we can find about as many 6-digit products with high P RODY s moving up than moving down (the same is true if we examine 6-digit products with
10
low P RODY s)..5 This means that the current level of a product’s or sector’s sophistication is of no real help in predicting whether the product or the sector will
relocate towards richer or poorer countries in the following years. Consequently,
industrial policies aimed at promoting those industries with better prospects for
future innovation and regional policies aimed at anticipating the dangers of future
relocations of the local industry towards lower-income regions face the potentially
unsolvable difficulty of identifying which industries are those.
The contribution of each sector to the global transformation of international
trade not only depends on the intensity of the sector’s international relocation but
also on the sector’s weight in world trade. Table 2 shows each sector’s contribution
⇣
⌘
to the global intensity of production relocation as measured by M AD R0,T
and
k
⇣
⌘
M AD P R0,T
, where these contributions are calculated as follows (the formula
k
is analogous for the P R0,T
indices):
k
Contribution of s to M AD
⇣
R0,T
k
⌘
P
k2s
R0,T
k
k=1
0
T
R0,T
(!kW
+ !kW
)
k
= P
K
0
T
!kW
+ !kW
.
(4)
The highest contributions to the overall international relocation of production
come from machinery and mechanical appliances, textiles and footwear, electrical equipment, minerals, and chemicals excluding the pharmaceutical industry.
These contributions measure each sector’s role in the reorganization of world
trade flows across country income groups that took place over the 1995-2007
period.
3
A simple theoretical framework (preliminary)
Which are the shocks leading to the international relocation of production as
captured by the R and RI indices? How can they a↵ect cross-country growth?
In this section we build a simple model (without fully developing its general
equilibrium details) that links exogenous technological shocks to production re5
Moreover, this negative coefficient could be due, at least in part, to temporary shocks to
the exporters of particular products at the beginning of the period and measurement errors
(instrumenting initial P RODY reduces further the coefficient to a half).
11
location and cross-country di↵erences in growth. The model will help interpret
our empirical work.
The model assumes that goods are produced using a combination of generic
knowledge and skills (which are relatively abundant in rich countries) and productspecific knowledge and skills (which are relatively abundant in the countries that
exported each product in the recent past). Generic and product-specific knowledge and skills are relatively good substitutes. The production of di↵erent goods
involves di↵erent degrees of sophistication. Higher-sophistication goods are relatively intensive in knowledge and skills. In other words, higher sophistication
means that countries that have higher general and product-specific knowledge
and skills are relatively better endowed to produce the good. Technical shocks
can increase or reduce product sophistication. Increased sophistication is likely
to be the consequence of intense innovation in a given product category, which
makes more important the use of knowledge and skills. The opposite occurs
if a good’s sophistication decreases: as production standardizes, it requires less
knowledge and skills.
Changes in product sophistication will a↵ect the value and incomes of the
production factors. If a product category experiences intense innovation, the relative value of the skills and knowledge comparatively better suited to produce
these goods increase. Conversely, if the production of a good experiences increasing standardization. Therefore, countries that were specialized in the products
experiencing a wave of innovation will be better o↵ and grow faster, whereas
countries that were specialized in products experiencing intense standardization
will be negatively a↵ected and grow slower.
These ideas can be formalized in a simple way as follows. Goods are produced
using product-specific knowledge and labor with varying levels of generic human
capital. Country j’s product-specific knowledge in product k is the result of
learning by doing and, therefore, of some discounted sum of the production of k
t 1 6
in previous periods, which is denoted by Xjk
. In turn, we assume that all the
6
We assume that this knowledge (or, more importantly, the value of the increased productivity that it can generate) is appropriated by the firms producing k and not by the specific workers
producing k. This makes simpler the equilibrium of the labor market, which assumes that all
the workers in each country have the same human capital and, therefore, are homogeneous.
12
workers in country j have the same generic human capital, which is denoted by
hj . The production function is:
⇥
⇤1/
t 1
xjk = (Sk ) + hj + Xjk
dk Aj (`jk )1/2 ,
< 0,
(5)
where xjk is country j’s output of good k, Sk denotes the sophistication of product
k, dk is a product-specific technological parameter common to all the countries,
Aj is country j’s aggregate TFP that captures productivity factors such as its
institutional quality, size, and density, and `jk is the labor input used in j to
produce k. Note that we simplify by assuming that generic human capital and
product-specific knowledge are perfect substitutes.7 In turn, the assumption that
sophistication, on the one hand, and generic human capital and product-specific
knowledge, on the other hand, behave as gross complements is important for our
results (which are based on that generic and product-specific knowledge and skills
have a comparative advantage at producing higher-sophistication goods).
We consider four types of shocks: (a) country-specific shocks corresponding
to shocks in Aj ; (b) technological shocks to product k a↵ecting its sophistication
Sk , which can be positive (innovations) or negative (standardizations); (c) technological shocks not changing the productive sophistication of product k, which
correspond to shocks in dk and are be called sophistication-neutral technological
shocks; and (d) product demand shocks a↵ecting pk . Product shocks hit all the
countries with the same intensity. The question is how much each of these four
shocks a↵ects RCAkj and income across countries, thereby leading to mutually
connected international relocations of production and cross-country di↵erences
in growth.
We assume perfect competition. Whenever we consider a country or a product, we assume that it is small relative to the world economy, so that we can ignore
general equilibrium e↵ects when analyzing the consequences of shocks that are
specific to the country or to the product category. However, a product category
could have an important weight in a country’s production and, therefore, shocks
7
The results hold exactly the same if we assume that generic human capital
and ⌘product⇣
t 1
specific knowledge are combined according to a positively valued function f hj , Xjk
satisfying fh > 0 and fX t 1 > 0. However, assuming perfect substitutability lightens the exposition.
13
to this category can a↵ect the country’s GDP. Still, although this perfect competition setting is the general framework of our analysis, we make some comments
on how results would change if a country had a significant weight in the world
market of a particular product.
From (5), the value of the labor’s marginal productivity in country j when
⇥
⇤1/
t 1
producing k is pk (Sk ) + hj + Xjk
dk Aj 12 (`jk ) 1/2 , where pk is the price
of k. Hence, the equality of the marginal productivity of labor across products
implies, for each country j and any two products k and k 0 :
✓
`jk
`jk0
◆1/2
⇥
⇤1/
t 1
pk dk (Sk ) + hj + Xjk
=
⇥
⇤1/ .
t 1
pk0 dk0 (Sk0 ) + hj + Xjk
0
(6)
Therefore, for any two countries j and j 0 , and any two products k and k 0 , revealed
comparative advnatage satisfies:
"
t 1
(Sk ) + hj + Xjk
xjk xj 0 k
/
=
t 1
xjk0 xj 0 k0
(Sk0 ) + hj + Xjk
0
/
(Sk ) + hj 0 + Xjt0 k1
(Sk0 ) + hj 0 + Xjt0 k10
#2/
.
(7)
Thus, country-specific shocks in shocks in Aj , as well as product-specific neutral
shocks in dk and demand shocks pk do not a↵ect the countries’ RCAs, whereas
sophistication shocks to do a↵ect RCA. Taking derivatives with respect to Sk to
analyze how innovation and standardization a↵ects RCA, yields:
d
dSk
✓
Hence,
xjk xj 0 k
/
xjk0 xj 0 k0
d
dSk
⇣
◆
=
xjk xj 0 k
/
xjk0 xj 0 k0
fore, because
⌘
"
(Sk0 ) + hj 0 + Xjt0 k10
#2/
"
t 1
(Sk ) + hj + Xjk
#(2/
2
t 1
(Sk0 ) + hj + Xjk
(Sk ) + hj 0 + Xjt0 k1
0
⇥
⇤
t 1
(Sk ) 1 hj 0 + Xjt0 k1
hj + Xjk
·
. (8)
⇥
⇤2
(Sk ) + hj 0 + Xjt0 k1
0 if and only if hj 0 + Xjt0 k1
t 1
hj + Xjk
. There-
< 0, if the sophistication of k increases, then the country with
higher human capital and previous specialization in k will increase further its
RCA in k. More specifically: (i ) conditional on previous specialization, coun14
) 1
tries with higher human capital will increase their RCA in k; and (ii ) vice versa,
conditional on human capital, countries with higher previous output of k will
increase further their RCA in k.
In sum, innovation in a product category (as captured by an increase in the
product’s sophistication parameter Sk ) will tend to raise the RCA of countries
with relatively high human capital (which are likely to be the countries with
higher income), and reduce the RCA of countries with lower human capital,
thereby leading to a relocation of production towards higher income countries.
Simultaneously, the income and RCA in the countries that already were specialized in that product will also increase, thereby creating a positive link between
the positive international relocation of a good and the growth of the countries
that already had a specialization in the good. The opposite will tend to occur
in the case of the standardization of a product, as captured by a reduction in
the product’s sophistication parameter Sk . However, the other three types of
shocks that we considered (product-specific demand, neutral technological, and
country-specific shocks) lead to di↵erences in growth across countries but do not
tend to lead to the international relocation of production.
Returning now to the P RODY indices, recall that changes over time in a
product’s P RODY have two components: changes in the exporting countries’
RCAs across countries with di↵erent income levels and per capita GDPs. The
first component is the pure relocation e↵ect that is captured by the P R index,
whereas the second component is the increase in the exporters income. Thus,
we interpret the first component within our our theoretical framework as the
consequence of sophistication shocks Sk , whereas the second component could
be the consequence of any of the four shocks (product-specific demand shocks
on pk , neutral technological shocks on dk , and country-specific shocks on Aj , as
well as sophistication shocks on Sk ). The pure relocation indices P R will be
the base of our analysis of the impact of sophistication shocks Sk on growth.
In turn, we will also construct country specific P RODY s (cs P RODY s) that
exclude in the construction the data for a particular country (see the Appendix).
These cs P RODY s, which are used as instruments in 2SLS regressions, are not
a↵ected by the country-specific shocks on Aj and are the basis of our analysis of
the joint impact of the three product-specific shocks (i.e., on dk , pk , and Sk ) on
15
cross-country growth.
4
International Relocation and Growth
4.1
Measuring the impact on income growth
To measure export sophistication and how international relocation a↵ects each
country, we build again on HHR (2007). HHR define the EXP Y index of country
j’s export sophistication at time 0 as a weighted average of its exports’ P RODY ,
as follows:
X
hhrEXP Yj0,T =
0
P RODYkT !kj
.
k
Note that the hhrEXP Y , as defined by HHR, combines data from two di↵erent
0
periods: on the one hand, product shares in country j’s exports !kj
refer to the
initial period 0, while, on the other hand, the P RODYkT s refer to the final period
T .8 In this paper, we define an initial EXP Y , denoted by iEXP Yj0 , that is
calculated using the data of the initial period for both the export shares and the
P RODY s:
iEXP Yj0 =
X
0
P RODYk0 !kj
.
k
Then, we use the ratio between the HHR’s EXP Y and the iEXP Y to capture
the impact of the international production relocation on country j. We call this
ratio the country j 0 s relocation impact index between periods 0 and T:
RIj0,T
= log
hhrEXP Yj0,T
iEXP Yj0,T
P
0
P RODYkT !kj
= log Pk
.
0 0
k P RODYk !kj
(9)
Note that the di↵erence between hhrEXP Yj0,T and iEXP Yj0 originates exclusively from the change in the P RODY s , which in turn are the result of the
8
HHR constructed the P RODY measures using 6-digit trade data for the 1999-2001. Then,
they used these P RODY s together with income information for each year to construct their
initial EXP Y measures used in their growth regressions for the 1991-2003 and 1994-2003
periods, respectively. As we define another variation of the EXP Y index, we denote the
original HHR index by hhrEXP Y to highlight the distinction and include the superscript T
to highlight that the P RODY s being used in the calculation for the index corresponding to
period 0 are the final period P RODY s.
16
international relocation of production. A high (low) value of the relocation impact index RI means that the country’s export basket is made up of products
whose production, on average, has moved towards higher (lower) income countries
(i.e., their P RODY s have increased).
Next, recall that the change in the P RODY indices is, in turn, the result of
a pure relocation e↵ect, which is captured by the new shares P RODY s and P R
indices, and the potential increase in the exporters’ income. Thus, we can identify
the pure relocation impact on the countries’ export sophistication by defining a
country j’s pure relocation impact index between periods 0 and T, P RI 0,T
j , as:
P RIj0,T
P
0,T 0
k nsP RODYk !kj
= log P
.
0 0
k P RODYk !kj
In terms of the theoretical framework set in Section 3, this index captures the
impact of the sophistication shocks Sk , which are those leading to international
relocation of production. Then, we define the residual from the HHR’s EXP Y
as:
hhrEXP Y
residual0,T
j
P
= log P
k
k
0
P RODYkT !kj
0
nsP RODYk0,T !kj
⇣
P PJ
k
RCAT
kj
T
P
T GDP pcj
j=1
j RCAkj
= log P ⇣P
J
k
RCAT
kj
0
P
T GDP pcj
j=1
j RCAkj
⌘
⌘
0
!kj
.
0
!kj
This index is capturing other shocks a↵ecting the exporters’ income (such as
product-specific demand pk and neutral technological dk shocks) that do not lead
to international production relocation. Including this latter index in the regression can help avoid omitted variable biases. We can summarize our approach to
the impact of international relocation on the countries’ export sophistication as
a pair of decompositions on the HHR’s EXP Y index, as follows:
log hhrEXP Yj0,T = log iEXP Yj0,T + RIj0,T
= log iEXP Yj0,T + P RIj0,T + hhrEXP Y residual0,T
j (. 10)
17
These two decompositions are the basis of the econometric specifications that
follow.
4.2
Econometric procedure
We now proceed to the econometric analysis on the relationship between initial export sophistication, product relocation and economic growth within the
framework of growth regressions (Barro & Sala-i-Martin (2003)). Our first specification uses the first decomposition in expression (10): GDP per capita growth
is regressed on initial per-capita GDP, iEXP Y , the relocation impact index RI,
and a vector of controls Xj0 that include human and physical capital and measures
of institutional quality:
1
GDP pcTi
log
=
T
GDP pc0i
0
0
1 logGDP pci
+
0
2 log(iEXP Yj )
+
0,T
3 RI j
+
+
0
4 Xj
+ uj ,
(11)
where uj is the error term. The second specification substitutes the RI index
with the P RI index:
1
GDP pcTi
log
=
T
GDP pc0i
0
+
0
1 logGDP pci
+
0
2 log(iEXP Yj ) +
0,T
3 P RI j
+
0
4 Xj
+ uj .
(12)
The third specification uses the second decomposition in expression (10):
1
GDP pcTi
log
=
T
GDP pc0i
0
+
0
1 logGDP pci
+
0
2 log(iEXP Yj )
+
0,T
3 P Rj
+ hhrEXP Y residual0,T
+
j
0
4 Xj
+ uj (13)
We estimate several variations of the preceding equation by OLS as well as
2SLS. Using instrumental variables is important to deal with the potential problem of circularity that can arise from the use of the P RODY and EXP Y indices.
Note that the P RODY s are a weighted average of exporting countries’ per capita
income. These P RODY s are used to construct the EXP Y measures of export
sophistication, which in turn are then used to explain the country’s per capita
income at the end of the period. Thus, a country-specific shock (Aj in our theo18
retical model) could create a correlation between growth and the relocation index
that is not due to any world-wide technological shock leading to international production relocation but only to the country-specific shock. To break the potential
circularity and avoid this potential problem, we calculate specific P RODY s and
nsP RODY s for each country that are constructed excluding all the data relative
to the country (i.e., the country’s exports and GDP per capita). Then, we use
these country specific P RODY s and nsP RODY s to construct instruments for
the country’s EXP Y and relocation indices (see the Appendix for the specific
formulas of these instruments, which are denoted by adding a csp prefix to the
instrumented variable).
4.3
Data and descriptive statistics
The EXP Y and relocation impact indices are constructed using the P RODY s
from the previous sections and the 6-digit vale-’ shares in each country’s exports
from BACI. We consider four alternative measures of institutional quality from
the World Bank’s World Governance Indicators: rule of law, regulatory quality,
government e↵ectiveness, and corruption control. Our main measure for human
capital is years of schooling from Barro and Lee (2013). We also conduct robustness checks using the percentage of population over 25 years with the secondary
education complete (denoted as secondary), also from Barro and Lee (2013), and
the index of human capital per person from Penn World Tables 8.1 ( denoted as
PWT; Feenstra, Inklaar, and Timmer (2015)), which is also based on Barro and
Lee and returns to education from Psacharopoulos (1994). Capital intensity is
defined as the country’s capital stock per person engaged in production, which is
also obtained from PWT 8.1. We also consider some additional controls: share
of oil exports in total exports (from PWT 8.1), and population and land area
(from the World Bank’s WDI).
The sample of 136 countries used to construct the P RODY s is reduced to 113
countries when we consider human and physical capital variables. Furthermore,
a number of countries su↵ered important shocks in the 90s such as civil wars,
large ethnic conflicts, and the sometimes traumatic dismemberment from the
Soviet Union. Thus, economic fundamentals may not be the main driver of some
19
countries’ economic performance over the 1995-2007 period or during the 19951997 years that we take as the initial reference for income growth. Moreover,
these countries are potential outliers that could distort the econometric analysis.
To check for these potential outliers in our sample we identify the countries whose
output gap (actual GDP over Hodrick-Prescott filtered GDP) at the beginning of
the period (1995-1997) deviate from the sample median more than three times the
interquartile range. This filter selects six countries: Liberia, Rwanda, Moldova,
Ukraine, Tajikistan, and Kyrgyz Republic. We also applied the same criterion
(a deviation from the sample median of more than three times the interquartile
range) to the distribution of the pure relocation impact index and identified Sierra
Leona as an additional outlier. All of these seven countries either went through
civil wars during the period of analysis or su↵ered major upheavals as a result
of the break down of the Soviet Union. Thus, we run the regressions using, first,
the whole available sample of 113 countries and, then, excluding from this sample
the cited seven outliers.9
Table 3 reports the main variables’ descriptive statistics and correlations.
Figure 5 shows the high correlation between the RI and P RI indices. Figures
6 and 7show the scatterplots of initial GDP per capita against the RI and P RI
indices. Poorer countries exhibit higher relocation indices (i.e. the correlation is
negative) as well as larger indices’ dispersion. Figures 8, 9, 10, and 11 show the
correlations between per capita growth and initial EXP Y , relocation impact,
pure location impact, and the hhhEXP Y residual, respectively.
4.4
4.4.1
Results
Relocation impact
We estimate equations (11), (12), and (13) using data for the period 1995-2007.
Table 4 shows the OLS estimates of equation (11) with robust standard errors
in parenthesis. The first column replicates the main specification in HHR (2007)
and confirms their results using our sample, which contains about 50-percent
more countries than their sample. Their hhrEXP Y measure of export sophis9
The list of countries can be found in Table A1 in the Appendix.
20
tication, initial per capita GDP and human capital have the expected signs and
are significant at the 1-percent, though, our point estimate for the coefficient on
hhrEXP Y es smaller and, contrarily to their estimates, we find human capital
to be highly significant. We decompose hhrEXP Y into our measures of initial
export sophistication (iEXP Y ) and the relocation impact index (RI) in column
2. Both variables have a positive and statistically significant at 1-percent impact on growth. In columns 3-7, we consider alternative measures of institutional
quality and additional controls: the share of oil products in total exports, the
country’s population–in thousands of millions–, and the log of the country’s area.
The coefficient on the relocation impact index increases and keeps its statistical
significance, while the opposite occurs with the coefficient on initial export sophistication. In turn, regulatory quality is the only measure for institutions that
is positive and statistically significant at the 5-percent level. As our controls
for institutions followed this same heterogenous pattern of statistical significance
in all the specifications that we estimated, we prefer and always report in our
tables the estimates that use regulatory quality as the control for institutions.
All the additional controls included in columns 3-7 are statistically significant at
1-percent, implying that oil exporters and countries with large and high-density
populations10 experienced higher growth over the period. The specification in
the last column of Table 4 excludes from the sample the seven outliers that were
experienced very large exogenous shocks over the period of analysis, as explained
in the data section. The coefficient on the relocation impact index increases further as well as the significance of the control for institutional quality, whereas the
significance of iEXP Y drops below 10-percent.
Table 5 reports the 2SLS estimates using as instruments the EXP Y s and
relocation impact indices constructed with country-specific P RODY s (see the
Appendix for the details on their construction). Each one of these instruments
is a good predictor of the instrumented variable with very large F statistics, as
shown in Table 8 (in the Appendix), which reports the first-stage regressions. As
in Table 4, we replicate the HHR’s (2007) main specification in the first column
and again confirm their qualitative results with our instruments and sample. In
10
We find a negative coefficient for country area that combined with the positive coefficient
on population implies a positive e↵ect of population density.
21
column 2, we use our measures of initial export sophistication and relocation impact and find again both variables positive and statistically significant. Hence,
the previous results hold when using instrumental variables that control for the
potential circularity of the measures of export sophistication and international
production relocation. Compared with the OLS estimates, the coefficient and
statistical significance of iEXP Y decreases while those of the RI index stay
constant or even increase slightly. This same pattern is apparent in all of the
following specifications, in which we include the additional controls and consider
four di↵erent measures of institutional quality (columns 3-6), two additional measures of human capital (columns 7-8), and exclude the outliers (column 9; see the
section on data for details on the additional controls and alternative measures of
institutions and human capital). The relocation impact index is always very significant, while initial export sophistication is only significant at 10-percent o no
significant at all. The estimated coefficient for the RI index in our most complete
specification (column 9) implies an economically very important impact: an increase along the distribution of the relocation impact index from the first quartile
(0.13) to the third (0.22) implies an increase in the average annual growth rate of
about 1.1 percentage points. Initial per capita GDP, years of schooling, export
share of oil, population size, and country area have the expected signs and are
always significant at the 1-percent. Regulatory quality is also always significant
at the 1-percent except in one specification for which significance is 5-percent.
Overall, these results lend ample support to the hypothesis that being specialized in those products that exhibited a production relocation towards higher
(lower) income countries was very positive (negative) for per capita GDP growth
over the 1995-2007 period. At the same time, the results suggest that once we
isolate this relocation e↵ect, the remaining component of the countries’ export
sophistication (i.e., iEXP Y ) had, at most, a very limited e↵ect.
4.4.2
Pure relocation impact
We now analyze the results of using the narrow (pure) definition P RI of the
relocation impact index. Tables 6 and 7 display the OLS and 2SLS estimates, respectively. In Table 6, we only consider our preferred specification, which includes
22
all the controls, the years of schooling measure of human capital, and regulatory
quality for institutions. We consider, alternatively, the whole sample (columns
1 and 3) and the one excluding the seven outliers (columns 2 and 4). Columns
1 and 3 report the results of estimating equation (12), whereas columns 2 and
4 report the results estimating (13), that is, including the hhrEXP Y residual
variable. In Table 7, we consider all the alternative measures of institutional
quality and human capital. Estimates in this table are the most reliable ones as
we instrument iEXP Y and the relocation impact indices with the measures that
exclude each country information (see the Appendix).
We use as a benchmark for our comments our preferred and most complete
specification in Table 7, which is in column 9. At any rate, the alternative specifications lead to very similar results. In this preferred specification, as in almost
all the previous estimates, the controls for initial per capita GDP, human capital,
regulatory quality, share of oil exports, population, and land area (or, conversely,
population density given that we already control for population) are all statistically significant. Focusing on our key regressor, we find that the pure relocation
impact is significant at the 1-percent The estimated impact is economically very
important: taking a country from the 1st quartile (-0.121) to the 3rd quartile
(-0.036) along the distribution of the P RI index implies an increase in the annual rate of growth of 0.8 percentage points, whereas a rise of the size of one
standard deviation (which after excluding the outliers is 0.073) implies an annual
growth increase of 0.7 points. Thus, countries that were specialized at the beginning of the period in product categories showing a relocation process towards
advanced (low-wage) economies over the following years, exhibited significantly
greater (lower) growth over the period.
The coefficient on initial export sophistication ( iEXP Y ) is positive but not
statistically significant. This confirms the previous results suggesting that the
growth e↵ect ofinitial export sophistication along the 1995-2007 period is uncertain once it is dissociated from the relocation e↵ect and what we have called the
hhrEXP Y residual.. In turn, the coefficient on hhrEXP Y residual is positive,
statistically very significant, and quantitatively important. However, as previously discussed, this component is the one most likely to capture the impact of
potentially omitted shocks common to exporters of similar baskets of goods. Its
23
inclusion in the specification has only an econometric purpose as it controls for
those potential shocks that are common across similar exporters but did not led
to a relocation of production and exports across countries.
5
11
Concluding Comments
The process of international relocation of production has been a central feature of
economic globalization over the 1995-2007 period, which is likely to have had notable consequences for output and employment across countries. This paper has
explored the broad numbers of this process and its consequences on cross-country
growth. We find that countries that were specialized in 1995 in product categories
that, on average, relocated towards low-income (high-income) economies over the
following years, exhibited significantly lower (greater) growth over the 1995-2007
period. This impact is statistically significant and economically important: a difference of one standard deviation in the country’s pure relocation impact index
resulted in a di↵erence of about 0.7 percentage points in the country’s average annual growth. Conversely, once we identify and isolate the impact of the
product-specific shocks (such as those leading to relocation) during the period,
initial sophistication appears to have a weak e↵ect, if any, on subsequent growth.
Thus, what you export matters not so much because of the definite prospects
that a country’s current export basket creates, but because products experience
continuous specific (and, apparently, unpredictable) technological and demand
shocks that have a significant impact on the country’s growth performance.
As already noted, product relocations appear to be largely unpredictable. At
the six-digit level, we find that product relocation is only weakly (negatively)
correlated with the product’s initial sophistication index. Thus, the fact that a
product is currently an export of a low- (high-) income countries is of no real help
in predicting whether the product will relocate in the future towards higher or
lower income producers. Similarly, at the sector level, we find industries with high
11
The fact that the IV estimates for the coefficient on hhrEXP Y residual are significantly
lower than the OLS estimates (unlike the estimates for the coefficient on the pure relocation
impact index) is consistent with this idea of hhrEXP Y residual capturing country-specific
shocks (which do not a↵ect our instruments), besides capturing the common shocks to exporters
of similar products.
24
initial sophistication relocating towards higher-income countries (e.g., pharmaceuticals and chemicals), as well as industries relocating towards lower-income
countries (e.g., machinery and motor vehicles). Conversely, we find industries
with low initial sophistication moving upwards along the exporters’ income ladder (e.g., food and beverages), as well as industries moving downwards (e.g.,
textiles). This leaves little room for industrial policies aimed at promoting those
industries with better chances of moving up along the exporters’ income ladder
and for regional policies aimed at anticipating the dangers of future relocations
of the local industry towards lower-income areas. Still, the time profile of the relocation processes within each industry and the analysis of the global relocation
process along other periods deserve further research.
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28
Table 1: International Relocation of Production by Sector
Textiles, footwear, leather
Miscellanea
Electrical equipment
Metals and manuf. exc. Iron
Motor vehicles
Machinery and mech. appliances
Plastics
Furniture and other manufactures; ston
Minerals
Vegetable products
Instruments
Iron and manufactures thereof
Food, beverage and tobacco
Wood and paper
Animal products
Chemicals exc. Pharma
Transport equip., exc. Motor vehic.
Pharmaceuticals
Total economy
Initial
PRODY
Relocation
index (R)
8.803,54
8.156,48
17.084,87
13.820,73
19.266,84
19.315,33
16.914,22
14.230,03
11.944,21
8.573,63
19.482,27
13.221,75
10.768,08
15.237,37
13.130,50
17.560,97
14.621,00
17.130,54
-0,44%
-1,18%
-0,33%
-0,43%
-0,42%
-0,17%
-0,24%
0,16%
-0,59%
-0,19%
0,22%
0,81%
0,59%
0,94%
0,73%
0,81%
1,27%
2,72%
Pure
relocation
index (PR)
-0,79%
-0,65%
-0,51%
-0,44%
-0,31%
-0,24%
-0,18%
-0,13%
-0,06%
0,09%
0,32%
0,48%
0,58%
0,63%
0,84%
0,89%
1,34%
2,42%
Difference
R-PR
0,34%
-0,53%
0,18%
0,01%
-0,11%
0,07%
-0,06%
0,29%
-0,53%
-0,28%
-0,09%
0,33%
0,01%
0,31%
-0,11%
-0,08%
-0,07%
0,30%
Table 2: Sector contributions to the global relocation of production
Sector
Machinery and mech. appliances
Textiles, footwear, leather
Electrical equipment
Minerals
Chemicals exc. Pharma
Metals and manuf. exc. Iron
Iron and manufact. thereof
Motor vehicles
Transport equip. exc. Motor vehic.
Food, beverage and tobacco
Plastics
Wood and paper
Furniture, stone and others
Pharmaceuticals
Animal products
Miscellanea
Vegetable products
Instruments
Total economy
Contribution to Contribution to
MAD (R)
MAD (PR)
Weight in
world trade
1996-2006
1996-2006
1996-2006
11,6%
10,3%
9,2%
12,5%
7,0%
5,6%
5,3%
4,8%
3,9%
4,2%
3,9%
4,5%
2,8%
3,4%
2,9%
3,1%
2,5%
2,3%
100,0%
13,0%
11,2%
10,4%
8,4%
7,0%
5,9%
5,3%
5,0%
4,1%
4,1%
4,0%
4,0%
3,2%
3,1%
3,0%
2,9%
2,8%
2,5%
100,0%
14,8%
7,8%
12,3%
11,7%
6,5%
3,6%
4,8%
9,8%
2,4%
3,4%
4,6%
3,8%
3,4%
2,0%
2,3%
1,4%
2,7%
2,8%
100,0%
29mean absolute deviations of the 6-digit
Note: M AD(R) and M AD(P R) are the
relocation indices. The contribution of each sector s to M AD(R) and
M AD(P R) are calculated using expression (??).
Table 4: Impact of relocation on cross-country growth. OLS estimates
Dependent variable : Growth rate of GDP per capita
(1)
log hhrEXPY
Relocation Impact index
Rule of law
(3)
(4)
(5)
(6)
(7)
0.0239***
(0.0083)
0.0789***
(0.0251)
-0.0119**
(0.0053)
0.0004
(0.0024)
0.0157**
(0.0076)
0.0936***
(0.0282)
-0.0139**
(0.0053)
0.0021
(0.0024)
0.0135*
(0.0072)
0.0878***
(0.0254)
-0.0201***
(0.0058)
0.0143*
(0.0072)
0.0930***
(0.0275)
-0.0151***
(0.0055)
0.0155**
(0.0076)
0.0939***
(0.0285)
-0.0132**
(0.0053)
0.0130*
(0.0072)
0.1280***
(0.0248)
-0.0185***
(0.0054)
0.0282***
(0.0081)
log initial EXPY
log initial GDPpc
(2)
-0.0176***
(0.0049)
0.0024
(0.0024)
Regulatory Quality
0.0099**
(0.0038)
Government Effectiveness
0.0095**
(0.0037)
0.0042
(0.0030)
Control of Corruption
log Human Capital (years sch)
log Capital Intensity
-0.1137**
(0.0500)
0.0148***
(0.0049)
0.0021
(0.0033)
0.0224***
(0.0084)
0.0374***
(0.0099)
-0.0024***
(0.0009)
-0.0356
(0.0446)
0.0138***
(0.0045)
0.0046
(0.0035)
0.0332***
(0.0095)
0.0402***
(0.0087)
-0.0025***
(0.0009)
0.0135
(0.0451)
0.0154***
(0.0049)
0.0022
(0.0032)
0.0258***
(0.0090)
0.0380***
(0.0096)
-0.0025***
(0.0009)
-0.0139
(0.0445)
0.0010
(0.0020)
0.0147***
(0.0049)
0.0022
(0.0033)
0.0211**
(0.0083)
0.0380***
(0.0094)
-0.0024***
(0.0009)
-0.0404
(0.0456)
No
No
No
No
No
0.0174***
(0.0051)
-0.0001
(0.0035)
0.0153***
(0.0047)
-0.0014
(0.0035)
-0.1118**
(0.0511)
No
Oil exports (share)
Population
log area
Constant
Excluding outliers
Observations
R2
0.0145***
(0.0046)
0.0039
(0.0033)
0.0385***
(0.0088)
0.0407***
(0.0088)
-0.0025***
(0.0008)
0.0026
(0.0433)
Yes
113
113
113
113
113
113
106
0.326
0.362
0.437
0.473
0.442
0.436
0.512
Notes: Results from estimating equation (11) using OLS. The dependent variable
is the annual average growth rate of GDP per capita over 1995-2007. Robust
standard errors are in parentheses. The excluded outliers in the regression in
column (7) are Liberia, Rwanda, Moldova, Ukraine, Tajikistan, Kyrgyz Republic,
and Sierra Leona. Significance levels: *** 10-percent, ** 5-percent, * 1-percent.
30
31
9,931.7
-0.08
0.25
Pure Reloc. Impact Index (PRI)
hhr EXPY residual
0.25
-0.10
0.16
0.04
0.08
0.09
5,014.1
4,397.0
Max
0.13
-0.23
-0.09
0.33
0.23
0.48
3,008.1 21,147.5
2,503.7 18,117.6
138.7 68,975.7
Min
118
118
118
118
118
118
Observations
l_gdppc0
reguqual
l_hk_yrsch
l_capintens
oilsh
pop
l_area
l_iEXPY
RI
PRI
hhrEXPYresid
-0.09
0.05
0.24
0.03
-0.11
0.23
-0.07
0.08
0.43
0.23
0.57
0.83
0.72
0.92
0.15
-0.07
-0.15
0.87
-0.32
-0.43
0.10
0.64
0.75
-0.17
-0.05
-0.18
0.71
-0.10
-0.27
0.31
0.79
-0.07
-0.03
-0.13
0.66
-0.02
-0.20
0.37
0.05
-0.09
-0.09
0.84
-0.16
-0.31
0.25
g_gdppc l_gdppc0 reguqual l_hk_y~h l_capi~s
-0.04
0.16
0.23
-0.51
-0.30
-0.61
oilsh
0.37
0.10
-0.03
-0.06
0.05
pop
-0.02
-0.06
0.05
-0.25
l_area
Note: GDP per capita, initial EXP Y and the hhrEXP Y are measured in US$.
0.17
St. Dev.
5,652.2 11,854.1
Median
11,487.3 11,440.4
9,785.4
10,429.1
Mean
Reloc. Impact Index (RI)
hhr EXPY
Initial EXPY
per capita GDP
Statistic
-0.27
-0.38
0.12
l_iEXPY
Table 3: Descriptive statistics and correlations
0.91
0.55
RI
0.16
PRI
32
0.326
0.362
113
No
-0.1038*
(0.0534)
-0.0012
(0.0033)
0.0152***
(0.0045)
0.0221**
(0.0087)
0.0804***
(0.0254)
-0.0113**
(0.0053)
0.0003
(0.0023)
0.437
113
No
0.0022
(0.0031)
0.0235***
(0.0085)
0.0384***
(0.0095)
-0.0024***
(0.0008)
-0.0277
(0.0476)
0.0147***
(0.0046)
0.0142*
(0.0080)
0.0975***
(0.0286)
-0.0133***
(0.0050)
0.0020
(0.0023)
0.473
113
No
0.0046
(0.0033)
0.0343***
(0.0097)
0.0412***
(0.0083)
-0.0025***
(0.0008)
0.0210
(0.0479)
0.0138***
(0.0042)
0.0099***
(0.0036)
0.0120
(0.0077)
0.0912***
(0.0275)
-0.0196***
(0.0054)
0.442
113
No
0.0023
(0.0030)
0.0270***
(0.0092)
0.0390***
(0.0092)
-0.0024***
(0.0009)
-0.0053
(0.0478)
0.0154***
(0.0046)
0.0042
(0.0028)
0.0128*
(0.0078)
0.0962***
(0.0281)
-0.0147***
(0.0052)
(5)
0.435
113
No
0.0023
(0.0030)
0.0221***
(0.0083)
0.0390***
(0.0089)
-0.0024***
(0.0009)
-0.0324
(0.0482)
0.0011
(0.0019)
0.0147***
(0.0046)
0.0140*
(0.0080)
0.0967***
(0.0287)
-0.0128**
(0.0050)
(6)
0.444
113
No
0.0056
(0.0035)
0.0311***
(0.0099)
0.0415***
(0.0084)
-0.0024***
(0.0009)
0.0208
(0.0518)
0.0058**
(0.0024)
0.0108***
(0.0039)
0.0110
(0.0080)
0.0905***
(0.0283)
-0.0186***
(0.0058)
(7)
0.475
113
No
0.0346***
(0.0094)
0.0050
(0.0034)
0.0346***
(0.0098)
0.0440***
(0.0075)
-0.0026***
(0.0008)
0.0291
(0.0509)
0.0091**
(0.0036)
0.0105
(0.0080)
0.0904***
(0.0283)
-0.0198***
(0.0058)
(8)
Notes: Results from estimating equation (11) using 2SLS. The dependent variable is the average growth rate of
GDP per capita over 1995-2007. The variables log (hhrEXP Y ), log (iEXP Y ) and RI are instrumented using the
country specific P RODY variables log (csp hhrEXP Y ), log (csp iEXP Y ), and csp RI, (see the body text and the
Appendix for details and first-stage regressions). Robust standard errors are in parentheses. The excluded outliers
in the regression in column (9) are Liberia, Rwanda, Moldova, Ukraine, Tajikistan, Kyrgyz Republic, and Sierra
Leona. Significance levels: *** 10-percent, ** 5-percent, * 1-percent.
R
113
Observations
2
No
-0.1036*
(0.0541)
0.0001
(0.0034)
0.0174***
(0.0049)
-0.0174***
(0.0049)
0.0024
(0.0023)
0.0269***
(0.0086)
Excluding outliers
Constant
log area
Population
Oil exports (share)
log Capital Intensity
log Human Capital (PWT)
log Human Capital (secondary sch)
log Human Capital (years sch)
Control of Corruption
Government Effectiveness
Regulatory Quality
Rule of law
log initial GDPpc
Relocation Impact index
log initial EXPY
log hhrEXPY
Dependent variable : Growth rate of GDP per capita
(1)
(2)
(3)
(4)
Table 5: Impact of relocation on cross-country growth. IV estimates
0.510
106
Yes
0.0043
(0.0033)
0.0389***
(0.0091)
0.0427***
(0.0082)
-0.0025***
(0.0007)
0.0264
(0.0433)
0.0147***
(0.0044)
0.0098***
(0.0035)
0.0094
(0.0068)
0.1221***
(0.0285)
-0.0179***
(0.0050)
(9)
Table 6: Impact of pure relocation on cross-country growth. OLS estimates
Dependent variable : Growth rate of GDP per capita
(1)
(2)
(3)
(4)
0.0151*
(0.0080)
0.0454*
(0.0269)
0.0136
(0.0083)
0.0727**
(0.0336)
-0.0245***
(0.0062)
0.0112***
(0.0040)
0.0155***
(0.0049)
0.0066*
(0.0038)
0.0237**
(0.0098)
0.0397***
(0.0089)
-0.0028***
(0.0009)
0.0357
(0.0499)
-0.0235***
(0.0063)
0.0112***
(0.0042)
0.0173***
(0.0052)
0.0063
(0.0038)
0.0254**
(0.0099)
0.0413***
(0.0089)
-0.0028***
(0.0009)
0.0428
(0.0500)
0.0097
(0.0068)
0.0414*
(0.0240)
0.3853***
(0.0507)
-0.0151***
(0.0056)
0.0075**
(0.0032)
0.0097**
(0.0040)
0.0013
(0.0030)
0.0530***
(0.0088)
0.0302***
(0.0076)
-0.0012
(0.0008)
-0.0491
(0.0415)
0.0078
(0.0061)
0.0720**
(0.0281)
0.4012***
(0.0538)
-0.0121**
(0.0047)
0.0078**
(0.0032)
0.0099**
(0.0042)
0.0001
(0.0025)
0.0558***
(0.0086)
0.0318***
(0.0075)
-0.0010
(0.0007)
-0.0500
(0.0372)
Excluding outliers
No
Yes
No
Yes
Observations
113
106
113
106
0.408
0.402
0.613
0.65
log initial EXPY
Pure Relocation Impact index
hhr EXPY residual
log initial GDPpc
Regulatory Quality
log Human Capital yrs sch
log Capital Intensity
Oil exports (share)
Population
log area
Constant
2
R
Notes: Results from estimating equation (12) in columns 1-2 and equation (13)
in columns 3-4 using OLS. The dependent variable is the average growth rate of
GDP per capita over 1995-2007. Robust standard errors are in parentheses. The
excluded outliers in the regression in columns (2) and (4) are Liberia, Rwanda,
Moldova, Ukraine, Tajikistan, Kyrgyz Republic, and Sierra Leona. Significance
levels: *** 10-percent, ** 5-percent, * 1-percent.
33
34
113
Observations
2
0.403
113
No
0.0062*
(0.0036)
0.0255***
(0.0097)
0.0416***
(0.0087)
-0.0029***
(0.0009)
0.0379
(0.0508)
0.0153***
(0.0046)
0.0110***
(0.0039)
-0.0231***
(0.0059)
0.0143*
(0.0082)
0.0693**
(0.0325)
(2)
0.362
113
No
0.0037
(0.0033)
0.0160*
(0.0090)
0.0389***
(0.0096)
-0.0028***
(0.0009)
0.0056
(0.0506)
0.0171***
(0.0050)
0.0043
(0.0031)
-0.0177***
(0.0056)
0.0155*
(0.0084)
0.0714**
(0.0333)
(3)
0.355
113
No
0.0038
(0.0033)
0.0113
(0.0081)
0.0390***
(0.0093)
-0.0027***
(0.0009)
-0.0201
(0.0513)
0.0013
(0.0022)
0.0164***
(0.0049)
-0.0159***
(0.0055)
0.0167*
(0.0087)
0.0725**
(0.0342)
(4)
0.374
113
No
0.0070*
(0.0038)
0.0219**
(0.0099)
0.0416***
(0.0088)
-0.0027***
(0.0010)
0.0392
(0.0550)
0.0068***
(0.0026)
0.0119***
(0.0040)
-0.0220***
(0.0063)
0.0130
(0.0085)
0.0671**
(0.0326)
(5)
0.408
113
No
0.0387***
(0.0102)
0.0066*
(0.0037)
0.0259***
(0.0097)
0.0446***
(0.0080)
-0.0029***
(0.0009)
0.0470
(0.0539)
0.0101***
(0.0038)
-0.0233***
(0.0063)
0.0126
(0.0086)
0.0676**
(0.0332)
(6)
0.397
106
Yes
0.0064*
(0.0038)
0.0278***
(0.0097)
0.0439***
(0.0085)
-0.0029***
(0.0008)
0.0562
(0.0471)
0.0171***
(0.0048)
0.0112***
(0.0040)
-0.0222***
(0.0059)
0.0111
(0.0077)
0.0948**
(0.0386)
(7)
0.580
113
No
0.0028
(0.0028)
0.0445***
(0.0093)
0.0356***
(0.0079)
-0.0018**
(0.0008)
-0.0164
(0.0473)
0.0116***
(0.0039)
0.0086***
(0.0031)
0.0107
(0.0073)
0.0691**
(0.0291)
0.2471***
(0.0648)
-0.0170***
(0.0051)
(8)
Notes: Results from estimating equation (12) in columns 1-7 and equation (13) in columns 8-9 using 2SLS. The
dependent variable is the average growth rate of GDP per capita 1995-2007. The variables log (iEXP Y ), P RI, and
hhrEXP Y residual are instrumented using the country specific P RODY variables log (csp iEXP Y ), csp P RI,
and csp hhrEXP Y residual (see the body text and the Appendix for details and first-stage regressions). Robust
standard errors are in parentheses. The excluded outliers in the regressions in columns (7) and (9) are Liberia,
Rwanda, Moldova, Ukraine, Tajikistan, Kyrgyz Republic, and Sierra Leona. Significance levels: *** 10-percent, **
5-percent, * 1-percent.
R
0.356
No
Excluding dummies
Constant
log area
Population
Oil share (mean)
0.0036
(0.0034)
0.0130
(0.0082)
0.0384***
(0.0099)
-0.0027***
(0.0009)
-0.0142
(0.0499)
0.0165***
(0.0049)
-0.0165***
(0.0055)
0.0024
(0.0025)
0.0168*
(0.0087)
0.0740**
(0.0340)
log Capital Intensity
log Human Capital (PWT)
log Human Capital (secondary sch)
log Human Capital (years sch)
Control of Corruption
Government Effectiveness
Regulatory Quality
Rule of law
log initial GDPpc
hhr EXPY residual
Pure Relocation Impact index
log initial EXPY
(1)
Dependent variable : Growth rate of GDP per capita
Table 7: Impact of pure relocation on cross-country growth. IV estimates
0.618
106
Yes
0.0022
(0.0028)
0.0481***
(0.0087)
0.0375***
(0.0075)
-0.0017**
(0.0007)
-0.0074
(0.0377)
0.0122***
(0.0040)
0.0088***
(0.0030)
0.0075
(0.0063)
0.0951***
(0.0319)
0.2685***
(0.0632)
-0.0147***
(0.0046)
(9)
Figure 1: Growth rates of the P RODY s, international trade, and World GDP
Figure 2: Intensity of the International Relocation of Production using data at
di↵erent disaggregation levels
Note: The intensity of international relocation is measured by the mean absolute deviation (MAD) of the product relocation indices R0,T
k . The 1-digit line is
obtained using data for 18 sectors as explained in Subsection 2.2., whereas the
2-digit and the 6-digit lines correspond to data for the 96 industries and 4,873
products, respectively, in the HS-92 classification.
35
Figure 3: Initial 1995 P RODY and the pure relocation P R index for the 19952007 period (18 broad sectors)
36
Figure 4: Initial 1995 P RODY and the pure relocation index P R for the 19952007 period (6-digit products)
Note: The coefficients in the box are the result of estimating the expression:
P R0,T
= 0 + 1 log(P RODYk0 ) + u
k
37
Figure 5: Country relocation impact indices RI 0,T
and pure relocation impact
j
0,T
indices P RI j .
38
Figure 6: Initial per capita GDP and the impact of international relocation as
measured by the RI index.
Figure 7: Initial per capita GDP and the pure international relocation impact as
measured by the P RI index.
39
Figure 8: Countries’ Initial Export Sophistication (iEXP Y ) and per capita GDP
growth
Figure 9: International relocation impact (as measured by the RI 0,T
index) and
j
per capita GDP growth
40
Figure 10: International pure relocation impact (as measured by the P RI 0,T
j
index) and per capita GDP growth
41
Figure 11: hhrEXP Y residual0,T
index and per capita GDP growth
j
.35
ALB
.3
LAOBIH
BLR ESTTTO
LTU
DOM
ROM
LVA
LKA UKR
ARM
BTN
MKD
IRL
POL
BGR
MDA
ZMB MNG
FJI TUN TUR
SVNFIN SGP
JOR
HRV
NPL
CHL
MAR
SVK
HND
PHL
MUS
CHN
GEO
HUN
CZE
GRCSWE
HKG
SLV
KAZ
KGZ
THA MYS
TKM
AUT
PRT
GIN UZB
AZE IDN
NZL
KOR
PAK
ESP
PAN
GBR
ERI
NLD
MEX
CYP
CRI
IND
FRA
ITA
MDG
CAN
BLX
MLI
USA
DNK
DEU
AUS
SLE
TCDSEN NIC GTMPERZAF
RUS
BFA
ISRJPN
BRA
TGO
BEN
LBN
KHM
KEN
VNM
URY
CHE
TZA
BHR
TJK
DZA
MWI
GMB
CIV
CAF
MRT
COL
ETH
GUY
NOR
MOZ NER
UGA
BOL
EGY
RWA
BDI
GHA
ECU
VEN
ZAR
KWT ARE
SAU
PRY
GNB
CMR
PNG
IRN
YEM
NGAAGO COG
BGD
.15
hhrEXPY residual
.2
.25
LBR
.1
GAB
4
6
8
log initial GDPpc
10
12
Figure 12: hhrEXP Y residual0,T
index and initial per capita GDP
j
Appendix
A.1 Construction of the instrumental variables and first
stage regressions
As explained in the body text, to break the potential circularity of high P RODY
exports explaining the high income of a country, and the high income of a country
leading to the high P RODY of its exports, we calculate specific P RODY s for
each country that are constructed excluding all the data relative to the country (i.e., its exports and GDP per capita). Then, we use these country specific P RODY s to construct instruments for the country’s EXP Y and relocation
impact indices. In this appendix we provide the specific formulas used in the
42
calculations.
The country j’s specific P RODY s for good k are defined as:
cs P RODYk,t
j
=
X
i6=j
cs nsP RODYk,0,Tj =
X
i6=j
where RCAtki
j
P
P
RCAtki j
T
i6=j RCAki
RCATki j
T
i6=j RCAki
GDP pcti ,
j
GDP pc0i ,
j
is the country i’s revealed comparative advantage in good k
calculated by excluding country j’s exports from world trade. Thus, these indices
reflect the level of development of the countries other than j exporting product
k. The formulas for the EXP Y s and relocation impact indices constructed using
the cs P RODYk,t
j
and cs nsP RODYk,t
j
(which are denoted as csp for country
specific P RODY s) are the following:
csp hhrEXP Yj0 =
X
0
cs P RODYkT j !kj
,
k
csp
iEXP Yj0
=
X
0
cs P RODYk0 j !kj
,
k
csp hhrEXP Yj0
,
csp iEXP Yj0
P
0
cs nsP RODYk0,T !kj
= log k
,
csp iEXP Yj0
csp RIj0,T = log
csp P RIj0,T
csp hhrEXP Y residual0,T
= log P
j
csp hhrEXP Yj0
0,T 0
k cs nsP RODYk !kj
.
The following table reports the first stage regressions of the key 2SLS estimations.
43
Table 8: First-stage regressions
Dependent variable :
(1)
Relocation Impact
(PRIj)
(2)
0.9235***
(0.0262)
-0.1174
(0.1954)
0.0336**
(0.0143)
0.9832***
(0.0886)
log initial EXPY
log csp initial EXPY
csp Relocation Index
csp Pure Relocation Index
(3)
Pure Relocation
Impact (PRIj)
(4)
0.9243***
(0.0324)
0.0234*
(0.0141)
0.0095
(0.0064)
0.9331***
(0.0603)
0.0476
(0.1500)
-0.0410***
(0.0098)
-0.0013
(0.0084)
0.0089
(0.0073)
0.0058
(0.0074)
-0.0122
(0.0239)
-0.0584**
(0.0235)
0.0009
(0.0019)
0.0442
(0.1098)
0.0359
(0.0273)
1.0448***
(0.0679)
-0.0156***
(0.0045)
0.0058
(0.0038)
0.0056*
(0.0033)
0.0059*
(0.0033)
0.0240**
(0.0108)
0.0073
(0.0106)
-0.0017*
(0.0009)
-0.0138
(0.0497)
log initial EXPY
hhr EXPYresidual
(5)
0.1112***
(0.0201)
-0.0155
(0.0250)
-0.0176
(0.0161)
0.0090
(0.0146)
0.0656**
(0.0265)
0.1315***
(0.0332)
-0.0056
(0.0034)
-0.0301
(0.2396)
-0.0580***
(0.0129)
0.0057
(0.0101)
0.0156*
(0.0088)
0.0120
(0.0086)
0.0025
(0.0217)
-0.0491***
(0.0160)
-0.0012
(0.0024)
0.0569
(0.1052)
-0.1020
(0.1387)
-0.2364
(0.3449)
0.1097***
(0.0226)
-0.0143
(0.0192)
-0.0164
(0.0168)
0.0093
(0.0169)
0.0556
(0.0550)
0.1336**
(0.0540)
-0.0060
(0.0044)
-0.0012
(0.2525)
Yes
Yes
Yes
Yes
Yes
F-statistic
Observations
826.4
106
55.58
106
515.2
106
34.77
106
66.16
106
R2
0.982
0.835
0.982
0.785
0.874
csp hhrEXPY residual
log initial GDPpc
log Human Capital (years sch)
log Capital Intensity
Regulatory Quality
Oil share (mean)
Population
log area
Constant
Excluding dummies
Note. The specification of the regressions for log initial EXP Y (iEXP Y ) and RI
in the first two columns correspond to our preferred 2SLS estimation of equation
(11), which is in column 9 of Table 5. The specification of the regressions for
logiEXP Y , P RI, and hhrEXP Y residual in columns (3)-(5) correspond to our
preferred 2SLS estimation of equation (13), which is in column 9 of Table 7.
Robust standard errors are in parentheses. Significance levels: *** 10-percent,
** 5-percent, * 1-percent.
44
Table 9: List of countries considered in the regression analysis
ISO3
ALB
ARM
AUS
AUT
BDI
BEN
BGD
BGR
BHR
BLX
BOL
BRA
CAF
CAN
CHE
CHL
CHN
CIV
CMR
COG
COL
CRI
CYP
CZE
DEU
DNK
DOM
ECU
EGY
ESP
EST
FIN
FJI
FRA
GAB
GBR
GHA
GMB
Country name
Albania
Armenia
Australia
Austria
Burundi
Benin
Bangladesh
Bulgaria
Bahrain
Benelux
Bolivia
Brazil
Central African Republic
Canada
Switzerland
Chile
China
Cote d'Ivoire
Cameroon
Congo, Rep.
Colombia
Costa Rica
Cyprus
Czech Republic
Germany
Denmark
Dominican Republic
Ecuador
Egypt, Arab Rep.
Spain
Estonia
Finland
Fiji
France
Gabon
United Kingdom
Ghana
Gambia, The
ISO3
GRC
GTM
HKG
HND
HRV
HUN
IDN
IND
IRL
IRN
ISR
ITA
JOR
JPN
KAZ
KEN
KGZ
KHM
KOR
KWT
LAO
LBR
LKA
LTU
LVA
MAR
MDA
MEX
MLI
MNG
MOZ
MRT
MUS
MWI
MYS
NER
NLD
NOR
Country name
Greece
Guatemala
Hong Kong SAR, China
Honduras
Croatia
Hungary
Indonesia
India
Ireland
Iran, Islamic Rep.
Israel
Italy
Jordan
Japan
Kazakhstan
Kenya
Kyrgyz Republic
Cambodia
Korea, Rep.
Kuwait
Lao PDR
Liberia
Sri Lanka
Lithuania
Latvia
Morocco
Moldova
Mexico
Mali
Mongolia
Mozambique
Mauritania
Mauritius
Malawi
Malaysia
Niger
Netherlands
Norway
45
ISO3
NPL
NZL
PAK
PAN
PER
PHL
POL
PRT
PRY
ROM
RUS
RWA
SAU
SEN
SGP
SLE
SLV
SVK
SVN
SWE
TGO
THA
TJK
TTO
TUN
TUR
TZA
UGA
UKR
URY
USA
VEN
VNM
YEM
ZAF
ZAR
ZMB
Country name
Nepal
New Zealand
Pakistan
Panama
Peru
Philippines
Poland
Portugal
Paraguay
Romania
Russian Federation
Rwanda
Saudi Arabia
Senegal
Singapore
Sierra Leone
El Salvador
Slovak Republic
Slovenia
Sweden
Togo
Thailand
Tajikistan
Trinidad and Tobago
Tunisia
Turkey
Tanzania
Uganda
Ukraine
Uruguay
United States
Venezuela, RB
Vietnam
Yemen, Rep.
South Africa
Congo, Dem. Rep.
Zambia