WPS5043
Policy Research Working Paper 5043
Technology Adoption and Factor
Proportions in Open Economies
Theory and Evidence from the Global Computer
Industry
Ana P. Cusolito
Daniel Lederman
The World Bank
Development Research Group
Trade and Integration Team
&
Office of the Chief Economist
Latin America and the Caribbean
September 2009
Policy Research Working Paper 5043
Abstract
Theories of international trade assume that all countries factor intensities, and thus unit factor input requirements
use similar and exogenous technologies in the production can vary across economies. Using data on net exports of
of any good. This paper relaxes this assumption. The a single industry, computers, intellectual property rights
marriage of literatures on biased technical change and and factor endowments for 73 countries during 1980
trade yields a tractable theory, which predicts that 2000, the paper shows that once technological choices are
differences in factor endowments and intellectual considered, countries with different factor endowments
property rights bias technical change toward particular can become net exporters of the same product.
This papera product of the Trade and Integration Team, Development Research Group, and the Office of the Chief
Economist for Latin America and the Caribbeanis part of a larger effort in both departments to study the how the structure
of trade affects development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
The author may be contacted at dlederman@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Technology Adoption and Factor Proportions in Open
Economies: Theory and Evidence from the Global
Computer Industry
Ana P. Cusolito
Daniel Lederman
Development Research Group, The World Bank. The authors thank Irene Brambilla, Juan C. Hallak, James
Harrigan, Aart Kraay, Pravin Krishna, Norman Loayza, William Maloney, John McLaren, Claudio Raddatz, Ariell
Reshef and Sergio Schmukler for insightful comments. They also wish to express their gratitude to other seminar
participants at the Universidad de San Andrés (Argentina), the University of Virginia, and the World Bank. This
research was partly funded by the World Bank's Latin American and Caribbean Regional Studies Program and the
MultiDonor Trust Fund. The views expressed herein do not necessarily reflect the views of the World Bank, its
Board of Directors, or the governments which it represents.
1 Introduction
Theories of international trade, such as the factor proportions model, often
assume that countries use similar technologies in production or that techno
logical differences are Hicks neutral.1 In contrast, models of biased technical
change assert that innovation and technology adoption are determined by lo
cal factor endowments. This paper marries these two literatures. It proposes
a matching mechanism between factor endowments and technologies in open
economies, and it studies how the crosscountry pattern of trade changes
once technology choices are considered.
The theory concerns economies that are open and differ in their factor
endowments. Economies are composed of multiple goods, which can be pro
duced with a range of factorcomplementary machines. These machines are
traded in a global market, which is characterized by a monopolistic com
petitive structure. The model is tractable even though it predicts that unit
factor input requirements within industries can vary across countries.
The econometric analyses utilize data on factor endowments, intellec
tual property rights (IPRs), and net exports of computers and components,
an industry that has received much attention in the technology adoption
and growth literature. The data set covers 73 countries during 19802000.
The empirical models test for the existence of multiple technological country
groups in the data, and estimate the factor proportions model in a twostage
estimation procedure. The technology selection function is modeled as an
Ordered Probit, where endowments and IPRs determine technology choices.
The trade specialization equation follows closely the standard specification
of Rybczynski functions found in the trade literature.
The econometric results from our preferred estimator suggest the exis
tence of up to four distinct technological groups that differ in terms of their
unit factor input requirements in the production of computers. The evi
1
The term factorproportions refers both to relative abundance of factors of production
and relative intensity with which different factors of production are used in the production
of different goods. As Krugman and Obstfeld[23] explain "... because the Heckscher
Ohlin theory emphasizes the interplay between the proportions in which different factors
of production are available in different countries and the proportions in which they are
used in producing different goods, it is also referred to as the factorproportions theory."
1
dence rejects the hypothesis that the set of estimated Rybczynski coeffi
cients are statistically equivalent across technological country groups. Fur
thermore, these international differences are at least partly due to differences
in IPRs, after controlling for factor endowments, relative factor prices and
Hicksneutral productivity differences across countries.
The rest of this paper is organized as follows. Section 2 discusses the
related literatures. Section 3 introduces the model. Section 4 solves the equi
librium of the theoretical model. Section 5 presents the empirical strategy.
Section 6 discusses the empirical results, including alternative explanations
of heterogeneous Rybczynski coefficients. Section 7 concludes.
2 Related Literature
At least two distinct literatures are related to our model and empirical appli
cation. The first one is the trade literature on factor proportions and trade
patterns. The second one concerns biased technical change.
2.1 Trade and factor proportions
This literature can be divided into two strands of research. One explores the
implications of the factor proportions theory under the assumption that all
countries have access to the same technologies. A second assumes that there
are Hicksneutral technology differences across countries.
In the first strand, Harrigan[16] examines the production side of the factor
proportions model. The author employs manufacturing outputs and factor
endowments data for up to 20 OECD countries during 19701985. The most
robust evidence suggests that capital abundance is a source of comparative
advantage in most of the sectors, but the effects of skilled and unskilled
labor are not clear. The signs of the Rybczynski coefficients, however, change
across econometric specifications.
In the same vein, but motivated by a slightly different question, Schott[33]
investigates whether developed and developing countries specialize in differ
ent subsets of products as a result of their differences in factor endowments.
He proposes a methodology that distinguishes single from multiplecone
2
equilibria and allows for the effect of factor accumulation on a given sector's
output to vary with a country's endowments. Schott[33] uses valueadded,
capital stock, and employment data from UNIDO for up to 45 developed and
developing countries across 28 manufacturing industries in 1990. The find
ings reject the singlecone framework in favor of a twocone model with labor
abundant countries producing relatively little of the most capitalintensive
goods.
Romalis[31] examines how factor proportions determine the structure of
commodity trade by integrating a multicountry version of the Heckscher
Ohlin model with a continuum of goods with Krugman[22]'s model of mo
nopolistic competition and transport costs. His model assumes that there
are no factor intensity reversals and that factor shares are fixed within in
dustries and across countries. Two predictions emerge from this framework.
First, countries capture larger shares of world production and trade of com
modities that more intensively use their abundant factors. Second, countries
that rapidly accumulate a factor see their production and export structures
systematically shift towards industries that intensively use that factor.
In the second strand of the trade literature, Harrigan[17] provides the first
empirical test of the factor proportions theory in a framework that accounts
for international technology differences. The author uses manufacturing out
put shares and factor endowments data for up to 10 developed countries
across 7 industries with data from 19701988. The most reliable inferences
across sectors that can be obtained from this study are roughly consistent
with Leamer[24] and Harrigan[16]. Capital and mediumeducated workers
are associated with larger GDP output shares in most of the seven indus
tries (Food, Apparel, Paper, Chemicals, Glass, Metals and Machinery); while
nonresidential construction and higheducated workers are related to lower
output shares.
Harrigan[17] improves substantially upon previous empirical frameworks,
but his OECD data have little crosscountry variation as highincome coun
tries have similar factor endowments and sectoral output shares. To overcome
this drawback, Harrigan and Zakrajsek[18] work with a larger sample, which
includes data for up to 28 OECD and nonOECD countries and 12 industries
from 19701992. Their evidence arguably supports the neoclassical theory.
3
In a related article, Fitzgerald and Hallak[13] estimate the effect of factor
endowments on the pattern of manufacturing specialization in a crosssection
of OECD countries, taking into account that factor accumulation responds
to productivity. The authors show that the failure to control for productivity
differences across countries produces biased estimates of the Rybczynski co
efficients. Their model explains 2/3 of the observed differences in the pattern
of specialization between the poorest and richest OECD countries.
Hakura[15] explores the role of differences in production techniques to
explain the empirical failure of the HeckscherOhlinVaneck (HOV) model.
The paper develops a 2x2 modified HOV model that relaxes the assumption
of identical production techniques across countries. Using inputoutput data
for six member countries of the European Community for the years 1970 and
1980, the paper shows that allowing for international technique differences
significantly improves the predictive power of the HOV model.
In Redding[29], a country's pattern of specialization at any point in time
is characterized by the distribution of shares of GDP across industries. Its
dynamics are represented by the evolution of the entire crosssectional distri
bution of output shares over time. Redding[29] utilizes data on 20 industries
in 7 OECD countries from 19701990. A comparison of GDP shares between
1970 and 1990 reveals substantial variation across sectors and countries.
Perhaps more importantly, Redding[29] concludes that in the short run,
common crosscountry effects such as technological progress are more impor
tant in explaining observed changes in specialization than factor endowments
for the majority of the countries. Over longer periods, factor endowments
become relatively more important, and in the infinite horizon, factor en
dowments account for most of the observed variation in specialization. This
evidence is consistent with the idea that changes in relative factor abundance
occur gradually and take time to affect the structure of production.
Overall, the factor proportions model provides a story about static and
dynamic specialization around the world. Some evidence shows that tech
nological differences across countries can produce similar patterns of special
ization in spite of large differences in factor endowments (Schott[33]). Our
model extends the standard factorproportions theory to allow for technol
ogy differences across countries, thus introducing elements of the literature
4
on biased technical change into the factorproportions literature.
2.2 Biased technical change
This literature can also be divided into two different approaches. The first
one assesses whether factor shares vary systematically with the level of de
velopment (e.g. Young[34], Gollin[14], Bernanke and Gurkaynak[3], and Or
tega and Rodriguez[27]). The second investigates whether complementari
ties between inputs and technology bias technical change (e.g. Acemoglu[1],
Caselli[6]).
The first literature initially found that labor shares in national income
vary widely, ranging from 0.05 to 0.80 in international crosssectional data
(e.g. Elias[11] and Young[34]). Gollin[14] questioned these estimations by
arguing that the widely used approach, which is based on CobbDouglas pro
duction functions, tends to underestimate the labor income of selfemployed
workers, and the corrected labor shares fall in the range of 0.65 to 0.80.
This evidence was later reaffirmed by Bernanke and Gurkaynak[3], but re
jected by Ortega and Rodriguez[27]. The latter uses industrial survey data
to explore the same question, and controlling for the measurement problem
of selfemployed workers it found a significant negative crosssectional rela
tionship between capital share and per capita income within industries. In
a related paper, Dobbelaere and Mairesse[9], find that imperfections in the
product and labor markets generate a wedge between factor elasticities in
the production function and their corresponding shares in revenue, at firm
and industry levels.
The second approach builds on the works by Kennedy[20], Samuelson[32],
and Drandakis and Phelps[10], who proposed an induced innovation the
ory that highlights the relation between factor prices and technical change.
The modern formulation of this theory has been presented by Acemoglu[1],
who study how crosscountry differences in factor endowments bias technical
change. In his framework, the price and marketsize effects determine the
direction of technological change. The price effect reflects the incentives to
generate technologies that create more expensive goods. The second effect
captures the incentives to produce technologies for which there is a big mar
5
ket. While the former encourages innovations to complement scarce factors,
the latter leads to technical change favoring abundant factors. The elasticity
of substitution between different factors determines the relative magnitudes
of these effects. In the long run, technical change favors the abundant factor
if the elasticity of substitution is sufficiently large.
Evidence of complementarities between factors of production and technol
ogy has been provided by Caselli[6], who explored the relationship between
factor endowments and the composition of capital imports. The author finds
that humancapital abundant countries devote a larger share of their in
vestment to acquire complex technologies, which can only be employed by
skilledworkers.
We depart from the neoclassical trade literature by relaxing the assump
tion of Hicks neutral technological differences across countries, by allowing
countries to make their own technology choices. Thus, the model presented
in the following section complements the biasedtechnical change literature
by analyzing how countries' technology choices alter the impact of factor
endowments on trade patterns.
3 Model
Let c=1,...,C index countries, let f =1,...,F index factors, and let j =1,...,J
index industries. Countries are open to trade in goods and technology. They
differ in factor endowments and the degree of intellectual property rights
protection, m , with m=1,...,M. Each economy has two sectors, a final good
and a R&D sector.
3.1 Final good sector
Output of industry j in country c, Yjc , can be written as a constant elasticity
c
of substitution (CES) production function of factor inputs f, Vjf , and a set
of factorf complementary machines, Ac ,~
jf
F j 1 j
Yjc =[ ~c1j cj
jf (Ajf Vjf ) j
] j 1
. (1)
f =1
6
jf (0, 1) is a distribution parameter that captures how important factor f
is in the production of output j. We assume F=1 jf = 1. Parameter j is
f
the elasticity of substitution between two factors. The set of complementary
~
machines, Ac , has the following functional form:
jf
c
Nf 1
~jf
Ac = [ Ac (i) di] ,
jf (2)
0
c
where Nf is the number of varieties of factorf complementary machines
available to country c, and Ac (i) is the number of typei machines that
jf
country c acquires. Parameter determines the elasticity of substitution
between two varieties of the same type of equipment. Final goods producers
face a twostage decision process. First, they decide how many units of each
factor of production to hire. Second, they choose how many machines to buy
to complement each factor.
3.2 R&D sector
Firms in this sector produce machines that belong to the category of general
purpose technologies and thereby they can be employed in different sectors.
The world's technological market has a monopolistic competitive structure.
R&D firms face a twostep decision process. First, they decide to which
country to export. Second, they choose the price per unit of machine.
Each monopolist from country o that produces machines to complement
factor f in country d faces a marginal cost of production, ”o , and a fixed cost,
f
(d do,d ), of protecting his patent, with > 0 and (0) = 0. Parameter
do,d stands for the distance between countries. Entry in the research activity
o
involves a fixed cost, f .
4 Equilibrium
To find the equilibrium of the model we proceed in the following manner.
First, we solve backwardly the equilibrium for a representative firm in a
representative sector. Second, we characterize the equilibrium for the whole
economy. To solve the equilibrium for a sector we need to find the solutions
7
to the finalgoods producers' problem and the technology suppliers' problem.
This is presented in the following sections.
4.1 Final good producers
Firms in this sector choose how many machines to buy in order to complement
each factor of production. The problem for a representative firm in sector j
can be written as follows:2
F c
Nf
min{Ac j (i)} {
f
[ pf (i)Ac (i)di]}
jf (3)
f =1 0
subject to the following constraints:
j 1 j
1. [ F
f =1
~ 1j V cj )
jf (Ac j
] j 1 1
jf jf
Nfc
1
~jf
2. Ac = [ 0 Ac (i) di] ,
jf
where Ac (i) = Yjc ac (i) and ac (i) is the demand of machine i per unit of
jf jf jf
output j. The first order conditions for problem (3) deliver the following
solution to ac (i):
jf
ec p (i)
ac (i)
jf = jf f c ,
c
(4)
Pjf Pjf
where ec represents the expenditure that country c devotes to complement
jf
c 1
1 Nc
1
factor f per unit of output j, Pjf 0 f pf (i)di, and 1 is the
elasticity of substitution between two varieties of machines f. Equation (4)
shows that the demand of machine i is an increasing function of the real
ec
expenditure available to buy technology f, Pjf , and a negative function of
c
jf
the price of the machine, pf (i). Given this demand, firms minimize unit cost
functions to determine the optimal unit factor input requirements. They
solve the following problem:
2
For the sake of simplicity firms' subindexes are omitted.
8
F
c c
min{Vjf }f =1,...,F {
c wf Vjf } (5)
f =1
subject to the following constraints:
j 1 j
1. [ F ~ 1j V cj )
jf (Ac j
] j 1
1
f =1 jf jf
Nfc
1 Nf Yjc ec p (i)
c
1
~
2. Ac = [ 0 Ac (i) di] = [ 0 ( Pc
jf f
 ) di]
jf jf Pf
jf
c
where wf represents the cost per unit of factor f in country c. In the opti
mum, each factor's marginal product equals its marginal cost. The optimal
requirement of factor f per unit of output j in country c, Qc , is as follows:
jf
(1j )

j
F
ac (1j )+ wf jz (1j)+ (j 1)  ( 1)
~jz
~c j j
Qc
jf = ~c
ajf { jz [( ) j j j ( c ) j j j ] j } j j , (6)
z=1 ac
~jf ~
wz jf
Nc 1
where wf is the cost per efficiency unit of factor f and ac [ 0 f ac (i) di] .
~c ~jf jf
Equation (6) shows that differences in technology choices and relative fac
tor prices lead to endogenous differences in unit factor input requirements.
Specifically, technology choices affect unit requirements through two different
channels: a factor saving effect and a relative efficiency effect. According to
the first effect, larger values of ac increase the productivity of the factor and
~jf
reduce its requirements. Due to the second effect, factor f becomes relatively
more productive than other factors, which increases firms' incentives to hire
~c
more units. Lower values of wf (jz ), and increasingly negative (positive)
~c ~c
differences between wf (jf ) and wz (jz ), for z = f and z = 1, ...F , make
the second effect more prominent.
4.2 Technology suppliers
A monopolist from country o that sells machines to country d in order to
complement factor f solves the following problem:
o,d Yjd ed po
jf f
max{po } jf = (po  ”o )
f f . (7)
f
Pjf Pjf
o o
9
The solution to this problem delivers the following expression for the optimal
price, pf , at which he will sell the machine:
p o = ”o (
f f ). (8)
1
This price is a constant markup, ( 1 ), over the marginal cost of producing
the machine, ”o . Given the price, the monopolist decides whether to export
f
technology to country d. In doing so, he compares the benefits of selling the
machines with the fixed cost he has to pay to receive such benefits. Thus,
the monopolist sells his technology if and only if the following condition is
satisfied:
o,d
jf (d do,d ), (9)
which can be rewritten as follows:
d
Ejf
d
> (d do,d ), (10)
Nf
d
where Ejf Yjd ed . Assuming that is a linear function of d do,d , country
jf
d do,d N d
o exports technology to country d if and only if E d f is lower than 1.
jf
To continue with the characterization of the equilibrium, we substitute
equation (8) into (4) and we rewrite ad (i) as follows:
jf
ed
jf (  1)
ad (i) =
jf , (11)
o o(1)
oD Nf ”f
o
where D is the set of countries that provide technology to country d, and Nf
is the number of varieties that country o offers to complement factor f. This
number is determined by the free entry condition in the research activity of
country o. Entry in this country occurs until the marginal firm breaks even:
d
C J
Ejf d
d do,d Nf o
[ o o ]I[ d
< 1] = f . (12)
d=1 j=1 d do,d (Nf + Nf ) Ejf
10
I is an indicator function that takes value 1 if the condition in brackets is
d o o o
satisfied and 0 otherwise. Nf Nf + Nf , and Nf is the set of varieties
provided to country d by countries other than o.
d d
Notice that because Ejf = ed Yjd ; Ejf is a function of country d 's factor
jf
endowments. Furthermore, because (0) = 0, each country sells machines
to domestic technology demanders, and the number of varieties produced in
equilibrium is a function of the factor endowments of the country, among
other determinants. This result is thus similar to Schott's multiplecone
d
version of the neoclassical model. Both results imply that Nf is a function
of the factor endowments of the countries that provide technology to country
d. Thereby, we can rewrite ad (i) in the following manner:
jf
ed
jf (  1)
ad (i) =
jf d
Nf (d ,V o )
. (13)
”
n(1)
dn
0 f
where V o is a vector of the factor endowments of the countries that belong to
set D. By inserting equation (13) into equation (6) we can write unit factor
input requirements as follows:
d o
Nz (d ,Vz )
ed w d ”n(1) dn
Qd
jf = g( j , j , jz , , , d , f,
jz
d
d
0
d o
Nf (d ,Vf )
z
, do,d , V o )3 ,
oD
ejf wz n(1)
”f dn
commonwithinindustryj 0
countryspecif ic
(14)
with z = 1, ..., F and z = f . Equation (14) shows that unit factor input
requirements are a function of :
1. IPRs of the destination country, d ,
d
wf
2. relative factor prices in the destination country, d,
wz
ed
jz
3. relative technology expenditures, ed
,
jf
4. factor endowments of technology suppliers, V o , and
3
d o
Nf (d ,Vf ) n(1)
Qd also depends on
jf 0
”f dn.
11
5. distance to technology suppliers, do,d .
oD
Finally, note that if pairs of technologytrading countries emerge depend
ing on bilateral distances, and if there is a finite number of groups, then we
can cluster countries across a finite number of technological regimes. Two
countries belong to the same regime if they adopt the same technologies.
This implication emerges in our model because the number of countries, fac
tors, sectors, and institutional frameworks is finite, and because there is a
fixed cost of exporting technology.4
4.3 The economy
To analyze how technology choices affect the impact of factor endowments on
trade, we solve the equilibrium for the aggregate economy. Employing matrix
notation, we define Qc as the matrix of unit factor input requirements for
economy c. Market clearing conditions in this economy are as follows:
Qc Yc = V c , (15)
where Yc is the vector of sectoral outputs and Vc is the vector of factor
endowments. Assuming that the number of goods is equal to the number
of products, and denoting by Rc the inverse of matrix Qc , it is possible
to express output of country c as a linear function of country c's factor
endowments. Specifically,
Yc = R c Vc . (16)
From the previous section, we know that in equilibrium there will be a finite
number of technological groups. We let the data inform us about the par
ticular number. However, in order to study the implications of technology
4
Another mechanism that would group countries into different technological regimes,
which could equal the number of countries, is the existence of transport costs for ma
chines, which would yield machineprice differences across economies, thus affecting their
technology adoption decisions.
12
choices on the pattern of specialization, we assume that countries are clus
tered in K groups. Output of country c, which belongs to group k, Yc,k , with
k = 1, ..., K, and worldwide output, Yw , can be written as follows:
Yc,k = Rk Vc,k (17)
and
K
w
Y = Rk Vw,k , (18)
k=1
respectively. Vw,k is the vector of factor endowments of group k. Denoting
by T B c the trade balance of country c and by sc country c's share of world
consumption, net exports of this economy can be written as follows:
K
NXc = Yc  sc Yw = Rk (Vc,k  sc Vw,k )  Rz sc Vw,z . (19)
z=1,z=k
The previous system provides the following estimating equation for the net
exports of country c in sector j, where c belongs to technology group k :
F K F
c,k
N Xj = rf j (Vfc,k
k
 sc Vfk ) + z
rf j (sc Vfz ) . (20)
f =1 z=1,z=k f =1
standardef f ect consumptionef f ect
Equation (20) relates netexports of product j in country c, which belong to
technology group k, with measures of relative abundance of factors f with
f = 1, ..., F in country c and a pure consumption effect, which captures the
impact of importing product j from countries that belong to other technolog
k
ical groups. The rf j s are the analogue to the Rybczynski coefficients in the
standard theory. However, in our model, the concept of relative abundance
of a factor in a country is redefined, so that a country's endowments are
compared to the endowments of the technological group to which it belongs
instead of being compared to the world's endowments, as in the standard
theory. Adding and subtracting K z=1,z=k
F k z
f =1 rf j (sc Vf ) to equation (20),
c,k
we can rewrite N Xj as a function of an endowment and a technology effect:
13
F K F
c,k
N Xj = rf j (Vfc,k  sc Vfw ) +
k k z
(rf j  rf j )sc Vfz , (21)
f =1 z=1,z=k f =1
endowmentef f ect technologyef f ect
where Vfw stands for the world's endowment of factor f. Consistent with re
cent literature on comparative and absolute advantage, equation (21) implies
that the pattern of trade is determined by both relative endowments as well
as relative factor productivity.
5 Empirical Strategy
This section presents the empirics. The analyses focus on the computer
industry, which has received a lot of attention in the technology adoption
and growth literature. The empirical approach begins with the estimation
of the neoclassical Rybczynski equation with a single technological regime.
In turn, we discuss results of adhoc tworegime models, where the data are
divided into two groups depending on rankings based on technology selection
variables, namely factor endowments, IPRs, TFP, and relative factor costs.
The rest of this section describes our preferred twostep estimator, which
includes a multivariate technology selection equation.
5.1 The twostep approach
The theoretical framework motivates an empirical model which consists of
two equations as net exports are governed by different sets of parameters,
and the set of parameters which determine a particular country's net exports
depend on the technological group to which the country belongs.
The most efficient method to estimate this model is the FullInformation
Maximum Likelihood (FIML) estimator (see Chiburis and Lokshin[7]). How
ever, we employ the least efficient method, the TwoStep approach, as it
performs better than the FIML with small samples. A relevant implication
of relying on the TwoStep approach to test our model is that the procedure
increases the chance of rejecting the theory, as it delivers wider confidence in
tervals for the estimated parameters. This implies that if we find evidence in
14
line with our predictions, then our theory is very robust. However, evidence
against the theoretical results may not be enough to reject the theory.
In the first step we estimate an OrderedProbit equation and we cluster
countries across technological groups as motivated by the theoretical model.
To do so, we construct an index of technology choices based on the theory,
and we estimate the locations of the cutoff points at which the sample splits
across technological regimes.
To estimate the cutoff points, we first assume that the sample splits in a
particular number of groups e.g., 2, 3, or 4, and we estimate the model with
the assumed number of regimes. To determine the optimal cutoff points, we
follow Hotchkiss[19] and estimate each model for every reasonable cutoff.5
Given such values, in the second step, we estimate the Rybczynski coefficients
for each technological group. For such purpose, we employ the OLS approach
but we control for selection.6 Finally, we apply the goodness of fit criterium
to identify the set of estimated parameters that best fits the data.
The firststep selection equation can be written as:
c
Rt = Ztc + ”c
t (22)
c
0 if  < Rt R1t
c
1 if R1t < Rt R2t
.
~c
Rt =
.
.
c
K  1 if RK1t < Rt ,
5
We start by dividing the sample in a way that delivers the maximum number of groups
with no more than 25% of the observations per group. This provides the highest degree of
freedom to move the cutoff points along the range of possible values. The cutoff points are
moved iteratively in steps of 1 percentile of the continuous variable we employ to cluster
countries across technological regimes.
^ c ^ c
6
Specifically, we introduce the estimated c (Rk R )(Rk+1 R ) as an explanatory
^
i ^ ^
(Rjk+1 Rc )(Rk Rc )
variable of the Rybczynski equation corresponding to regime i.
15
c
where Rt is the continuous variable that clusters countries in technological
regimes.7 is a vector of parameters and Ztc is the vector of the variables
c
used to estimate the composite index Rt .8 ”c is a standard normal shock,
t
and R1t , R2t , ..., RK1t are the unknown cutoff points, which satisfy the
following condition: R1t < R2t <, ...,< RK1t . We also define R0t  and
RKt to avoid having to handle the boundary cases separately.
The resulting secondstage Rybczynski equations are:
F F
N Xtc,k = r0 + rf (Vfc,k  sc Vfkt ) +
k
t
z c,k
rf (sc Vfzt ) + t (23)
f =1 f =1,z=k
N Xtc,k0 if ~c
Rt = 0
c,k1 ~c
N Xt if Rt = 1
.
N Xtc =
.
.
N Xtc,K1 if ~c
Rt = K  1,
where N Xtc,k are net exports of computers for country c, which belongs to
k
group k, in period t. Parameter rf is the Rybczynski coefficient correspond
ing to factor f in technology group k. We include four factors of production:
stock of capital, skilled labor, unskilled labor, and arable land. Following
Fitzgeral and Hallak[13], Harrigan[16], and Reeding[29] we interpret he con
stant term, r0 , as the mean effect of omitted factors. Finally, our model relies
c,k 2
on the following assumptions: A1. t N (0, ,k ), for k = 1, ..., K; A2.
2 2
”c N (0, 1); A3. ,kz = 0, for k = z and k, z = 1, ..., K; A4. ,” = 0.
t
7
Section 5.2 explains the methodology, the variables, and the economics of the index
variable.
8
Our baseline model includes variables that are strictly related to technology adoption
such as IPRs of each country, capital/labor ratio of each country, which we use to proxy
ed
jk
ed
, and weighted averages of the same variables for technology trading partners.
jl
16
5.2 Indicators and proxies
This section describes the empirical proxies we employ to estimate equations
(22) and (23). It also documents the sources of data.
5.2.1 The technology selection variable
As mentioned, our model suggests that countries face discretely different
c
technological choice sets. Therefore, to construct variable Rt we rely on the
theory, according to which the key determinants of the technological group
to which a country belongs are IPRs of the destination country, d , relative
wd
factor prices of the destination country, wf , relative technology expenditures,
d
z
ed
jz
ed
, factor endowments of technology trading partners, V o , and distance to
jf
technology suppliers, do,d .
oD
Our baseline model for the selection equation considers variables strictly
related to technology adoption such as IPRs of the destination country, rel
ed
jz
ative factor endowments (capital/labor), which we use to proxy ed , and
jf
the same variables for technology trading partners. The latter variables are
weighted by the inverse of the distance between trading countries. To test the
robustness of our specification, we add factor price ratio, namely the ratio of
the manufacturing wages over bank lending interest rates, and national TFP
c
levels to control for Hicksneutral technological differences. Our proxy for Rt
is the first component in the principal component analysis of the variables
c
employed to construct variable Rt .
5.2.2 Data
Factor endowments
o
Data on capital stocks come from Serven and CalderŽn[33], who extend the
series provided by the Penn World Tables. The labor force is from the In
ternational Labor Organization (ILO), and it refers to economically active
population defined as the 2564 age group. To calculate endowments of high
and lowskilled labor, we use data on educational attainment from Barro
and Lee[2]. Skilled workers are defined as the population economically active
with at least one year of secondary school. The rest are considered unskilled
17
labor. The endowment of arable land comes from the World Bank's World
Tables and it is defined as hectares of arable land.
IPRs
Data on intellectual property rights protection come from Ginarte and Park
[28]. The measure is an index of patent rights at the country level, which
is based on the following categories: extent of patent coverage, membership
in international patent agreements, provisions for loss of protection, enforce
ment mechanisms, and duration of protection. Each of these categories is
scored from 0 to 1. The unweighted sum of these five values constitutes the
overall value of the IPRs index.
Net exports of computers
Bilateral data on imports and exports of computers come from Feenstra et
al.[12]. The data are available at the 4digit level of the Standard Interna
tional Trade Classification, Revision 2. To measure net exports of computers
for the global industry, we consider the following categories, 7521, 7522, 7523,
and 7528, which are the same as the ones employed by Caselli and Coleman[6]
to study the determinants of crosscountry technology diffusion. Code 7521
refers to Analogue and hybrid data processing machines; code 7522 refers to
Complete digital data processing machines, comprising in the same housing
the central processing unit and one output unit; code 7523 refers to Complete
digital central processing units, digital processors consisting of arithmetical,
logical, and control elements; codes 7528 refers to Offline data processing
equipment, n.e.s. To measure net exports of the computers in the final good
industry we restrict our analysis to the 7521 and 7522 codes.
Wages
Data on manufacturing wages come from Nicita and Olarreaga[26]. The wage
variable includes all payments in cash or in kind paid to employees during
the reference year in relation to work done for the establishment. Payments
include direct wages and salaries, remuneration for time not worked, bonuses
and gratuities, housing allowances and family allowances paid directly by
the employer, and payments in kind. Excluded are employer socialsecurity
contributions on behalf of their employees, pension and insurance schemes, as
18
well as the benefits received by employees under these schemes, and severance
and termination pay. Our proxy is the average of industry wages over a five
year period.
Lending rates
Data on lending interest rates, a proxy for the cost of capital, come from
the International Financial Statistics dataset of the IMF. The measure is
defined as the annual average of the national lending rates.
TFP
Data on total factor productivity (TFP) has been obtained from Klenow and
RodriguezClare[21], who estimate TFP by substracting estimates of human
and physical capital per worker from GDP per worker.
The resulting sample covers 73 developing and developed countries over
the period 19802000. Table 1 presents the summary statistics.
[Insert Table 1 about here]
5.3 Descriptive analysis
Table 2 presents the list of countries that are located at the top and the
bottom of the distribution of countries ranked according to their net exports
of computers in 2000. For these countries the table reports their net exports
of computers, their capital/labor ratios, their skilledlabor/labor ratios, and
the positions the countries occupy in the rankings for each of these variables.
Each ranking ranges from 173.
[Insert Table 2 about here]
To assess if the data supports our theory, we compare the positions coun
tries occupy in the net exports and relative factor endowments distributions.
According to the standard theory, if the production of computers is capi
tal (skilled labor) intensive, we should expect to observe countries that are
relatively more abundant in this factor to be located at the top of the net
exports of computers distribution.
19
Interestingly, the data in Table 2 seem remarkably far from the predic
tions of the neoclassical theory. For example, among the set of capital
abundant countries, there are countries such as Korea Republic, Singapore,
and Japan, which are among the top net exporters of computers, and others
such as Switzerland, U.S.A, Italy, and France, which are at the bottom of
the netexports distribution. Skilledlabor abundant countries such as Ko
rea Republic and Japan are at the top of the netexports distribution, while
other skilledlabor abundant countries such as U.S.A, Sweden, Canada, and
Australia are located at the bottom.
A similar pattern is also observed in the finalgoods computer industry.
Among capital intensive countries, we find Singapore and Japan, which are
among the highest net exporters, and other countries such as Switzerland, the
U.S.A, Italy and France that are among the highest netimporters. Overall,
the data shows evidence that contradicts the standard theory. We devote
the following sections to explore this question in detail.
6 Results
The discussion of econometric results proceeds in stages. We first discuss the
model as the standard factorproportions theory. We also present the results
of the estimation of the model for various subsamples of the data, which
are split at the median of potential technologyselection variables. These
selection variables are: (a) capital/labor ratio, (b) IPRs, (c) wage/lending
rate ratio, and (d) TFP. In turn, we report the results from the estimation
of the selection equation of the optimal 2regime, 3regime, and 4regime
models, followed by a discussion of the estimated Rybczynski coefficients of
the model that best fits the data. Formal tests of the null hypothesis that the
Rybczynski coefficients are equivalent across regimes are also discussed. At
the end we discuss robustness tests, which entail the estimation of the two
step approach with additional explanatory variables (namely relative factor
costs and national TFP differences) in the selection equation.
20
6.1 Results for the standard theory
Table 3 presents the estimated Rybczynski coefficients under the assumption
that all countries employ the same technology. The table shows that the
model is unsatisfactory, as none of the explanatory variables are statistically
significant, both in the global and finalgoods computer industries.
[Insert Table 3 about here]
Consistent with Hakura[15], the results improve when we estimate the
model for different subsamples. Table 4 shows the estimations for various
samples, depending on the selection variables.
[Insert Table 4 about here]
Two conclusions can be drawn from Table 4. First, the division of the
sample according to technologyselection variables improves substantially
econometric estimates. Second, there is important variation in the sign
and statistical significance of the explanatory variables across subsamples.
For example, capital abundance is a source of comparative advantage in the
production of computers and components for countries that are below the
median of the capital/labor ratio, IPRs, and TFP, while it is a source of
comparative disadvantage for countries above the median of the variables.
Unskilledlabor abundance is a source of comparative advantage for coun
tries above the median of the capital/labor ratio and IPRs, while it is a
source of comparative disadvantage for countries below the median. That is,
there seems to be a notable technologyselection mechanism, which appears
to be related to endowments, IPRs, and national TFP differences. The two
step estimations discussed below improve upon these estimations by allowing
for a multivariate selection mechanism.
6.2 Results for the twostep approach
This section presents the results from the implementation of the twostep
approach. We discuss the results from the estimation of the selection equa
tion, followed by the results from the estimation of the Rybczynski equations
21
for each technological group. We also test the null hypotheses that the Ry
bczynski coefficients are equivalent across these groups.
6.2.1 Selection equation
Table 5 shows the results of the estimation of the selection equations for the
optimal 2regime, 3regime, and 4regime models. The dependent variable is
the technology index and the regressors include own capital over labor, own
IPRs, trading partners' capital/labor ratio, and trading partners' IPRs.
[Insert Table 5 about here]
The own capital/labor ratio, the own IPRs, and the trading partners'
IPRs variables are statistically significant at the 1% level in most of the
models, for both the global and finalgoods computer industries. The latent
index rises with these variables, a result that appears in all specifications. It
is noteworthy that the significance of a country's own endowments is con
sistent with Schott's multiplecones of specialization. In contrast, the sig
nificance of IPRs and trading partner characteristics are new results for the
trade literature and lend credence to our theoretical model with endogenous
technology adoption. However, the effect of trading partners' capital/labor
ratio is ambiguous. Its estimated coefficient is significant and positive only
in the 3regime model and for the global computer industry, but it is signif
icant and negative in the other cases. The models that best fit, those with
the lowest sum of squared residuals (SSR), have three or four technologi
cal regimes, for the global computer industry and the finalgoods computer
industry, respectively.
Table 6 presents specification tests for the optimal models. The first tests
the significance of the cutoff points or threshold values of the latent index,
which split the samples into technological regimes. These cutoff points are
statistically different from each other in both industries, as reflected in their
confidence intervals that do not overlap.
Although it is not a test of the validity of the theory, the significance of
the inverse of Mills Ratio in the second regime of the finalgoods computer
industry estimates suggests that the lack of control for technology choices
22
delivers selectionbias in the estimated Rybczynski coefficients. This evidence
of biased coefficients is broadly consistent with Fitzgerald and Hallak[13],
who found that Rybczynski coefficients tend to be biased when crosscountry
productivity differences are ignored.
[Insert Table 6 about here]
6.2.2 Rybczynski equations
Table 7 presents estimated Rybczynski coefficients for each technological
regime. In the global computer industry, capital abundance is a source of
comparative advantage for countries that belong to the lowest and middle
regimes. However, it is a source of disadvantage for countries in the highest
regime. The coefficients are statistically significant at the 1% level. Evidence
in line with the first result has also been provided by Harrigan[16], David and
Weinstein[8], Bernstein and Weinstein[4], and Leamer[24]. Evidence related
to the second result has been documented by Harrigan and Zakrajsek[18].
The authors do not find systematically positive coefficients on capital for
most manufacturing sectors.
[Insert Table 7 about here]
Skilled labor abundance increases net exports of computers in the low
est and highest regimes. This result is consistent with Harrigan and
Zakrajsek[18], who find that educated workers have a strongly positive effect
on the production of electrical machinery sectors. By contrast, skilled labor
reduces net exports of computers in the middle regime. Unskilled labor has a
positive and statistically significant impact on the net exports of computers
of the highest regime, but a significant and negative effect on the production
of the lowest regime. The last finding contradicts Harrigan[16], who observes
that unskilled labor is a source of comparative advantage in most industries.
The impact of land also varies across regimes. It is significant and positive
in the lowest regime, and significant and negative in the other regimes.
In the finalgoods computer industry, the qualitative effects of skilled
labor and land resemble those of the global computer sector. However, capital
23
is a source of comparative disadvantage for countries that belong to the lowest
regime, and unskilled labor is statistically insignificant across regimes.
Overall, the findings are consistent with Schott[33], who documents het
erogenous impact of factor endowments on within industry's output across
countries. One limitation of Schott's[33] analysis is that it does not jointly
control for variation in intraindustry product mix and technology differences
across countries. The ongoing analysis fills this gap and provides evidence
consistent with Schott's findings.
6.2.3 Are Rybczynski coefficients equivalent across regimes?
Having presented preliminary evidence in line with our theory, we now dis
cuss a formal test of the null hypothesis that the Rybczynski coefficients are
equivalent across regimes. Table 8 reports the pvalues corresponding to the
null hypothesis that the Rybczynski coefficients of the regimes in brackets
are statistically equivalent. The Table shows that in spite of the fact that
we employ the least efficient method to estimate the model, which delivers
wider confidence intervals for the estimated parameters, there is substantial
evidence supporting the theory. The null hypotheses are rejected at the 1%
level for many cases in both industries.
[Insert Table 8 about here]
6.3 Robustness checks
It may be argued, however, that the Rybczynski coefficients vary across coun
tries not because of technology adoption, but as a result of differences in
relative factor prices. They may also differ because the quality of endow
ments varies across countries, or because there are Hicksneutral technology
differences across economies, as in Fitzgerald and Hallak[13]. That is, these
variables could be correlated with our selectionequation regressors, our pre
vious results could suffer from omitted variables bias, and the estimated
heterogeneous Rybczynski coefficients could be due to these other factors.
Two additional specifications test the robustness of our results. The first
24
adds the wage/lending rate ratio to the set of explanatory variables in the
selection equation. The second adds national TFP levels to the previous set
of regressors. Table 9 reports these results.
[Insert Table 9 about here]
Relative factor prices appear insignificant, and thereby play no role in
explaining the variation of the Rybczynski coefficients across technological
regimes. In contrast, TFP is significant and has a positive effect on the latent
selection variable. Yet the sign and statistical significance of the regressors
of the baseline model remain intact. Furthermore, the magnitudes of the
coefficients related to our theory, namely a country's own capital/labor ratio
and IPRs, are larger than in the baseline estimation. This suggests that
omitted variables bias had attenuated the estimated effect of our technology
selection regressors.
With the expanded specification, the optimal models for both industries
have four technological regimes. The specification tests corresponding to the
complete model are reported in Table 10. In all but one of the regimes, the
inverse Mills ratio is statistically insignificant. Also, there is some overlap in
the estimates of the 95 percent confidence intervals of the first and second
cutoff points in both industries. Again, it is worth clarifying that these tests
are not require to validate our proposed theory, the findings of more than one
regime with heterogeneous Rybczynski coefficients is sufficient to support the
proposed model.
[Insert Table 10 about here]
The results from the estimation of the Rybczynski equations and the for
mal tests of equivalence of these coefficients across regimes appear in Tables
11 and 12. Once again the findings support our theory, and the Rybczynski
coefficients follow the same patterns as in Table 7.
[Insert Tables 11 and 12 about here]
25
7 Conclusion
The neoclassical model of trade predicts that international specialization
will be jointly determined by crosscountry differences in relative factor en
dowments and exogenous technologies. Our proposed model relaxes the
Hicksneutral technological differences assumption by allowing countries to
adopt different technologies. The marriage of literatures on biased technical
change and trade yielded a tractable theory, whereby differences in factor
endowments and intellectual property rights bias technical change towards
particular factors, and thus unit factor input requirements can vary across
economies.
We tested this theoretical model with data on net exports of a single in
dustry, computers, intellectual property rights, factor endowments, and other
controls for 73 countries over the period 19802000. The descriptive and
econometric results provide robust evidence suggesting that once technologi
cal choices are considered, countries exhibit different Rybczynski coefficients.
This is partly due to differences in factor endowments, as in Schott's multiple
cone model of international specialization with identical technologies across
countries. But the evidence also indicates that differences in intellectual
property rights and the characteristics of technology trading partners, which
also determine technologyadoption choices in our model but not in Shott's
theory, are associated with differences in factor intensities across countries.
References
[1] Acemoglu, D., 2002. Directed Technical Change. Review of Economic
Studies, 7 (4), 781809.
[2] Barro, R. and L., JongWha, 1993. International Comparisons of Edu
cational Attainment. Journal of Monetary Economics, 32 (3) 363394.
[3] Bernanke, B.S. and R.S., Gurkaynak, 2002. Is Growth Exogenous? Tak
ing Mankiw, Romer, and Weil Seriously, B.S. Bernanke and K. Ro
goff (eds.) NBER Macroeconomics Annual 2001 (Cambridge, MA: MIT
Press), 1157.
26
[4] Bernstein, J. and D., Weinstein, 2002. Do Endowments Determine the
Location of Production? Evidence from National and International
Data. Journal of International Economics, 56 (1), 5576.
o
[5] CalderŽn, C., and L, Serven, 2004. Trends in Infrastructure in Latin
America, 19802001. World Bank Policy Research Working Paper 3401,
Washington, DC.
[6] Caselli, F. and J., Coleman, 2001. CrossCountry Technology Diffusion:
The Case of Computers. American Economic Review P & P. 91 (2),
328335.
[7] Chiburis, R. and M., Lokshin, 2007. Maximum Likelihood and TwoStep
Estimation of an Orderedprobit Selection Model. The Stata Journal. 7
(2), 167182.
[8] Davis, D. and D., Weinstein, 2001. An Account of Global Factor Trade.
American Economic Review, 91 (5),142354.
[9] Dobbelaere, S. and J., Mairesse, 2008. Panel Data Estimates of the Pro
duction Function and Product and Labor Market Imperfections. NBER
Working Paper N. 13975.
[10] Drandakis, E. and E., Phelps, 1965. A Model of Induced Invention,
Growth and Distribution. Economic Journal, 76, 82384.
[11] Elias, V., 1992. Sources of Growth: A Study of Seven Latin American
Economies. San Francisco: ICS Press.
[12] Feenstra, R., Lipsey, R. and H., Bowen, 1997. World Trade Flows, 1970
1992, with Production and Tariff Data. NBER WP 5975.
[13] Fitzgerald, D., and J.C., Hallak, 2004. Specialization, Capital Accu
mulation, and Development. Journal of International Economics, 64(2),
277302.
[14] Gollin, D., 2002. Getting Income Shares Right. Journal of Political Econ
omy, 90, 458474.
27
[15] Hakura, D., 2001. Why does HOV fail? The role of technological differ
ences within the EC. Journal of International Economics, 56 (2), 509
511.
[16] Harrigan, J., 1995. Factor Endowments and the International Location
of Production: Econometric Evidence from the OECD, 19701985. Jour
nal of International Economics, 39 (1/2), 123141.
[17] Harrigan, J., 1997. Technology, Factor Supplies and International Spe
cialization: Testing the Neoclassical Model. American Economic Review,
87 (4), 475494.
[18] Harrigan, J. and E. Zakrajsek, 2000. Factor Supplies and Specialization
in the World Economy. NBER Working Paper 7848.
[19] Hotchkiss, J., 1991. The Definition of PartTime Employment: A
Switching Regression Model with Unknown Sample Selection. Interna
tional Economic Review, 32 (4), 899917.
[20] Kennedy, C., 1964. Induced Bias in Innovation and the Theory of Dis
tribution. Economic Journal, LXXIV, 541547.
[21] Klenow, P. and A. RodriguezClaire, 2005. Externalities and Growth.
Handbook of Economic Growth, vol. 1A, P. Aghion and S Durlauf, eds.
Chapter 11, 817861.
[22] Krugman, P., 1980. Scale Economies, Product Differentiation, and the
Pattern of Trade. American Economic Review, 70 (5), 950959.
[23] Krugman, P. and M. Obstfeld, 1994. International Economics: Theory
and Practice. Third edition, pp.6465.
[24] Leamer, E., 1984. The Commodity Composition of International Trade
in Manufactures: An Empirical Analysis. Oxford Economic Papers, 26,
350374.
[25] Leontief, W., 1956. Factor Proportions and the Structure of American
Trade: Further Theoretical and Empirical Analysis. Review of Eco
nomics and Statistics, 38, 386407.
28
[26] Nicita, A. and M. Olarreaga, 2007. Trade, Production and Protection
19762004. World Bank Economic Review 82(1), 165171.
[27] Ortega, D. and F., Rodriguez, 2003. Are Capital Shares Higher in Poor
Countries?. Working Paper.
[28] Park, W. and J.C., Ginarte, 1997. Determinants of Patent Rights: A
Cross National Study. Research Policy N26 (3), 283301.
[29] Redding, S., 2002. Specialization Dynamics. Journal of International
Economics, 58, 299334.
[30] Reppeto, A. and J. Ventura, 1997. The Leontief.Trefler Hypothesis and
Factor Price Insensitivity, Working papers 97113, Massachusetts Insti
tute of Technology (MIT), Department of Economics.
[31] Romalis, J., 2004. Factor Proportions and the Structure of Commodity
Trade. American Economic Review, 94 (1), 6797.
[32] Samuelson, P., 1965. A Theory of Induced Innovations Along Kennedy
Weisacker Lines. Review of Economics and Statistics, XLVII, 444464.
[33] Schott, P., 2003. One Size Fits All? Theory, Evidence and Implications
of Cones of Diversification. American Economic Review, 93 (3), 686708.
[34] Young, A. 1995. The Tyranny of Numbers: Confronting the Statistical
Realities of the East Asian Growth Experience. Quarterly Journal of
Economics, 100, 64180.
29
8 Appendix
Proof first stage of FGP's problem
Dividing the first order condition for variety i and n, we obtain:
Ac jz c(1 )
pc (i)
jz ac (i)
jz
ajz (i)
= Ac = c(1 )
(24)
pc (n)
jz
jz
ajz (n)
ac (n)
jz
Multiplying both size of equation (24) by pc (i), and then integrating over i,
jz
we obtain the following solution:

ec pc
ac (i)
jz = z c1 ,
jz
(25)
Pjz
NzW
where ec
z 0 pc (i)ac (i)di.
jz jz
30
Table 1. Summary statistics
Variable obs. mean std. dev min max
Net exports of computers 365 16952.93 2577342 31100000 13200000
Net exports of computers (excluding components) 365 5425236 4.24E+08 4.16E+09 2.88E+09
Stock of capital 365 7.94E+11 2.16E+12 1.58E+09 2.13E+13
Skilled labor 365 7685.693 26463.59 14.19536 258038.5
Unskilled labor 365 13873.19 50602.99 65.86906 413936.7
Land 365 13000000 31900000 1000 1.89E+08
Wages 365 9.629792 9.496766 0.2007 59.1211
Lending rate 365 54.8177 253.0469 117.4739 4774.53
TFP 365 10255.21 2983.809 2570 18795
IPRs 365 2.303616 1.24133 0 4.875
Note: T his table reports summary statistics of the variables employed for the estimation of the twostep model.
Table 2: Netexports of computers and factor endowments
Net exports of Skilled
Industry Country computers (XM) Ranking Capital/Labor Ranking Labor/Labor Ranking
China 1.24E+07 1 1.45E+07 51 38.4 37
Malaysia 1.18E+07 2 5.76E+07 27 50.5 25
Singapore 1.05E+07 3 2.03E+08 3 59.1 17
Korea Rep. 9187286 4 2.42E+08 1 75.3 5
Philippines 6350562 5 1.61E+07 48 53.6 23
Ireland 5953102 6 1.04E+08 21 64.1 15
Japan 5000000 7 1.85E+08 5 71.9 8
Mexico 4675278 8 4.48E+07 29 40.3 36
Indonesia 2329506 9 1.61E+07 49 26.8 50
Global computer industry India 33958 10 7649168 58 22.2 56
(finalgoods and components) Denmark 1196473 64 1.44E+08 12 68.1 12
U.K. 1200000 65 1.11E+08 20 58.2 18
Sweden 1592865 66 1.32E+08 16 80.3 3
Spain 1613921 67 1.13E+08 18 46.9 30
Switzerland 2773254 68 2.03E+08 2 71 9
Australia 3062108 69 1.48E+08 10 73.4 6
France 3942278 70 1.52E+08 9 55.7 20
Italy 4117605 71 1.53E+08 8 46.7 31
Canada 5744931 72 1.40E+08 14 79.6 4
U.S.A 3.11E+07 73 1.60E+08 7 89.7 1
Note: T his table presents the countries at the top and bottom of the distribution of netexports of computers. For each of these countries the
table reports their netexports, capital/labor ratio and skilledlabor/labor ratio.
Table 2: Netexports of computers and factor endowments (Cont'd)
Net exports of Skilled
Industry Country computers (XM) Ranking Capital/Labor Ranking Labor/Labor Ranking
Mexico 2.60E+09 1 4.48E+07 29 40.3 36
Ireland 1.62E+09 2 1.04E+08 21 64.1 15
Malaysia 1.05E+09 3 5.76E+07 27 50.5 26
Japan 4.20E+08 4 1.85E+08 4 71.9 8
China 2.27E+08 5 1.45E+07 51 38.4 37
Singapore 68000000 6 2.03E+08 2 59.1 17
Indonesia 48000000 7 1.61E+07 48 26.8 50
Netherlands 30000000 8 1.43E+08 13 67.4 14
Philippines 18700000 9 1.61E+07 49 53.6 23
Finalgoods computer Turkey 2.55E+08 63 3.11E+07 36 22.3 55
industry Denmark 2.61E+08 64 1.44E+08 12 68.1 12
Spain 3.52E+08 65 1.13E+08 18 46.9 30
Sweden 4.07E+08 66 1.32E+08 16 80.3 3
Switzerland 4.36E+08 67 2.03E+08 3 71 9
Australia 5.83E+08 68 1.48E+08 10 73.4 6
Italy 8.55E+08 69 1.53E+08 8 46.7 31
UK 9.60E+08 70 1.11E+08 20 58.2 18
France 1.22E+09 71 1.52E+08 9 55.7 20
Canada 1.23E+09 72 1.40E+08 14 79.6 4
USA 4.16E+09 73 1.60E+08 7 89.7 1
Note: T his table presents the countries at the top and bottom of the distribution of netexports of computers. For each of these countries the
table reports their netexports, capital/labor ratio and skilledlabor/labor ratio.
Table 3. Neoclassical Rybczynski equations
Industry Explanatory variables Netexports of computers
Capital abundance 2.74E07
[8.86E07]
Skilledlabor abundance 3.63E+01
[5.85E+01]
Global computer industry (final Unskilledlabor abundance 5.96E+00
goods and components) [2.80E+1]
Land abundance 2.72E02
[3.19E02]
Constant 1.26E+05
[1.91E+05]
Capital abundance 3.19E05
[1.26E04]
Skilledlabor abundance 4.71E+03
[8.76E+03]
Unskilledlabor abundance 1.65E+03
Finalgoods computer industry
[4.23E+03]
Land abundance 2.43E01
[4.81+00]
Constant 1.80E+07
[1.87E+07]
Note: T his table shows the results of neoclassical Rybczynski equations, for the global computer industry
(final goods and components) and for the final good computer industry. T he dependent variables are net
exports of computers for each industry. T he independent variables are capital, skilled labor, unskilled labor
and land. T he results control for time effects. Robust standard errors are reported in brackets. *** means
statistically significant at the 1% level, ** 5%, and * 10%.
Table 4. Rybczynski equations for different groups of countries
K/L ratio IPRs Wage/Lending rate TFP
Net exports of computers
Industry Below median Above media Below median Above media Below median Above media Below median Above media
Capital abundance 1.96E06 2.34E06 1.51E06 2.18E06 4.63E07 1.14E06 7.72E07 3.70E06
[3.53E07]*** [1.74E06] [5.51E07]*** [1.61E06]*** [5.48E07] [1.47E06] [7.95E08]*** [1.21E06]***
Skilledlabor abundance 2.01E+01 4.64E+02 8.77E+00 4.38E+02 6.40E+01 2.65E+02 1.42E+01 4.78E+02
[1.99E+01] [2.67E+02]* [2.23E+01] [2.42E+02]* [1.78E+01] [2.06E+02] [6.11E+00]** [1.67E+02]***
Global computer Unskilledlabor
industry (final abundance
4.65E+00 1.40E+02 1.14E+00 1.17E+02 2.18E+01 3.96E+01 4.70E+00 1.74E+02
goods and
[8.64E+00] [6.77E+01]** [7.79E+00] [6.63E+01]* [1.02E+01]** [4.84E+01] [4.95E+00] [1.21E+02]
components)
Land abundance 6.42E03 1.17E01 1.56E02 1.12E01 1.45E02 8.99E02 9.23E03 2.42E02
[8.90E03] [4.30E02]*** [1.29E02] [4.42E03]** [1.02E02] [3.65E02]** [8.03E03] [3.31E02]
Constant 2.91E+03 2.97E+05 4.81E+04 [3.51E+04] 2.11E+04 4.92E+05 1.50E+05 7.69E+04
[3.35E+04] [3.46E+05] 1.74E+05 [2.72E+05] [8.07E+04] [4.39E+05] [1.22E+05] [1.22E+05]
Capital abundance 3.30E05 5.62E05 2.97E05 3.78E04 1.33E04 1.68E04 1.29E04 5.59E04
[1.26E06]*** [1.83E05]*** [2.12E05] [2.26E04]* [5.18E05]*** [2.07E04] [7.07E06]*** [1.50E04]***
Skilledlabor abundance 1.30E+03 1.98E+03 7.10E+02 7.93E+04 5.58E+03 4.19E+04 1.20E+03 7.75E+04
[1.11E+02] [8.80E+02]** [9.73E+02] [3.50E+04]** [1.01E+03]* [6.91E+03] [1.62E+03]*** [2.45E+04]***
Finalgoods Unskilledlabor
computer abundance 6.05E+01 5.02E+02 1.27E+02 1.82E+04 1.84E+03 7.54E+03 4.39E+03 1.54E+04
industry [9.87E+00]*** [4.70E+02] [3.97E+02] [1.16E+04] [1.83E+03]*** [3.01E+04] [1.39E+03] [1.71E+04]
Land abundance 1.41E01 6.20E01 4.89E01 1.55E+01 2.16E+00 9.55E+00 1.83E+00 1.09E+00
[4.2703]*** [5.37E01] [5.47E01] [6.40E+00]** [1.39E+00]* [5.45E+00]* [1.80E+00] [4.73E+00]
Constant 1.48E+06 4.12E+07 3.99E+05 1.64E+07 8.09E+05 6.73E+07 2.04E+07 2.24E+07
[1.46E+06] [4.76E+07] [1.52E+06] [4.50E+07] [1.93E+07] [4.48E+07] [1.59E+07] [4.32E+07]
Note: T his table shows the results of the Rybczynski equations for countries that are located below and above the median of capital/labor (K/L), intellectual property rights (IPRs), wage/lending
rate, and total factor productivity (T FP). T he dependent variables are net exports of computers in the global computer and the finalgood computer industries. T he independent variables are
capital, skilledlabor, unskilledlabor and land. T he results control for time effects. Robust standard errors are reported in brackets. *** means statistically significant at the 1% level, ** 5%,
and * 10%.
Table 5. Twostep approach. Estimation of the Selection Equation
Industry Technology Index 2regimes 3regimes 4regimes
Capital/Labor 6.85E08 8.27E08 1.03E07
[1.56E08]*** [1.01E08 ]*** [1.20E08]***
Country's
IPRs 2.38E+00 2.22E+00 2.36E+00
[0.6187 ]*** [0.2561]*** [0.28524]***
Global computer industry
Capital/Labor 2.10E08 5.14E+01 1.04E08
(finalgoods and components)
[ 1.00e08]*** [8.5543]*** [4.65e09]**
Technology Trading partners'
IPRs 4.02E+01 5.14E+01 3.55E+01
[1.23E+01]*** [8.5543]*** [6.4661]***
SSR 1.61E+15 1.69E+09 5.64E+14
Capital/Labor 1.38E07 8.16E08 7.36E08
[5.74E08]** [1.06E08 ]*** [7.16E09]***
Country's
IPRs 2.57E+00 2.07E+00 1.79E+00
[1.1474]** [0.30647]*** [ 0.19044]***
Finalgoods computer industry Capital/Labor 4.35E08 8.35E10 9.14E09
[2.23E08]** [5.19e09 ] [3.58e09]***
Technology Trading partners'
IPRs 7.73E+01 3.49E+01 2.95E+01
[32.586]** [5.9819]*** [4.0935]***
SSR 4.59E+19 4.32E+19 4.33E+19
Note: T his table present the results of the selection equation for the 2regime, 3regime, and 4regime models. T he dependent variable is categorical and
captures countries' technology choices. T he independent variables are capital/labor ratio and intellectual property rights protection (IPRs) of each
country as well as that of its technology trading partners (inversely weighted by bilateral distance). SSR means sum of squared residuals. Standard errors
are in brackets. *** means significant at the 1% level, ** 5%, and * 10%. T ime effects are not reported.
Table 6. Specification tests for the baseline model that best fits the data
Industry Test Test regime 1 regime 2 regime 3 regime 4
Cutoff_1 3.2591
Global computer industry [ 2.189, 4.329]*** 75592.68 126004.3 880412.4 n.a
Inverse Mills Ratio
(finalgoods and components) Cutoff_2 6.7228
[5.1487, 8.2968]*** [207762] [91009.17] [743991] n.a
Cutoff_1 2.192
[1.3846 , 2.9999]***
Cutoff_2 4.857 704011.5 12700000 62800000 89100000
Finalgoods computer industry Inverse Mills Ratio
[3.8125 , 5.9019]***
Cutoff_3 12.3037
[10.0106 , 14.5968]*** [3204522] [6210496]** [7.38e+07] [1.87E+08]
Note: T his table shows the estimated values of the technologycal index at which the sample splits across regimes (cutoff), together with their confidence
intervals. T he table also presents the estimated coefficients for the variable that controls for selection bias (Inverse Mills Ratio). Standard errors are
reported in brackets. *** means statistically significant at 1% level, ** 5%, and * 10%.
Table 7. Estimation of the Rybczynski equations for the optimal models
Industry Netexports of computers regime 1 regime 2 regime 3 regime 4
Capital abundance 1.90E06 1.66E06 1.95E06 n.a.
[7.33E07]*** [1.41E07 ]*** [ 4.06E07]*** n.a.
Skilledlabor abundance 5.53E+01 2.42E+01 4.05E+02 n.a.
[22.4488]** [12.6323]** [ 78.4266]*** n.a.
Global computer industry Unskilledlabor abundance 3.40E+01 1.44E+01 1.05E+02 n.a.
(finalgoods and components) [8.62937]*** [9.6125] [40.0186]*** n.a.
Land abundance 1.42E02 4.20E02 1.08E01 n.a.
[0.00678]** [0.0110]*** [0.01953]*** n.a.
Constant 4.81E+04 9.33E+03 1.03E+06 n.a.
[133791] [133373] [ 750228.6] n.a.
Capital abundance 7.60E05 5.33E05 3.61E04 3.71E04
[0.00001]*** [8.47E06]*** [0.00019]* [0.00010]***
Skilledlabor abundance 1.04E+03 1.74E+03 7.67E+04 8.56E+04
[360.2037]*** [772.3358 ]** [19946.58]*** [19225.73]**
Finalgoods computer Unskilledlabor abundance 4.68E+01 7.25E+02 9.11E+03 5.19E+03
industry [138.156] [588.9245] [9268.00] [ 17159.76 ]
Land abundance 3.53E01 1.67E+00 1.73E+01 1.61E+01
[0.1087]** [0.6735]** [5.5219]*** [5.1654]***
Constant 1.06E+06 7.86E+06 1.98E+08 9.75E+08
[2157942] [8503779]*** [1.01E+08]** [3.68e+08]**
Note: T his table shows the results of the Rybczynski equations for the 3regime model, for the global computer (final goods and
components) and finalgood computer industries. T he dependent variables are netexports of computers for each industry. T he independent
variables are capital, skilled labor, unskilled labor and land. T he results control for the "consumption effect" and time effects. Standard
errors are reported in brackets. *** means statistically significant at the 1% level, ** 5%, and * 10%.
Table 8. Are the Rybczynski coefficients equivalent across regimes?
Null Hypothesis pvalue
Capital abundance_[reg1=reg2] 0.7433
Capital abundance_[reg1=reg3] 0.000***
Capital abundance_[reg2=reg3] 0.000***
Skilledlabor abundance_[reg1=reg2] 0.002***
Skilledlabor abundance_[reg1=reg3] 0.000***
Skilledlabor abundance_[reg2=reg3] 0.000***
Unskilledlabor abundance_[reg1=reg2] 0.0002***
Global computer industry
Unskilledlabor abundance_[reg1=reg3] 0.0007***
(finalgoods and components)
Unskilledlabor abundance_[reg2=reg3] 0.0280**
Land abundance_[reg1=reg2] 0.000***
Land abundance_[reg1=reg3] 0.000***
Land abundance_[reg2=reg3] 0.0032***
Constant_[reg1=reg2] 0.7611
Constant_[reg1=reg3] 0.1988
Constant_[reg2=reg3] 0.1737
Note: T his table presents the pvalues corresponding to the null hypothesis of equivalence between
the Rybczynski coefficients of two different regimes in the 3regime model. T he brackets indicate the
regimes involves in each test. *** means significant at the 1% level, ** 5% , and * 10%.
Table 8. Are the Rybczynski coefficients equivalent across regimes? (Cont'd)
Capital abundance_[reg1=reg2] 0.7239
Capital abundance_[reg1=reg3] 0.9558
Capital abundance_[reg1=reg4] 0.000***
Capital abundance_[reg2=reg3] 0.8295
Capital abundance_[reg2=reg4] 0.000***
Capital abundance_[reg3=reg4] 0.0001***
Skilledlabor abundance_[reg1=reg2] 0.0024***
Skilledlabor abundance_[reg1=reg3] 0.2236
Skilledlabor abundance_[reg1=reg4] 0.0002***
Skilledlabor abundance_[reg2=reg3] 0.0391***
Skilledlabor abundance_[reg2=reg4] 0.000***
Skilledlabor abundance_[reg3=reg4] 0.0307***
Unskilledlabor abundance_[reg1=reg2] 0.0003***
Unskilledlabor abundance_[reg1=reg3] 0.9959
Unskilledlabor abundance_[reg1=reg4] 0.1264
Finalgoods computer industry
Unskilledlabor abundance_[reg2=reg3] 0.2906
Unskilledlabor abundance_[reg2=reg4] 0.2845
Unskilledlabor abundance_[reg3=reg4] 0.1576
Land abundance_[reg1=reg2] 0.000***
Land abundance_[reg1=reg3] 0.0002***
Land abundance_[reg1=reg4] 0.0001***
Land abundance_[reg2=reg3] 0.1019
Land abundance_[reg2=reg4] 0.036**
Land abundance_[reg3=reg4] 0.5776
Constant_[reg1=reg2] 0.8513
Constant_[reg1=reg3] 0.9175
Constant_[reg1=reg4] 0.0041**
Constant_[reg2=reg3] 0.9759
Constant_[reg2=reg4] 0.0039**
Constant_[reg3=reg4] 0.0046**
Note: T his table presents the pvalues corresponding to the null hypothesis of equivalence
between the Rybczynski coefficients of two different regimes in the 4regime model. T he brackets
indicate the regimes involved in each test. *** means statistically significant at the 1% level, **
5%, and * 10%.
Table 9. Robustness check: Selection Equation
Baseline model
Baseline model
Industry Dependent variable: Technology index Baseline model + factor prices
+ factor prices
+ TFP
Capital/Labor 8.27E08 8.37E08 8.75E08
[1.01E08 ]*** [1.09E08]*** [1.09E08]***
IPRs 2.22E+00 2.08E+00 2.69E+01
Country's [0.2561]*** [0.2970]*** [5.4074]***
Wage/Lending rate 1.55E01 5.72E02
Global computer industry [0.1671] [0.1721]
(finalgoods and components) TFP 7.23E04
[9.38E05]***
Capital/Labor 5.14E+01 3.96E09 5.05E09
[8.5543]*** [5.21E09] [ 4.32E09]
Trading partners'
IPRs 5.14E+01 3.01E+01 2.69E+01
[8.5543]*** [5.5495]*** [5.4074]***
SSR 1.69E+09 1.51E+15 1.44E+15
Country's Capital/Labor 8.16E08 7.51E08 8.75E08
[1.06E08 ]*** [7.42E09]*** [1.09E08]***
IPRs 2.07E+00 1.80E+00 2.52E+00
[0.30647]*** [0.19083]*** [0.31321]***
Wage/Lending rate 1.40E01 5.73E02
[0.15279] [0.1721]
Finalgoods computer industry TFP 7.20E04
[0.00009]***
Trading partners' Capital/Labor 8.35E10 9.09E09 5.05E09
[5.19e09 ] [3.59e09]*** [4.32e09]
IPRs 3.49E+01 28.6955 2.69E+01
[5.9819]*** [4.1487]*** [5.4074]***
SSR 4.32E+19 4.33E+19 4.46E+19
Note: T his table presents the estimated coefficients of the selection equation corresponding to the optimal model. T he first column reports
coefficients of the baseline model. T he second column results add to the set of explanatory variables the wage/lending rate. T he third column
results add to the set of explanatory variables total factor productivity (T FP). All the regressions control for time effects. Standard errors
are reported in brackets. *** means statistically significant at the 1% level, ** 5%, and * 10%. SSR means sum of squared residuals.
Table 10. Robustness check: Specification tests
Baseline selection equation + factor prices+ TFP
Industry Test Test regime 1 regime 2 regime 3 regime 4
Cutoff_1 9.6337
[7.2992 , 11.9683]***
Global computer industry Cutoff_2 13.3928 Inverse Mills 28038.46 61592.62 40135.71 40135.71
(finalgoods and components) [10.4163 , 16.3693]*** Ratio
Cutoff_3 23.5303
[18.3366 , 28.7241]*** [176838.6] [38364.74]* [389948.3] [389948.3]
Cutoff_1 9.6337
[7.2992 , 11.9683]***
Cutoff_2 13.3928 Inverse Mills 1815631 10614.36 3.81E+07 5.83E+07
Finalgoods computer industry
[10.4163 , 16.3693]*** Ratio
Cutoff_3 23.5303
[18.3366 , 28.7241]*** [3329255] [4246815] [83200000] [190000000]
Note: T his table shows estimated values of the technologycal index at which the sample splits across regimes (cutoff), together with their confidence intervals. T he table
also presents coefficients for the variables that control for selection bias (Inverse Mills Ratio). T ime effects are not report ed. Standard errors are reported in brackets.
*** means statistically significant at the 1% level, ** 5%, and * 10%.
Table 11. Robustness check: Rybczynski equations
Baseline selection equation + factor prices + TFP
Industry Netexports of computers regime 1 regime 2 regime 3 regime 4
Capital abundance 7.25E07 1.69E07 2.69E06 2.56E06
[ 3.44E07]** [1.30E07] [5.49E07 ]*** [ 5.92E07]***
Skilledlabor abundance 2.02E+00 2.92E+01 1.09E+02 5.03E+02
[10.0727] [7.4703]*** [88.1423] [110.074]***
Global computer industry Unskilledlabor abundance 6.58E+00 2.20E+01 7.05E+01 1.76E+02
(finalgoods and components) [4.6158] [6.0202]*** [44.8552] [100.7755]*
Land abundance 8.20E04 1.92E02 7.99E02 1.08E01
[0.00555] [0.00471]*** [0.03040]*** [0.0288]***
Constant 3.94E+04 3.85E+04 2.74E+05 4.21E+06
[153743] [39860] [447515] [2098217 ]**
Capital abundance 4.10E05 3.11E07 3.00E05 3.63E04
[6.49E06 ]*** [0.000014] [0.00011] [0.00010]***
Skilledlabor abundance 8.47E+01 24.8714 6.31E+04 8.21E+04
[190.2525] [834.7603] [18828] [18989]***
Unskilledlabor abundance 1.22E+02 1040.17 1.81E+02 6.69E+03
Finalgoods computer industry
[87.2031] [ 671.712 ] [9581] [17386]
Land abundance 1.16E01 1.54E+00 8.81E+00 1.48E+01
[0.10494] [0.52461]*** [6.4961] [4.9733]
Constant 2.55E+06 2.00E+05 1.46E+08 9.46E+08
[2900038] [ 43962] [9.56E+07] [3.62E+08]***
Note: T his table shows Rybczynski equations for the 4regime model, for the global computer (final goods and components) and finalgoods computer industries. T he
dependent variables are netexports of each industry. T he independent variables are capital, skilled labor, unskilled labor and land. T he results control for the
"consumption effect" (see text) and time effects. Standard errors are reported in brackets. *** means statistically significant at the 1% level, ** 5 %, and * 10%.
Table 12. Are the Rybczynski coefficients equivalent across regimes? Table 12. Are the Rybczynski coefficients equivalent across regimes? (Cont'd)
Capital abundance_[reg1=reg2] 0.0149** Capital abundance_[reg1=reg2] 0.0082***
Capital abundance_[reg1=reg3] 0.0024*** Capital abundance_[reg1=reg3] 0.5325
Capital abundance_[reg1=reg4] 0.000*** Capital abundance_[reg1=reg4] 0.0017***
Capital abundance_[reg2=reg3] 0.000*** Capital abundance_[reg2=reg3] 0.7893
Capital abundance_[reg2=reg4] 0.0001*** Capital abundance_[reg2=reg4] 0.0004***
Capital abundance_[reg3=reg4] 0.000*** Capital abundance_[reg3=reg4] 0.0111***
Skilledlabor abundance_[reg1=reg2] 0.0305** Skilledlabor abundance_[reg1=reg2] 0.9443
Skilledlabor abundance_[reg1=reg3] 0.2285 Skilledlabor abundance_[reg1=reg3] 0.0008***
Skilledlabor abundance_[reg1=reg4] 0.000*** Skilledlabor abundance_[reg1=reg4] 0.000***
Skilledlabor abundance_[reg2=reg3] 0.3676 Skilledlabor abundance_[reg2=reg3] 0.0008***
Skilledlabor abundance_[reg2=reg4] 0.000*** Skilledlabor abundance_[reg2=reg4] 0.000***
Skilledlabor abundance_[reg3=reg4] 0.0052*** Skilledlabor abundance_[reg3=reg4] 0.4755
Unskilledlabor abundance_[reg1=reg2] 0.0002*** Unskilledlabor abundance_[reg1=reg2] 0.1752
Global computer Unskilledlabor abundance_[reg1=reg3] 0.1561 Unskilledlabor abundance_[reg1=reg3] 0.9748
industry (final Unskilledlabor abundance_[reg1=reg4] 0.070* Finalgoods Unskilledlabor abundance_[reg1=reg4] 0.7058
goods and Unskilledlabor abundance_[reg2=reg3] 0.041** computer industry Unskilledlabor abundance_[reg2=reg3] 0.8989
components) Unskilledlabor abundance_[reg2=reg4] 0.1265 Unskilledlabor abundance_[reg2=reg4] 0.7456
Unskilledlabor abundance_[reg3=reg4] 0.0253** Unskilledlabor abundance_[reg3=reg4] 0.7294
Land abundance_[reg1=reg2] 0.0061*** Land abundance_[reg1=reg2] 0.002***
Land abundance_[reg1=reg3] 0.009*** Land abundance_[reg1=reg3] 0.1695
Land abundance_[reg1=reg4] 0.0002*** Land abundance_[reg1=reg4] 0.0027***
Land abundance_[reg2=reg3] 0.0485** Land abundance_[reg2=reg3] 0.2644
Land abundance_[reg2=reg4] 0.0023** Land abundance_[reg2=reg4] 0.0079***
Land abundance_[reg3=reg4] 0.4989 Land abundance_[reg3=reg4] 0.4632
Constant_[reg1=reg2] 0.624 Constant_[reg1=reg2] 0.6556
Constant_[reg1=reg3] 0.6201 Constant_[reg1=reg3] 0.1348
Constant_[reg1=reg4] 0.0474** Constant_[reg1=reg4] 0.0088***
Constant_[reg2=reg3] 0.4868 Constant_[reg2=reg3] 0.1287
Constant_[reg2=reg4] 0.0429** Constant_[reg2=reg4] 0.009***
Constant_[reg3=reg4] 0.0665* Constant_[reg3=reg4] 0.0036***
Note: T his table presents the pvalues corresponding to the null hypothesis of Note: T his table presents the pvalues corresponding to the null hypothesis of
equivalence between the Rybczynski coefficients of two different regimes in the 4 equivalence between the Rybczynski coefficients of two different regimes in the 4
regime model. T he brackets indicate the regimes involved in each test. *** means regime model. T he brackets indicate the regimes involved in each test. *** means
statistically significant at 1% level, ** 5%, and * 10%. statistically significant at 1% level, ** 5%, and * 10%.