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Export Structure and Economic Performance in Developing Countries: Evidence from Nonparametric Methodology

Discussion paper by Sudip Ranjan Basu, Monica Das, 2011

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This paper offers a closer look of the impact on institutional quality, human capital on GDP per capita for various country-groups in the core model. It provides evidence that a flow of credit and well function financial markets are essential to support higher level of economic performance.The results of the shown nonparametric model support the higher level of skill and technology intensive manufactures and the positive impact they have on GDP per capita in developing countries.

UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT







POLICY ISSUES IN INTERNATIONAL TRADE AND COMMODITIES


STUDY SERIES No. 48





EXPORT STRUCTURE AND ECONOMIC PERFORMANCE IN
DEVELOPING COUNTRIES:


EVIDENCE FROM NONPARAMETRIC METHODOLOGY


by



Sudip Ranjan Basu
UNCTAD, Geneva



and



Monica Das


Skidmore College, New York










UNITED NATIONS
New York and Geneva, 2011





ii


Note

The purpose of this series of studies is to analyse policy issues and to stimulate discussions
in the area of international trade and development. The series includes studies by UNCTAD staff
and by distinguished researchers from academia. This paper represents the personal views of the
authors only, and not the views of the UNCTAD secretariat or its member States.

The designations employed and the presentation of the material do not imply the
expression of any opinion whatsoever on the part of the United Nations Secretariat concerning the
legal status of any country, territory, city or area, or of its authorities, or concerning the
delimitation of its frontiers or boundaries.

Material in this publication may be freely quoted or reprinted, but acknowledgement is
requested, together with a reference to the document number. It would be appreciated if a copy of
the publication containing the quotation or reprint could be sent to the UNCTAD secretariat at the
following address:



Chief


Trade Analysis Branch
Division on International Trade in Goods and Services, and Commodities


United Nations Conference on Trade and Development
Palais des Nations
CH-1211 Geneva





Series Editor:
Victor Ognivtsev


Officer-in-Charge, Trade Analysis Branch






UNCTAD/ITCD/TAB/49






UNITED NATIONS PUBLICATION


ISSN 1607-8291










© Copyright United Nations 2011
All rights reserved





iii


Abstract



The objective of the paper is to use nonparametric methodology to examine the


relationship between skill and technology intensive manufacture exports and gross domestic
product (GDP) per capita, controlling for institutional quality and human capital in developing
countries. The paper uses the Li-Racine (2004) generalized kernel estimation methodology to
examine the role of skill and technology content of the exports in understanding differential level
of economic performance across countries and country groups. In the extended model, we also
control for other factors that influence economic performance such as availability of financial
capital and effective foreign market access of exports of developing countries. The paper uses the
database from the United Nations COMTRADE Harmonized System (HS) four-digit level of
disaggregation to provide new system of classification of traded goods by assigning each one of
them according to their skill and technology content as proposed in Basu (forthcoming). The
analysis is carried out for a set of 88 developing countries over 1995 to 2007. Similar to parametric
results, the nonparametric analysis lends further support to the view that as the skill and technology
content of the exports increase, the impact on GDP per capita increases positivity and significantly
as well, after controlling for other policy variables.






Keywords: Nonparametric analysis, Export structure, Institutions, Developing countries

JEL Classification: C1, F1, O43, R11


 
 








iv






Acknowledgements



We would like to thank Wojciech Stawowy for providing research support and
Sandwip Das, Khalil Rahman, Victor Ognivtsev, Aki Kuwahara and Mia Mikic for their
encouraging comments during the preparation of the paper. Thanks are also due to the
participants’ comments at the Research Workshop on Trade Diversification in the
Context of Global Challenge, UNESCAP-UNCTAD-WTO, Vientiane, Lao PDR, 27-28
October 2010 and XVth Spring Meeting of Young Economists, Luxembourg,
15-17 April 2010, where some concepts and results of this paper were presented. We
gratefully acknowledge the Faculty Development grant from Skidmore College, New
York, as well as the Economics department for funding the services of research assistant
Brian Stickles.

The views expressed in this paper are those of the authors and do not necessarily reflect
the views of the United Nations Secretariat or its members. Any mistakes and errors in
this paper are the authors’ own.








v


Contents
 




1 Introduction..........................................................................................................................1




2 Empirical Methodology .......................................................................................................2




2.1 Nonparametric Density Estimates................................................................................2


2.2 A Generalized Kernel Estimation ................................................................................3


2.3 Computing the IQI .......................................................................................................5




3 Data and Empirical Model ..................................................................................................6




3.1 Data ..............................................................................................................................6


3.2 Dependent and Independent Variables.........................................................................7


3.3 The Empirical Model .................................................................................................13




4 Results .................................................................................................................................13




4.1 Core Model Results....................................................................................................14


4.2 Extended Model Results: Robustness Checks ...........................................................17




5 Conclusions .........................................................................................................................19




References ....................................................................................................................................20





vi


List of figures




Figure 1: Nonparametric Estimation Analysis Framework ............................................................4


Figure 2: Nonparametric pdf Estimates for InGDPPCpenn ...........................................................9


Figure 3: Nonparametric pdf Estimates for Incnsexp .....................................................................9


Figure 4: Nonparametric pdf Estimates for Indnsexp ...................................................................10


Figure 5: Nonparametric pdf Estimates for Inensexp ..................................................................10


Figure 6: Nonparametric pdf Estimates for Iniqi ..........................................................................11


Figure 7: Nonparametric pdf Estimates for Incger .......................................................................11


Figure 8: Nonparametric pdf Estimates for Inpcrdbofgdp............................................................12


Figure 9: Nonparametric pdf Estimates for Inwavg......................................................................12






List of tables




Table 1: Nonparametric First, Second and Third Quartile Estimates .........................................23


Table 2: Nonparametric Median Estimates by Country .............................................................24


Table 3: Nonparametric Median Estimates by Year ...................................................................30


Table 4: Nonparametric Median Estimates by Region ...............................................................33


Table 5: Nonparametric Median Estimates by Emerging Country Group .................................34


Table 6: Nonparametric Median Estimates by Income Group ....................................................35


Table 7: Extended Model: Nonparametric First, Second and Third Quartile Estimates .............36


Table 8: Extended Model: Impact of Covariates on GDP Per Capita by Country ......................37


Table 9: Extended Model: Nonparametric Median Estimates by Year .......................................39


Table 10: Extended Model: Nonparametric Median Estimates by Region ...................................42


Table 11: Extended Model: Nonparametric Median Estimates by Emerging Country Group......43


Table 12: Extended Model: Nonparametric Median Estimates by Income Group........................44






Annex tables




Table A1. List of countries in sample ............................................................................................45


Table A2. Description and sources of variables.............................................................................47


 






1


1. Introduction 


Does transformation in export structure cause differential levels of economic performance
across countries? Should the trade policymaking agenda of developing countries be directed
towards building capacities and capabilities for producing skill and technologically intensive
manufacturing goods with similar to those of developed countries?1 What effects do low, medium
and high-skill and technological intensive exports at the national level have on Gross Domestic
Product per capita (GDPPC) in developing countries? Answers to these questions are relevant for
trade policymakers and planners in developing and least developed countries (LDCs) as well as to
the United Nations and other multilateral organizations.



During the recent global economic and financial crisis, many developing countries faced a


steady decline of their exports revenue due to the over-dependence on international trade leading to
over-exposure of those economies to the rest of the world that eventually led to many unwarranted
impacts on economic growth and employment opportunities at the domestic markets (UNCTAD,
2009) Some developing countries such as China, India, Brazil and others could undertake trade-
related policies to speed up the recovery process –diversification of their exports basket has been
one of the key trade policy components – to stabilize the exports sector growth and subsequently
GDP growth.



In recent years, the trade literature provides a number of empirical evidence to support the


importance of export diversification and what a country produces matter, by examining the
national share of exports (NSEXP) in manufacturing goods (Lall, 2000; Hausman, Hwang and
Rodrik, 2006; UNDESA, 2006; UNECA, 2007; World Bank, 2009; and Shirotori, Tumurchudur
and Cadot, 2010). However, to support the increasing role of exports and their transformation,
countries’ domestic industrial policies require emphasizing the promotion of efficient domestic
institutions, spending on human capital accumulation and well-balanced financial and trade-
supporting economic policies to raise the level of the GDP per capita – a measure of improvement
in economic performance – at the national level (UNCTAD, 2002; Imbs and Wacziarg, 2003;
Dollar and Kraay, 2003; Hausmann and Klinger, 2006; Rodrik, 2007; Klinger, 2009; UNDESA,
2010).



Apart from key role of diversification of exports as well as changing nature of skill and


technological content of products in developing countries to boost economic performance, there are
growing number of research papers in literature to document the critical role of efficient domestic
institutional conditions as well as human capital accumulation and geography (Acemoglu et al.,
2001; Sachs, 2003; Easterly and Levine, 2003; Rodrik et al., 2004; and Basu, 2008).



The purpose of our paper is to further investigate the quality of exports hypothesis by


classifying the exported products in relation to level of skill and technological contents. We
compute shares of low (C), medium (D ) and high (E ) level skill and technology contents of
exported products for each of the countries in the sample and then use the measure of institutional
quality index (IQI) by applying the latent variable technique developed by Nagar and Basu (2002)
and combined gross enrolment ratio (CGER) to explore their impact on income. Utilizing the Li–
Racine nonparametric estimation technique for mixed data, developed by Li and Racine (2004) and
Racine and Li (2004), our paper explores the relationship between GDP per capita (GDPPC) and
level of skill and technology contents of exports. The technique of choice allows us to examine the
GDPPC-(C/D/E) NSEXP, the relationship in a data-driven specification-free manner.





1 For details, refer to the United Nations Statistics Division. Table A1 gives a complete list and classification
of the countries used in the paper.





2


The contribution of our paper is in the application of the Li and Racine (2007)
nonparametric methodology to investigate the relationship between three types of manufactures
exports based on their skill and technology intensity and GDP per capita variable, in a panel with
both time and country effects. In the estimation of any model with GDP per capita and export
structure and other institutional, human capital and policy variables, mainly two types of biases can
be at work: (a) misspecification bias and (b) endogeneity/omitted variable bias. The parametric
estimates potentially suffer from both (Basu, forthcoming). The nonparametric estimates in the
paper effectively deal with (a). Bias due to (b) is left for future works.



Our nonparametric estimates find strong support for positive significant impact of higher


level of skill and technology intensive manufactures on GDP per capita, one of the first attempts in
this field of study. For the majority of the countries examined, the impact of higher level of skill
and technology related exports on the GDP per capita are quite favorable. Since the Li–Racine
methodology provides weighted estimates (weights determined by all observations) of the
regression function and its slope at every data point, we can also examine the nonparametric
estimates for various subgroups by continents and country characteristics. The impact of skill and
technology contents of exports on GDP per capita is far from uniform across countries or time
periods. However, the favourable relationship between these two or minimal support for a negative
relation between the two variables, is robust to most sub groups and country characteristics.



We now sketch a course for the rest of the paper. Section 2 presents the nonparametric


density estimates and the Li–Racine estimation technique for mixed data, utilized in the paper to
the estimation of (C/D/E) NSEXP-GDPPC relationship, and then latent variable technique for
calculating the IQI. Section 3 discusses the data set and the empirical model. Main results of the
paper are presented in section 4 and section 5 concludes the paper.





2. Empirical Methodology 
This section provides description of nonparametric density estimation to all the variables


considered in the analysis and then provide theoretical framework of the Li–Racine (2004)
generalized kernel estimation methodology. We also construct the IQI, which is a composite index
based on the methodology developed by Nagar and Basu (2002).




2.1 Nonparametric Density Estimates 

In this section we obtain some graphs of the probability density functions of the variables


considered in the core as well as extended models. Figures 1 through 8 are the graphs of the density
functions for all economic variables used in the empirical models. The estimator of the probability
function of random variable ℜ∈X at the point ℜ∈x is given by


( ) ( )∑
=


=
n


i
i hxxKn


xf
1


,,1ˆ (1)


In the above equation, X is a continuous random variable, K(.) is the Gaussian kernel density
function and h is a smoothing parameter obtained from the method of cross validation.


We estimate the density functions, unconditional or conditional moments of distributions,
without making any prior assumptions about functional forms. The data are allowed to speak for
themselves in determining the shape of the unknown functions (Silverman, 1986). Suppose X is a
continuous random variable, f(x) is the probability density function and F(x) is the cumulative






3


density function, when X = x. With h as the smoothing parameter, the nonparametric naive
estimate of f(x) is



( ) ( ) ( )[ ] h/2/hxF2/hxFltxf̂


0h
−−+=



(2)


According to equation (2), the nonparametric density estimate ( )xf̂ is 1/h the probability
that X belongs to the interval [ ]2/hx,2/hx +− . In other words, ( )xf̂ is 1/h the probability that
( ) h/xX − belongs to the interval [ ]2/1,2/1− . Following the methodology outlined in
Silverman (1986), we define an identity function.


( ) ( )
otherwise


hxXifI i
0


5.05.00.
=


≤−≤−=
(3)


We rewrite the nonparametric density function as


( ) ∑
=


⎟⎠
⎞⎜⎝


⎛ −=
n


1i


i


h
xXI


nh
1xf̂ (4)



The graph of the estimated density function from equation (3) is not a smooth curve. Thus


the weight function I(.) is replaced by the following kernel density function K(.),


( ) ( ) ⎟⎠
⎞⎜⎝


⎛−= 2ii 2
1exp


2
1K ψπψ (5)


( ) h/xX ii −=ψ ; ( ) 1dK =∫+∞∞− ψψ

The nonparametric density function is


( ) ( )∑
=


=
n


1i
iKnh


1xf̂ ψ (6)

It is well known in the literature that the choice of kernels does not influence significantly


the efficiency of estimates. The choice of window width is, however, crucial, since small values of
h cause over-smoothing and high values lead to under-smoothing of the estimates. To estimate the
density function is (5), we choose the optimum h such that is minimizes some function of the mean
squared error of ( )xf̂ .


2.2   A Generalized Kernel Estimation 

The basic principle behind the nonparametric estimation technique is to fit a window h


(also known as smoothing parameter) around every observation of the data set and estimate the
relationship of interest between variables in each window. A kernel density function K(.) is used to
give high weights to data points close to the window and low weights to data points far from the
window. Thus the regression relationship is estimated, piece by piece or window by window as
shown in figure 1. One of the advantages of nonparametric estimation is that it estimates the
regression function m(.) as well as the slope coefficients β(.) at every data point.






4


Figure 1: Nonparametric Estimation Analysis Framework






If yi is the target variable (GDP per capita) and xi the policy variable (level of skill and


technology content of the manufactures goods, institutional quality or enrolment ratio), (E(yi|xi) <
∞) the relation among them may be expressed in terms of the conditional moment E(yi|xi) =m(xi).
When the actual functional form is unknown, parametric specifications including complex ones
like the translog functions are deemed inadequate. Compared with the parametric procedures, the
nonparametric methodology is more proficient in capturing non linearities in the underlying system
thus dealing with the problem of model misspecification.



The paper uses the Li–Racine Generalized Kernel Estimation Methodology (by Li and


Racine, 2004; and Racine and Li, 2004) to examine the relationship between exports structure by
classifying the product space through level of skills and technology content manufactures and GDP
per capita. Equation (7) represents the basic regression model.



( ) iii xmy ε+= (7)


In equation (7), yi represents the ith observation on the dependent variable (GDP per capita)
and i indexes country-time observations of N countries and T time intervals. Also, m(.) is an
unknown smooth regression function with argument xi=[ ui


c
i xx , ], where


c
ix is a NT×k vector of


continuous variables (low, medium and high skill and technology intensive manufactures as well as
institutional quality and gross combined enrolment ratio), uix is a NT×1 vector of unordered
discrete variables (country effects) and εi is a NT×1 vector of errors. Following the Li-Racine
methodology, we take a first order Taylor expansion of (7) around xj to obtain equation (8).



( ) ( ) ( ) ijcjciji xxxxmy εβ +−+≈ (8)

Here, β(xj) is the partial derivative of m(xj) with respect to xc. The estimate of δ(xj) ≡ [m(xj)


β(xj)]’ is represented by equation (9).


( ) ( )( )⎟⎟⎠


⎜⎜⎝
⎛=


j


j
j x


xm
x βδ ˆ


ˆˆ






5



( )( ) ( )( ) ( )∑∑ ⎟⎟⎠



⎜⎜⎝


−⎥⎥⎦


⎢⎢⎣


⎟⎟⎠


⎜⎜⎝


−−−
−=




i
ic


j
c
i


h
i


c
j


c
i


c
j


c
i


c
j


c
i


c
j


c
i


h yxx
K


xxxxxx
xx


K
1


'
1


ˆ


1


ˆ (9)


In equation (9), ( )∏∏
==


− ⎟⎟⎠


⎜⎜⎝
⎛ −=


r


s


u
s


u
sj


u
si


u
q


s s


c
sj


c
si


sh xxlh


xx
whK


11


1
ˆ


ˆ,,ˆ
ˆ λ is the generalized kernel


function. The commonly used product kernel Kh is from Pagan and Ullah (1999), where w is the
standard normal product kernel function with window width hs = hs(NT) associated with the sth
component of xc. The kernel function lu is a variation of Aitchison and Aitken (1976) kernel
function which equals one if usj


u
si xx = and usλ otherwise.



It is well known in the nonparametric literature that estimation of the bandwidths (h, λu) is


crucial. The methodology helps to implement a number of “data-driven” numerical algorithms to
determine the appropriate bandwidth or smoothing parameters for a given sample. The paper uses
the Least squares cross validation method as discussed in Racine and Li (2004). Least squares
cross validation selects h1, h2, … hq, u1λ , u2λ , … urλ to minimize the following cross validation
function:




( )( ) ( )∑
=


−−=
n


i
iiii xMxmyCV


1


2ˆ (10)



Here, ( )ii xm−ˆ = ( ) ( )./. γγ KKy n illn il ≠≠ ΣΣ is the leave-one-out kernel estimate of m(xi) and


0≤M(.)≤1 is a weight function. The purpose of M(.) is to avoid difficulties caused by dividing by
zero or by the slow convergence rate induced by boundary effects.
 


2.3   Computing the IQI  

The IQI is latent variable, which cannot be measured directly in a straightforward manner.2


However, we assume that any latent variable (Y) is linearly determined by exogenous variables X1,
X2, … Xk. Let Y=α+β1X1+…+βkXk+ε, where X1, X2, … Xk is set of variables that are used to
capture Y. If variance of error ε is small relative to the total variance of the latent variable Y, we
can reasonably assume that the total variation in Y is largely explained by the variation in the
variables. So, which linear combination of X1, X2, … Xk can account for the explained part of the
total variation in Y due to the variables X2, … Xk?



Nagar and Basu (2002), propose to replace the set of variables by an equal number of their


principal components (PC), so that 100 per cent of variation in variables is accounted for by their
PCs.



First, the variables are transformed, or Xk= [Xk – minimum(Xk)/(maximum(Xk) –


minimum(Xk))].3 Finally, IQI is computed as a weighted sum of the transformed version of these
selected variables, where respective weights are obtained from the analysis of principal



2 See Anderson (1984) for detailed discussion on multivariate statistical analysis.
3 N is the total number of countries in the sample and k = number of variables as there are 3 in core model
and 5 in extended model.





6


components.4 Hence, the highest weight is assigned to the first PC, because it accounts for the
largest share of total variation in all indicator variables. Similarly, the second PC accounts for the
second largest share and therefore is assigned the second largest weight, and so on.



Therefore, to calculate IQI, we construct three separate components of IQI: Economic IQI,


Social IQI and Political IQI, and then combine them to obtain IQI. Higher values of IQI indicate a
higher level of institutional quality respectively.


 


3. Data and Empirical Model 
 


3.1   Data 

Our paper is based on 88 developing countries, of which 24 emerging developing countries


(emerging South)5, and 64 other developing countries. The developing country lists also include 45
LDCs and small island developing Countries (SIDS), as defined by United Nations and the World
Trade Organization (WTO) respectively.6 We obtained data from the UNCTAD sources and
several international and research institutions as well as from the University of Pennsylvania.7



The data on countries’ exports are based on the new UNCTAD database of Trade Statistics


called South-South Trade Information System (SSTIS), the data of which is mostly in drawn from
the United Nations Commodity Trade Statistics Database (COMTRADE) covering over 1,250
products at the HS 4-digit level for the years 1995 to 2007. The value of exports at the HS-4 digit
level is measured in United States dollars. Then, we decompose the exports database into six
categories as proposed in Basu (forthcoming) by their level of skill and technology content. The
categories of exports are used to compute different factor-contents to indicate how countries are
moving out from primary commodities to manufactures-skill and technology content sectors. This
paper builds on, especially for classifying the products by skill and technology contents of exports
products, the previous studies UNCTAD (2002, 1996) and Lall (2000, 2005). The novelty of this
new skill and technology contents exports structure classification is due to its focus at the HS-4
digit level products and also to identify products in terms of six different levels: Non-fuel primary
commodities (A), Resource-intensive manufactures (B), Low skill- and technology-intensive
manufactures (C), Medium skill- and technology intensive manufactures (D), High skill- and
technology intensive manufactures (E) and Mineral fuels (F). The paper computes share of low,
medium and high skill and technology intensive manufactures at the national level, a share of these
three categories of country’s total exports for any particular year, excluding mineral fuels.
Furthermore, all the countries with high value of minerals fuels exports are dropped from the
analysis. The classification of skill and technology content of products at HS-4 digit and HS-6 digit
levels can be downloaded from UNCTAD website (http://www.unctad.info/en/Trade-Analysis-
Branch/Data-And-Statistics/Other-Databases/)





4 See Nagar and Basu (2002) for details, and also see Basu, Klein and Nagar (2005).
5 Emerging South classification in this paper is based on UNCTAD country classification, IMF country
classification, Goldman Sachs N11 country groupings, Morgan Stanley Capital International Emerging
Market Index and Basu Emerging Seven country groupings (2007).
6 See Annex Table A1 for a complete list of developing countries.
7 See Annex Table A2 for data sources of the variables used in the paper.






7


3.2  Dependent and Independent Variables 

Our main dependent variable is real GDP per capita (international $, 2005 Constant Prices,


Chain series) to identify level of economic performance at the cross-country level. The
corresponding variable GDPPCpenn is obtained from PWT 6.3, Center for International
Comparisons of Production, Income and Prices at the University of Pennsylvania.



The three main variables are used to understand the skill and technology content of exports


to estimate their impact on the real GDP per capita. It is believed, according to the trade literature,
that with the improvement in quality of manufacturing exports in terms of skill and technological
contents, the country’s economic performance would be higher.



The variable CNSEXP measures share of low skill- and technology-intensive manufactures


as a percentage of total merchandise exports at any given year/period. Similarly, the two other
variables are the following: DNSEXP measures share of medium skill- and technology-intensive
manufactures as a percentage of total merchandise exports and ENSEXP measures share of high
skill- and technology-intensive manufactures as a percentage of total merchandise exports. The
higher values of these variables imply that their importance is increasing of these products in their
export baskets.



The variable IQI measures institutional quality index. IQI is constructed to evaluate the


quality of institutions. It is calculated from three aspects of institutional quality: economic (EIQI),
social (SIQI) and political (PIQI). Economic institutional quality is a combination of legal and
property rights, bureaucratic quality, corruption, democratic accountability, government stability,
law and order, independent judiciary, and regulation; social institutional quality is based on press
freedom, civil liberties, physical integrity index, empowerment right index, freedom of association,
women’s political rights, women’s economic rights, and women’s social rights; and political
institutional quality depends on executive constraint, index of democracy, political rights, polity
score, lower legislative, upper legislative and independent sub-federal units. The IQI is based on
23 indicators of quality of institutions from 1995 to 2007. The higher value of the IQI implies
better level of institutional quality (Basu, 2008).



The variable CGER measures combined gross enrolment ratio. CGER is constructed to


define a possible measure of human capital. Human capital plays a major role in enhancing labour
productivity and eventually the economic performance. Availability of skilled manpower eases
resource constraints, makes productive capacities efficient, and thereby increases production and
exports of skill and technology intensive manufactures. The measure comes from the UNESCO
Education Database from 1995 to 2007.



In the extended model, we include two variables to broaden up the scope of supporting


policies at the national and global level to help increase the trade integration process and
subsequently improve GDP per capita.



The variable PCRDBOFGDP measures financial sector resource availability.


PCRDBOFGDP is constructed to define a possible measure of size of financial system. The
functioning of financial system and markets significantly affects economic performance. A well-
functioning credit market can directly provide available funds/savings to where they can be
invested most efficiently. The following variable is selected to reflect the domestic credit allocation
condition for financial resource availability in private sector: the private credit by deposit money
banks and other financial institutions as a percent of GDP is another. The higher value of the
variable implies better access of a country’s financial resources for commerce (World Bank, 2009).
The measure comes from the World Bank Financial Structure Dataset from 1995 to 2007.






8


The variable WAVG measures effective foreign market access. WAVG is shown to define a
possible measure of effective access to foreign markets. This measure tries to capture trade barriers
faced in destination markets. For example, the trade-weighted average tariff that any country faces
on international markets corresponds to the trade weighted average imposed by its trade partners.
However, low tariff barriers in destination markets may not be a fully adequate guide to the
openness of the markets of receiving countries. The following variable is selected to reflect the
this market access: Trade-weighted average tariff applied on exports in partner countries (per cent)
is the average of effectively applied rates by trading partners weighted by the total imports of
trading partner countries. The higher value of the WAVG implies better access of a country’s
exports to the foreign markets (UNCTAD, 2007). The measure comes from the UNCTAD-
TRAINS database from 1995 to 2007.



In figures 2–9, we present the graphs of nonparametric estimates of the density (pdf)


function of all variables used in the paper. Using the methodology outlined in section 2.1, we
estimate the pdf using data information of all countries used in the core and extended model, for
the three years, 1995, 2003 and 2007. Thus, we are able to analyse how the functions change over
the time period under consideration in the paper. All variables are in measured in logs. In figure 2,
we look at the density function of the log of GDPPCpenn, the variable used to measure economic
performance. The density function is bi-model and moves to the right from 1995 to 2007, as all
countries have more income. Figures 3, 4 and 5 look at the density functions of the log of variables
(C/ D/ E) NSEXP; used to measure the share of low/ medium/ high skill- and technology intensive
manufactures in total merchandise exports. The pdf for log of share of low skill-technology
manufactures in exports (CNSEXP) shifts downwards and to the left, the pdf for the log of share of
medium skill-technology manufactures (DNSEXP) in exports shifts to the right and the density
function for the log of share of high skill-technology manufactures (DNSEXP) in exports also shifts
to the right and changes shape from a uni-modal to a bi-modal distribution. Overall, we observe
that during the period 1995 – 2007, more countries had a high share of high to medium skill-
technology manufactures in exports and more countries have a low share of low skill-technology
manufactures in exports. Figure 6, illustrates the density function of the log of IQI, the index
measuring institutional quality. Over time the pdf changes from a bi-modal to a uni-modal
distribution. Thus, the distribution of IQI is likely to be log-normal. In figure 7, we see movements
in the density function of log of CGER, variable measuring combined enrolment ratio or
accumulation of human capital in the country. The density function moves upwards during the time
period considered, as more countries have higher measures of human capital accumulation. The
distribution of log of PCRDBOFGDP, variable measuring the size of the financial resources
availability in the system, is illustrated in figure 8. We observe the estimated density function shifts
downwards as fewer countries have large credit flows available in their economies. A similar trend
is observed for log of WAVG, variable measuring effective access to foreign market access of their
exports. The pdf shifts downwards and to the left. Over the time period 1995–2007, it seems
through this measure that fewer countries have effective market access.








9


Figure 2: Nonparametric pdf Estimates for lnGDPPCpenn


0
.1


.2
.3


.4
no


np
ar


am
et


ric
d


en
si


ty
e


st
im


at
es


6 7 8 9 10
lnGDPPCpenn


1995 2003
2007


Variable lnGDPPCpenn







Figure 3: Nonparametric pdf Estimates for lncnsexp


0
.1


.2
.3


no
np


ar
am


et
ric


d
en


si
ty


e
st


im
at


es


-2 0 2 4 6
lncnsexp


1995 2003
2007


Variable lncnsexp







10


Figure 4: Nonparametric pdf Estimates for lndnsexp


0
.1


.2
.3


no
np


ar
am


et
ric


d
en


si
ty


e
st


im
at


es


-2 0 2 4
lndnsexp


1995 2003
2007


Variable lndnsexp







Figure 5: Nonparametric pdf Estimates for lnensexp


0
.0


5
.1


.1
5


.2
no


np
ar


am
et


ric
d


en
si


ty
e


st
im


at
es


-4 -2 0 2 4
lnensexp


1995 2003
2007


Variable lnensexp








11


Figure 6: Nonparametric pdf Estimates for lniqi


0
.5


1
1.


5
2


no
np


ar
am


et
ric


d
en


si
ty


e
st


im
at


es


3 3.5 4 4.5
lniqi


1995 2003
2007


Variable lniqi







Figure 7: Nonparametric pdf Estimates for lncger


0
.5


1
1.


5
2


no
np


ar
am


et
ric


d
en


si
ty


e
st


im
at


es


2.5 3 3.5 4 4.5
lncger


1995 2003
2007


Variable lncger








12


Figure 8: Nonparametric pdf Estimates for lnpcrdbofgdp


0
.1


.2
.3


.4
no


np
ar


am
et


ric
d


en
si


ty
e


st
im


at
es


-10 -8 -6 -4 -2 0
lnpcrdbofgdp


1995 2003
2007


Variable lnpcrdbofgdp







Figure 9: Nonparametric pdf Estimates for lnwavg


0
.1


.2
.3


.4
.5


no
np


ar
am


et
ric


d
en


si
ty


e
st


im
at


es


-2 0 2 4
lnwavg


1995 2003
2007


Variable lnwavg








13


3.3   The Empirical Model 

The main objective of our work is to examine the impact of three levels of exports based


on skill and technology content of the products (low, medium and high) on GDP per capita
(GDPPCpenn). In the core model specification, other covariates in the model are the institutional
quality index (IQI) and the combined gross enrolment ratio (CGER). To capture the relationship
between skill and technology contents of exports and GDP per capita, we replace a typical
parametric model of the form,



lnGDPPCpennit=β0+β1ln(C/D/E)NSEXPit+β2lnIQIit+β3lnCGERit+εit



with the corresponding nonparametric model in equation (5). Here, m(.) is an unknown smooth
function of the covariates, αi are unobserved country characteristics that are constant over time.
This flexible estimation strategy helps us avoid any functional form misspecification bias and
enables us to explore the shape of the underlying relationship without superimposing any a priori
functional form restriction.



lnGDPPCit=m(αi, lnCNSEXPit, lnIQIit, lnCGERit) (5)



lnGDPPCit=m(αi, lnDNSEXPit, lnIQIit, lnCGERit) (6)

lnGDPPCit=m(αi, lnENSEXPit, lnIQIit, lnCGERit) (7)



We have also estimated the extended model to check the robustness of the variable of


interest, along with two additional covariates such as PCRDBOFGDP and WAVG apart from IQI
and CGER.




lnGDPPCit=m(αi, lnCNSEXPit, lnIQIit, lnCGERit, lnPCRDBOFGDPit, lnWAVGit) (8)



lnGDPPCit=m(αi, lnDNSEXPit, lnIQIit, lnCGERit, lnPCRDBOFGDPit, lnWAVGit) (9)



lnGDPPCit=m(αi, lnENSEXPit, lnIQIit, lnCGERit, lnPCRDBOFGDPit, lnWAVGit) (10)





This paper is based on 88 countries as shown in table A1. However, sample size differs due
to availability of PCRDBOFGDP and WAVG which have data on 64 countries. We construct a
panel of 1144 observations with all country-time combinations in the core model and 832
observations with all country-time combinations in the extended model.


 


4. Results 

This section discusses results for the core empirical model and then describes results from


the extended model as robustness check. In section 4.1, we initially discuss results from core model
which has three main independent variables for the sample of 88 developing countries over the
period of 1995–2007. The three independent variables are the (low/medium/high) skill and
technology manufactures exports share of the total national exports of goods, measure of
institutional quality and combined enrolment ratio The results also reported for three group of
countries, namely (a) regional groupings as Asia, Americas and Africa; (b) emerging South and
other developing countries; and (c) least developed countries and small island developing countries





14


and other developing countries. In section 4.2, we discuss results for the extended model which
then include two additional variables, viz., measure of financial resource availability in the
economy and effective market access index. The extended model consists of 64 developing
countries due to lack of comparable data on two additional variables across the years and countries.
All the variables are in logs (denoted here as prefix “ln” to all the variables). Hence, we can
interpret all nonparametric as well as parametric estimates as measures of elasticity.



As noted earlier, the nonparametric estimation technique gives us an estimate of the value


of the regression function (the conditional moment) and its slope at every country-time period
combination. To help us with the analysis and interpretation of results, we provide the slope
estimates at the 25th, 50th and 75th percentiles (labeled quartiles 1, 2 and 3 or Q1, Q2 and Q3) and
their standard errors obtained via bootstrapping. For comparison we also state the results from a
similar parametric model. The table also indicates which estimates are significant at the 90 per
cent, 95 per cent or 99 per cent confidence level. To explore the relationship between exports with
low, medium and high skill and technology manufactures and GDP per capita along with other
independent variables, we show the results in the tables below. Hence all nonparametric as well as
parametric estimates measure elasticity of the dependent variable with respect to the independent
variable.



4.1 Core Model Results 

We now show the relationship between (C/D/E) NSEXP and GDP per capita. Tables 1 to 6


show a set of nonparametric estimates in which GDP per capita is regressed on values of (C/D/E)
NSEXP and IQI (institutions) and CGER (human capital) as regressors.



Table 1 displays the nonparametric estimates of the responsiveness of GDP per capita to


changes in CNSEXP, DNSEXP and ENSEXP. More specifically, the first column of tables 1.a to
1.c measures the percentage change in GDP per capita when skill and technology content of
manufactures exports changes by 1 per cent, i.e. the export elasticity.



For CNSEXP, at the first quartile, the nonparametric estimate of the impact CNSEXP on


GDPPCpenn is -0.059 (0.011), which is statistically significant at the 1 per cent level. At the
median, the impact is negative, - 0.006 (0.001), also significant. Finally, at the 75th percentile, the
nonparametric estimate is positive significant at the 1 per cent level (0.029 (0.002)). For the overall
sample, we can note that for more than 50 per cent of the country-year observations, the low skill
and technology content manufactures export elasticity is negative.



For DNSEXP, at the first quartile, the nonparametric estimate of the impact DNSEXP on


GDPPCpenn is -0.032 (0.004), which is statistically significant at 1 per cent level. At the median,
the impact is positive, 0.013 (0.003), but also significant. Finally, at the 75th percentile, the
nonparametric estimate is positive significant at the 1 per cent level (0.082 (0.004)). For the overall
sample, we can note that for more than 75 per cent of the country-year observations, the medium
skill and technology content manufactures export elasticity is positive.



For ENSEXP, at the first quartile, the nonparametric estimate of the impact DNSEXP on


GDPPCpenn is -0.004 (0.001), which is statistically significant at 5 per cent level. At the median,
the impact is positive, 0.040 (0.002), also significant. Finally, at the 75th percentile, the
nonparametric estimate is positive significant at the 1 per cent level (0.121 (0.005)). For the overall
sample, we can note that for more than 75 per cent of the country-year observations, the high skill
and technology content manufactures export elasticity is positive.



The estimated coefficient varies from -0.006 to 0.040, with ENSEXP impact estimates


being larger than CNSEXP and DNSEXP estimates at the median. At the median of ENSEXP slope






15


coefficient is 0.04 implies when the skill and technology content manufactures of exports increases
by 10 per cent, GDP per capita increases by 0.40 per cent.



The second column of tables 1.a to 1.c measures the institutional elasticity or the


percentage change in GDP per capita when institutional quality changes by 1 per cent. In all the
three specifications, more than 75 per cent of the observations show a positive estimate of the
institutional elasticity. Here, at the median of IQI slope coefficient is 0.16 (table 1.c) implies,
controlled for the high skill and technology content manufactures of exports and education
variables, when institutional quality increases by 1 per cent, GDP per capita increases by 0.16 per
cent which is a large impact.



The third column of tables 1.a to 1.c measures the education elasticity or the percentage


change in GDP per capita when education changes by 1 per cent. In all the three specifications,
more than 75 per cent of the observations show a positive estimate of the educational elasticity.
Here, at the median of CGER slope coefficient is 1.384 (table 1.c) implies, controlled for the high
skill and technology content manufactures of exports and institutional quality variable, when
combined school enrolment increases by 1 per cent, GDP per capita increases by 1.39 per cent
which is also a large impact. All standard errors are obtained via bootstrapping and are provided in
the parentheses below the estimates. So, the results in tables 1.a to 1.c shows that for three
categories of exports contents, we can make two important observations. First, there is quite large
evidence of a statistically significant, positive impact of high skill and technology content
manufactures on development as compared to low and medium groups. Second, the effect of
higher NSEXP is not uniform across country-time period combinations.



Tables 2.a to 2.c show the nonparametric median estimates of the responsiveness of


GDPPCpenn to changes in (C/D/E) NSEXP for each country.

For CNSEXP at the median, Uruguay has the highest positive and significant estimate of


∂GDPPCpenn/∂CNSEXP, while Malaysia has the highest negative and significant estimate.
Among 88 countries, 39 countries have positive median estimates and 49 have negative median
estimates. In the case of IQI, 58 countries have positive median estimates and 30 countries have
negative median estimates. For CGER, 62 countries have positive median estimates and 26 have
negative median estimates.



For DNSEXP at the median, Malaysia has the highest positive and significant estimate of


∂GDPPCpenn/∂DNSEXP, while Peru has the highest negative and significant estimate. Among 88
countries, 53 countries have positive median estimates and 35 have negative median estimates. In
this case, IQI in 59 countries have positive median estimates and 29 countries have negative
median estimates. For CGER, 64 countries have positive median estimates and 24 have negative
median estimates.



For ENSEXP at the median, Malaysia has the highest positive and significant estimate of


∂GDPPCpenn/∂ENSEXP, while Philippines has the highest negative and significant estimate.
Among 87 countries (data on Seychelles is missing), 66 countries have positive median estimates
and 21 have negative median estimates. Similarly, IQI in 58 countries have positive median
estimates and CGER in 77 countries have positive median estimates respectively.



Table 3 presents the median elasticities by time periods to access any changes in the


GDPPCpenn- C/D/E) NSEXP relationship over time. Table 3.a shows that for every time period,
the median nonparametric estimate of the slope of the GDPPCpenn- CNSEXP function is negative
but statistically insignificant, although in values, the median elasticities have not been stable over
time. The GDPPCpenn- IQI function is positive and statistically significant over time as well as
the function of GDPPCpenn-CGER.






16


Table 3.b shows the GDPPCpenn- DNSEXP function is positive and statistically
significant in some years. The values of median elasticities remained within the range of 0.010 and
0.017 over time which is much higher than median elasticties of GDPPCpenn- CNSEXP.



Table 3.b presents the median elasticities by time periods to access any changes in the


GDPPCpenn- ENSEXP relationship over time has increased positively and statistically significant
in all the 13 years. It is also worth noting that their absolute values are in the range of 0.026 and
0.063 (in 2007). In summary, we can make observation that the impact of high skill and
technology content manufactures exports on GDP per capita has increased over time as compared
to two other groups of products.



The nonparametric estimate of the regression function or the slope at any observation is a


weighted average, where the weights are determined by the closeness of other data points to that
observation. Also, he nonparametric estimates are calculated at every data point, so we are able to
examine the nonparametric slope estimates for various subgroups. We examine median estimates
for three continents: (a) Asia, (b) Americas and (c) Africa. Tables 4.a to 4.c show the
nonparametric median estimates of the responsiveness of GDPPCpenn to changes in (C/D/E)
NSEXP for each continents. At the median, estimate of the slope of the GDPPCpenn- CNSEXP
function is negative and statistically significant for Asia [-0.027(0.011)] and Americas [-0.010
(0.002)], and then impact is positive but insignificant [0.001(.001)] for Africa. Table 4.a also
shows that institutions have positive and significant impact in Americas and Asia, while
educational achievements have positive and significant impact in all continents.



In the case of GDPPCpenn- DNSEXP estimates at the median, all the continents have


positive and statistically significant impact with largest impact on DNSEXP on GDP per capita is
in Americas [0.031 (0.004)]. The results for IQI and CGER are similar as in the case of
GDPPCpenn- CNSEXP functional estimates as in table 4.a.



Interestingly, estimate of the slope of the GDPPCpenn- ENSEXP function is positive and


statistically significant for Asia [0.0 61(0.008)], followed by Americas [0.0 55(0.005)] and Africa
[0.025(0.003)]. Once again, for these continents, there is strong evidence of a statistically
significant positive relationship between GDPPCpenn and ENSEXP as compared to CNSEXP and
DNSEXP.



It should also be noted that IQI impact is largest in Americas on GDPPDpenn (table 4.c,


0.644) compared to Asia and Africa. Whereas in the case of CGER, for all the continents, it has a
positive and significant impact on GDPPCpenn and is largest in Asia (table 4.c, col. 3, 2.030).



Tables 5.a to 5.c show estimated results for two different country groups distinguished by


their growing importance in the world economy: emerging countries and other developing
countries at the median. For CNSEXP, impact is negative but significant for both the country
groups. However, the impact is positive and statistically significant for DNSEXP and ENSEXP.
The higher shares of medium and high skill technology intensive manufactures tend to have higher
positive and significant impact for the emerging South countries.



Tables 6.a to 6.c present estimates separately for two country groups distinguished by


income levels: least developed countries and small island developing countries (LDCSIDS) and
non LDCSIDS. Like before, impact of DNSEXP and ENSEXP is positive and statistically
significant in the case of both groups and estimated coefficient is much higher of ENSEXP in non-
LDCSIDS compared to LDCSIDS group of countries. IQI and CGER have positive and significant
impact on GDPPCpenn for both groups of countries.



To summarize the effects of CNSEXP, DNSEXP and ENSEXP covariates, we note the


following: the nonparametric estimate of ∂GDPPCpenn/∂ CNSEXP is negative and significant and






17


that of ∂GDPPCpenn/∂ DNSEXP is positive and significant at the median. The median
nonparametric estimate of responsiveness of ∂GDPPCpenn/∂ ENSEXP is positive and significant
for the entire dataset and different country groups and years under consideration. The higher values
of the estimated elasticities for ENSEXP suggest that high skill and technology intensive
manufactures have higher impact on GDP per capita than low and medium skill and technology
intensive manufactures in contributing the path of development of a country.



Also, the effects of the remaining covariates, ∂GDPPCpenn/∂ IQI and ∂GDPPCpenn/∂


CGER are mostly positive and significant in influencing GDP per capita in this current sample.

In addition, if we look at the estimates for the entire dataset, the parametric estimate of the


impact of CNSEXP, DNSEXP and ENSEXP on GDPPCpenn are always positive and statistically
significant and their estimated slope coefficient varies from 0.058 (CNSEXP) to 0.196
(DNSEXP), with 0.151 for ENSEXP. Also, the parametric estimates lie above third quartile of the
nonparametric estimates and are multiple times as large as the median of the nonparametric
estimates. It is clear that parametric estimates are global estimates whereas nonparametric
estimates are locally weighted, vary across the observations and give a broader picture of the
GDPPCpenn- (C/D/E) NSEXP relationship. The ∂GDPPCpenn/∂ IQI and ∂GDPPCpenn/∂ CGER
have positive and significant impact on GDP per capita as well likewise in the case of
nonparametric estimates.



Furthermore, any discrepancy between the signs of the parametric and nonparametric


estimates may arise due to two types of biases: a misspecification bias and an endogeneity/omitted
variable bias. The parametric model potentially suffers from both, the nonparametric model
potentially suffers only from the second type of bias. Thus, it is the misspecification bias and its
interaction with the endogeneity bias that drives the differences across the two estimation
techniques. Nonparametric instrumental variable techniques are not fully developed and will be
explored in our future research.




4.2 Extended Model Results: Robustness Checks 


 
In this section, we include two additional variables, as has been used in the literature, to


test the robustness of results in tables 1.a to 1.c. The objective here is to cross check to (a) resource
availability from financial sector (PCRDBOFGDP) institutions such as banks and (b) effective
foreign market access (WAVG) – as an exogenous variable − play a role in influencing GDP per
capita other than through level of skill and technology intensive manufactures exports, institutional
quality and combined gross enrolment. We run these model specifications for the sample of 64
developing countries from the core model sample as the data is not consistently available for
PCRDBOFGDP and WAVG.



Tables 7.a to 7.c examines the impact of PCRDBOFGDP and WAVG on GDPPCpenn for


countries with three different types of skill and technology intensive manufactures exports. It
displays the 25th, 50th and 75th percentiles of all nonparametric estimates. More than 50 per cent of
the nonparametric estimates of the impact of PCRDBOFGDP on GDPPCpenn are significant
positive in all the three types of exports structure. For all three levels of export structures at the 75th
percentile, the nonparametric estimate of WAVG-GDPPCpenn relationship is positive significant at
the conventional levels. It appears that the majority of the countries have not been able to
completely take advantage of the effective foreign market access (and preferences) in favorably
influencing the development paths of their economies. On the other hand, the results clearly
indicate that efficient functioning of the financial market and credit flows for business sector
development is critical ingredient to increase the level of GDP per capita in all countries over the





18


time period. More importantly, the level of skill and technology intensity manufacture exports still
matters for improving the level of GDP per capita, along with a strong institutional structure and
educational level.



Table 8 shows the impact of all the five covariates at the median for all the countries in the


sample with the high skill and technology intensive manufacture exports share. 8 The results
suggest that for a 60 per cent of the country-time period observations, the relationship between
ENSEXP and GDP per capita is significant positive, while 70 per cent of cases are for
PCRDBOFGDP and only 23 per cent for WAVG. The relationship between CGER and
GPPPCpenn is the strongest (77 per cent) and followed by IQI (59 per cent). So, the country level
results also show that higher level of skill and technology contents of exports matter to improve the
GDP per capita along with good institutions, human capital and financial markets. Tables 9.a to 9.c
present results of the nonparametric estimates at the median by year for all the covariates. The
results provide further support a positive impact of ENSEXP on GDP per capita along with other
covariates except for WAVG.



Likewise in core model (table 4), we now present the results by region. The new set of


results in tables 10.a to 10.c indicate that the impact of CNSEXP and DNSEXP on GDP per capita
is positive significant Africa, along with institutions, human capital and financial credit flows. The
effective foreign market access is positive and significant in the specification with DNSEXP in
African countries. It seems that the level of effective foreign market access to these low-income
countries has not been uniform across all sectors and their impact is also dispersed across countries
with the regions. In the case of Americas, the results show that their increasing share in ENSEXP
has been helping them to improve their GDP per capita along with support from human capital,
institutions and financial resource availability. The impact of ENSEXP on GDP per capita is
positive in Asia but not significant while human capital and efficient financial market activities
have positive and significant impact on their economic development.



A similar set of results are obtained in tables 11.a to 11.c in the case of emerging South


countries in comparison to other south countries in the sample. It clearly shows that emerging
south countries have transformed their exports structure from low skill and technology contents
exports to higher level of products to raise their level of GDP per capita. Another set of results for
LDCSIDS indicate that WAVG has positive and significant impact on GDP per capita in the case
of DNSEXP and ENSEXP of specifications which implies that highly targeted preferential foreign
market access of LDCSIDS exports products, especially in developed market could help them to
influence their GDP per capita as shown in tables 11.a to 11.c. It also appears that for
∂GDPPCpenn/∂ ENSEXP in LDCSIDS is positive and significant in this extended model. This
implies that the countries in LDCSIDS group when undertake policies to improve their export
structure for more sophisticated products, could potentially improve their GDP per capita
effectively as was shown in the case of core model (table 6c).






8 We report only the median nonparametric estimates of ENSEXP for brevity. More detailed nonparametric
results for DNSEXP and CNSEXP and the remaining covariates are available if requested from the authors.






19


5.  Conclusions 
The impact of high skill and technology intensive manufactures exports on economic


performance has enormous implications for development policy makers and international agencies
to achieve the Millennium Development Goals (MDGs). In this paper, we reassess the relationship
between three levels of skill and technology contents of manufactures and GDP per capita by
utilizing the Li–Racine methodology.



We examine here a dataset of 88 developing countries over the 1995–2007 time period.


There is strong evidence of a statistically significant, positive impact of high skill and technology
content products on GDP per capita. It’s worth noting that the nonparametric estimates are far from
uniform over all country-time period combinations.



The paper also offers a closer look of the impact on institutional quality, human capital on


GDP per capita for various country-groups in the core model. The extended model also provides
evidence that a flow of credit and well function financial markets are essential to support higher
level of economic performance. We also found that effective market access for products from
Africa and low income economies have been helpful to enhance their export capacity vis-à-vis
GDP per capita. Due to differences in level of economic development in Asia and the Americas, a
majority of the countries have not been, a first look at the evidence, beneficial of the foreign
market access of their products.



The results of the nonparametric model of our paper support the notion that in general the


higher level of skill and technology intensive manufactures could help increase GDP per capita in
developing countries. Our paper supports the view that countries with higher quality of exports
product along with better institutional quality, human capital and financial markets are in a better
position to reap benefits from trade integration and economic policies. On the other hand, countries
with low skill and technology related products with weak institutional quality, lower level of
human capital and lack of financial resources find it difficult to enhance their economic
performance level. Overall, our empirical evidence indicate that effective support to the exports
sectors, which has competitive advantage to enhance their capability to produce high quality and
skill and technology content exports. Developing countries should underscore the urgent need for
trade-policy support along with emphasizing on augmenting domestic investment for high quality
of human capital development and increasing institutional efficiency as a necessary component to
improve productive capacity for harmonious economic development.







20


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publication, New York.


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Level, Policy Issues in International Trade and Commodities. United Nations publication.
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paper no. 9490. National Bureau of Economic Research.


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York.


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22


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23


List of tables 


Table 1: Nonparametric First, Second and Third Quartile Estimates


Table 1.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lncnsexp lniqi lncger




(1) (2) (3)
1st quartile -0.059*


(.011)
-0.060*
(.005)


-0.159*
(.034)


Median -0.006*
(.001)


0.086*
(.004)


0.627*
(.05)


3rd quartile 0.029*
(.002)


0.316*
(.021)


1.384*
(.062)


Parametric 0.058*
(.013)


0.295*
(.051)


1.46*
(.065)




Table 1.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lndnsexp lniqi lncger




(1) (2) (3)
1st quartile -0.032*


(.004)
-0.056*
(.009)


-0.018
(.063)


Median 0.013*
(.003)


0.119*
(.011)


0.737*
(.041)


3rd quartile 0.082*
(.004)


0.324*
(.017)


1.478*
(.069)


Parametric 0.196*
(.014)


0.249*
(.249)


1.34*
(.061)




Table 1.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lnensexp lniqi lncger




(1) (2) (3)
1st quartile -0.004**


(.001)
-0.109*
(.018)


0.466*
(.026)


Median 0.040*
(.002)


0.160*
(.018)


1.384*
(.047)


3rd quartile 0.121*
(.005)


0.623*
(.022)


2.211*
(.055)


Parametric .151*
(.011)


.348*
(.048)


1.31*
(.062)



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.





24


Table 2: Nonparametric Median Estimates by Country


Table 2.a: Low skill- and Technology-Intensive Manufactures



Dependent variable: GDP per capita


(international $, 2005 Constant Prices)_ lnGDPPCPenn
ccode lncnsexp se lniqi se lncger se
(1) (2) (3) (4) (5) (6)
AFG -0.082 .054 0.084* .003 -0.423 .435
ARG -0.414* .014 0.983* .018 1.305* .001
BDI 0.002* .000 0.000 .020 -0.375* .056
BEN -0.018 .019 -0.019* .003 0.280* .071
BFA 0.133* .007 0.363* .016 0.806* .071
BGD 0.097* .002 -0.089* .001 1.953* .006
BHS -0.018* .000 0.034 .036 1.449* .064
BLZ -0.008* .000 0.166* .016 1.964* .02
BOL -0.116* .000 0.056* .002 0.902* .008
BRA -0.097* .001 0.542* .005 -0.620* .007
BTN -0.001 .001 0.493* .056 2.260* .064
CAF 0.036* .000 0.090* .001 0.374* .009
CHL -0.022* .003 -0.046* .000 3.326* .015
CHN -0.125* .026 0.208* .005 13.121* .163
CIV 0.192* .000 0.050* .002 -0.338* .002
CMR -0.040* .001 -0.136* .019 0.259* .004
COL 0.045* .000 0.238* .000 1.092* .000
COM 0.009* .000 0.088* .005 -0.166* .009
CPV -0.043* .000 1.4* .001 3.122* .011
CRI 0.031* .001 1.539* .001 1.873* .008
CUB 0.006* .000 0.204* .005 1.288* .012
DJI 0.026* .009 -1.210* .164 0.027 .037
DMA -0.063* .000 -0.070* .011 -1.16* .017
DOM 0.044* .006 0.897* .037 1.512* .126
EGY -0.183* .001 0.080* .0001 -0.5* .019
ERI -0.039* .007 0.02 .02 -0.475* .06
ETH 0.097*** .052 0.187* .041 0.263 .293
FJI -0.046* .000 0.02* .001 -0.015 .019
GHA -0.036* .001 0.007* .002 1.412* .029
GIN 0.003 .005 0.035 .047 0.431** .174
GMB -0.012* .000 -0.034* .001 1.028* .012
GNB -0.007* .000 0.41* .001 -10.516* .086
GRD -0.042* .000 1.912* .000 2.114* .030
GTM 0.002* .000 0.121* .015 0.485* .006
GUY 0.054* .008 0.169* .045 -0.1648 .011
HND 0.032* .001 0.404* .009 1.242* .005
IDN -0.210* .022 -0.218* .033 1.005* .041
IND 0.046 .054 -0.104* .008 1.923* .093
JAM -0.011* .000 0.240* .000 0.014 .027
JOR 0.029* .000 -0.151* .000 1.123* .011
KEN 0.004* .000 -0.026* .008 0.280* .000
KHM 0.090* .024 -0.148 .134 2.03* .016
KNA -0.02* .007 0.352* .006 -0.947* .006
KOR 0.003 .008 -0.456* .005 4.126* .072






25



Dependent variable: GDP per capita


(international $, 2005 Constant Prices)_ lnGDPPCPenn
ccode lncnsexp se lniqi se lncger se
(1) (2) (3) (4) (5) (6)
LAO -0.094* .001 -0.050* .011 3.091* .024
LBN 0.139* .002 -0.060* .001 0.543* .015
LBR 0.04 .037 -0.207 .18 1.349* .395
LKA -0.027* .004 -0.834* .002 -1.636* .009
LSO -0.12* .008 0.574* .047 0.616* .098
MAR -0.008 .009 0.272* .023 0.556* .038
MDG 0.009 .015 0.254* .071 -0.387* .094
MEX 0.044** .019 0.355* .001 1.215* .048
MLI 0.025* .004 0.447* .057 0.539* .052
MNG -0.040* .005 -0.119* .019 0.922* .017
MOZ 0.006 .037 0.177* .026 1.259* .076
MUS 0.209* .012 0.302* .022 3.193* .033
MWI -0.091* .017 -0.154 .136 -0.95* .103
MYS -0.517* .002 0.009* .001 2.919* .010
NAM 0.084* .000 -0.581* .000 -0.468* .000
NER 0.003** .001 -0.068* .016 0.07* .013
NIC -0.007** .003 -0.116* .02 0.648* .023
NPL -0.099* .006 -0.064* .001 0.758* .004
PAK 0.034* .011 0.068* .024 0.992* .053
PAN -0.328* .022 0.643* .029 -0.865* .218
PER -0.403* .000 0.488* .000 -0.992* .014
PHL -0.252* .000 -0.382* .001 -2.432* .041
PRY -0.005* .000 0.111* .005 -0.615* .007
RWA -0.004* .000 0.248* .002 -0.063* .005
SEN 0.021* .002 0.018* .006 0.381* .011
SLB -0.18* .008 0.327* .006 -1.651* .058
SLV -0.036* .003 0.214* .012 0.812* .003
STP -0.009* .000 0.357* .010 1.649* .036
SUR 0.007* .000 0.274* .000 -3.482* .02
SYC 0.034* .000 0.669* .003 1.343* .013
TCD -0.087* .000 -0.569* .032 1.085* .034
TGO -0.144* .000 0.140* .001 0.610* .000
TON 0.01* .000 0.079* .002 -0.124* .002
TUN -0.004** .001 0.086* .001 6.67* .004
TUR -0.279* .009 -0.088* .009 0.161* .062
TZA 0.042 .028 -0.09* .031 0.587* .013
UGA 0.059* .000 -0.034* .002 1.124* .018
URY 0.612* .005 0.256* .002 4.242* .047
VCT -0.038* .000 0.436* .010 4.801* .000
VNM 0.015* .006 -0.122* .002 13.874* .027
WSM -0.004* .000 0.496* .000 0.518* .004
ZAF -0.007* .002 -0.069* .005 -4.415* .031
ZMB -0.129* .025 0.214* .089 1.283* .071
ZWE -0.277* .002 1.264* .002 -4.08* .015



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.





26


Table 2.b: Medium Skill- and Technology-Intensive Manufactures



Dependent variable: GDP per capita


(international $, 2005 Constant Prices)_ lnGDPPCPenn
ccode lndnsexp se lniqi se lncger se
(1) (2) (3) (4) (5) (6)

AFG -0.096* 0.012 0.179** .078 -1.324* 0.22
ARG 0.395* .005 0.760* .018 1.879* .036
BDI 0.008** .003 -0.018 .013 -0.447* .107
BEN 0.030 .020 0.013 .029 0.222* .012
BFA 0.140* .012 0.053** .024 0.901* .084
BGD -0.016* .001 -0.088* .001 1.949* .017
BHS 0.301* .004 0.526* .055 1.495* .105
BLZ -0.024* .000 0.217* .027 1.965* .041
BOL -0.077* .001 -0.072* .003 0.771* .006
BRA -0.074* .005 0.810* .000 -0.560* .004
BTN -0.067* .001 0.359* .072 2.459* .088
CAF -0.002* .000 0.078* .001 0.503* .002
CHL 0.099* .007 0.102* .011 3.457* .012
CHN 0.320* .037 0.189* .007 13.252* .047
CIV -0.022* .000 0.405* .007 -1.075* .033
CMR -0.029** .012 -0.072* .004 0.314* .096
COL 0.031* .001 0.200* .001 1.146* .002
COM -0.016* .006 0.116* .019 -0.124* .039
CPV 0.006* .000 1.403* .010 2.913* .080
CRI 0.083* .001 1.439* .089 1.742* .042
CUB -0.043* .000 0.103* .023 1.683* .058
DJI 0.097* .006 -1.727* .222 -0.160* .024
DMA 0.076* .006 0.184* .005 -0.649* .035
DOM 0.064* .008 0.861* .056 0.860* .211
EGY -0.204* .005 0.199* .004 -2.095* .005
ERI -0.009 .009 0.060*** .033 -0.533* .148
ETH -0.001 .009 0.163 .110 0.412* .097
FJI 0.050* .000 -0.017* .002 0.199* .026
GHA 0.050* .003 -0.006 .008 1.117* .089
GIN 0.035 .069 0.100 .159 0.471** .204
GMB 0.009* .002 -0.019* .004 1.074* .025
GNB 0.010* .000 0.406* .001 -11.762* .082
GRD 0.166* .001 1.195* .003 1.138* .032
GTM 0.010* .012 0.127* .037 0.475* .026
GUY 0.011* .001 0.239* .063 -0.169* .017
HND 0.026* .001 0.306* .010 1.109* .013
IDN -0.209* .008 -0.269* .043 1.727* .014
IND 0.281* .049 -0.234* .062 1.518* .032
JAM 0.018** .007 0.175* .009 0.362* .001
JOR -0.25* .008 0.008** .004 0.696* .049
KEN 0.027* .002 -0.023 .026 0.260* .012
KHM -0.023 .017 0.077 .13 2.222* .032
KNA 0.139* .003 0.220* .013 -0.894* .049
KOR 0.271* .000 -0.460* .010 3.212* .008
LAO -0.074* .000 0.121* .015 3.407* .028
LBN 0.115* .010 0.026* .003 0.125* .006






27



Dependent variable: GDP per capita


(international $, 2005 Constant Prices)_ lnGDPPCPenn
ccode lndnsexp se lniqi se lncger se
(1) (2) (3) (4) (5) (6)
LBR 0.106* .008 -0.258 .220 0.873 .686
LKA 0.183* .000 -0.560* .001 -1.310* .013
LSO -0.067* .004 0.662* .092 0.672* .179
MAR 0.104* .033 0.297* .048 0.480* .010
MDG -0.009* .002 0.144** .061 -0.418* .119
MEX 0.286* .022 0.314* .030 1.358* .004
MLI 0.017** .007 0.328*** .198 0.504* .068
MNG 0.058* .006 -0.038 .024 1.211* .054
MOZ 0.007 .035 0.172*** .104 1.182* .160
MUS -0.108* .000 0.311* .022 3.143* .052
MWI 0.053* .002 -0.026 .161 -0.684* .183
MYS 0.581* .000 -0.220* .006 1.003* .010
NAM 0.088* .000 -0.728* .001 0.332* .001
NER -0.006* .001 -0.075*** .039 -0.013 .018
NIC -0.009* .002 -0.183* .025 0.622* .035
NPL 0.031* .000 -0.069* .003 0.540* .020
PAK -0.108* .003 0.036* .010 1.128* .034
PAN -0.034* .003 0.637* .061 2.252* .015
PER -0.382* .000 0.584* .001 -4.897* .036
PHL 0.271* .000 0.235* .000 2.426* .030
PRY 0.085* .001 0.233* .005 -0.498* .011
RWA -0.014* .000 0.302* .008 -0.198* .035
SEN 0.005** .002 -0.030 .023 0.412* .021
SLB -0.122* .001 -0.127* .022 -1.203* .034
SLV 0.055* .005 0.109* .029 0.559* .008
STP -0.044* .001 0.554* .019 1.745* .050
SUR -0.037* .000 0.254* .000 -3.201* .017
SYC -0.061* .000 0.781* .004 0.831* .005
TCD -0.088* .010 -0.210 .217 1.141* .056
TGO 0.031* .002 0.357* .003 1.079* .034
TON 0.003* .000 0.040* .001 -0.037* .009
TUN 0.321* .001 0.046* .000 5.378* .003
TUR -0.094* .006 -0.190* .024 1.408* .034
TZA 0.013* .004 -0.100* .017 0.786* .04
UGA 0.037* .000 -0.188* .013 0.935* .012
URY 0.326* .003 0.146* .011 0.823* .053
VCT -0.023* .001 0.378* .025 4.831* .004
VNM 0.162* .000 -0.050* .004 10.4* .013
WSM 0.028* .001 0.497* .001 0.200* .031
ZAF 0.061* .004 -0.138* .009 -3.937* .063
ZMB 0.031 .071 0.156* .026 1.101* .085
ZWE -0.156* .000 1.202* .007 -5.37* .109



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.





28


Table 2.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


ccode lnensexp se lniqi se lncger se
(1) (2) (3) (4) (5) (6)
AFG .073 .063 1.42** .767 -.141 .86
ARG 0.119* 0.011 1.404* 0.011 2.823* 0.016
BDI -.004 .005 .017 .084 -.406* .127
BEN -0.009* 0.003 0.274** 0.131 0.520* 0.185
BFA 0.033* 0.004 0.065* 0.015 0.797* 0.108
BGD 0.064* 0.003 -0.259* 0.028 0.901* 0.09
BHS 0.171* 0.015 0.718* 0.216 1.110* 0.21
BLZ -0.041* 0.003 0.687* 0.103 1.995* 0.128
BOL -0.020* 0.005 0.410* 0.12 -0.909* 0.17
BRA 0.011*** 0.006 0.955* 0.045 0.969* 0.041
BTN 0.004 0.026 -0.15 0.133 2.095* 0.114
CAF 0.024* 0.003 0.034** 0.015 0.546* 0.08
CHL 0.037* 0.01 1.164* 0.109 2.934* 0.113
CHN .279* .042 .172* .016 8.9* .757
CIV -0.005 0.004 0.408* 0.05 -0.764* 0.06
CMR -0.050* 0.004 -0.161* 0.017 0.463* 0.019
COL 0.121* 0.01 0.284* 0.034 2.108* 0.059
COM .010 .017 .116 .24 .27 .293
CPV .069* .009 1.26* .083 3.16* .266
CRI 0.041* 0.001 1.234* 0.073 1.479* 0.024
CUB -.063* .022 -.122 .162 2.418* .614
DJI .011 .007 -1.38* .175 -.094* .031
DMA .145* .007 .706* .041 .081 .063
DOM 0.009 0.056 0.174 0.23 2.572* 0.864
EGY -0.145* 0.014 0.091** 0.045 0.454* 0.202
ERI -.125* .041 .076 .073 .088 .154
ETH .060* .019 .378* .072 .457* .095
FJI 0.088* 0.003 0.033 0.066 2.854* 0.211
GHA 0.032* 0.005 -0.033 0.039 1.496* 0.024
GIN -.049* .007 -.934* .258 .100 .096
GMB 0.008 0.008 -0.057*** 0.03 1.453* 0.338
GNB -.001 .006 .51 .249** 1.8* .098
GRD .058* .009 1.7* .148 1.09* .321
GTM 0.201* 0.018 0.683* 0.033 0.798* 0.031
GUY 0.326* 0.03 0.206 0.129 -0.812* 0.037
HND 0.068* 0.01 0.761* 0.07 2.011* 0.058
IDN 0.033 0.059 -0.211 0.158 1.503* 0.199
IND 0.155* 0.028 -0.2* 0.055 1.676* 0.062
JAM 0.131* 0.005 0.67* 0.021 1.849* 0.071
JOR -0.013** 0.006 0.228* 0.045 1.770* 0.052
KEN 0.087* 0.005 -0.053* 0.017 0.493** 0.198
KHM -0.009 0.02 -0.174 0.175 2.094* 0.044
KNA .046* .006 1.09* .063 -.111** .052
KOR 0.163* 0.002 -0.415* 0.041 7.654* 0.033
LAO -0.023* 0.004 0.127* 0.03 2.914* 0.061






29


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


ccode lnensexp se lniqi se lncger se
(1) (2) (3) (4) (5) (6)
LBN .184* .007 -.088* .029 1.79* 0.094
LBR .297* .092 .954* .154 2.339* 0.719
LKA 0.179* 0.007 -0.497* 0.085 2.273* 0.141
LSO -.022* .006 -.256 .186 2.045* .318
MAR 0.078* 0.024 0.031 0.094 0.506* 0.114
MDG -0.018 0.012 0.134 0.115 0.113 0.4
MEX 0.148* 0.02 0.333* 0.007 1.038* 0.054
MLI 0.005 0.01 0.13* 0.019 0.419* 0.013
MNG 0.127* 0.027 -0.493* 0.062 1.354* 0.146
MOZ 0.035* 0.007 0.135* 0.05 1.266* 0.017
MUS 0.193* 0.016 1.251* 0.182 0.401* 0.141
MWI 0.115* 0.023 0.544 0.363 -0.276*** 0.165
MYS 0.385* 0.014 -0.232* 0.054 3.467* 0.072
NAM .144* .001 -.310* .026 2.019* .032
NER 0.006 0.013 -0.207 0.195 0.252** 0.121
NIC 0.074* 0.021 -0.078 0.29 2.024* 0.273
NPL 0.004 0.004 0.007 0.081 1.407* 0.21
PAK 0.090* 0.021 0.002 0.027 0.603* 0.103
PAN 0.045* 0.008 0.597* 0.013 2.259* 0.036
PER -0.027** 0.012 0.425* 0.036 0.077 0.063
PHL -0.235* 0.018 -0.047* 0.016 1.014* 0.142
PRY 0.177* 0.003 1.103* 0.041 1.381* 0.047
RWA 0 0.016 0.344* 0.055 1.0* 0.376
SEN 0.054* 0.008 0.255* 0.041 0.241* 0.043
SLB -.116* .004 -.742* .091 -.091 .117
SLV 0.034* 0.005 0.724* 0.06 1.401* 0.021
STP .004 .005 .783* .059 1.51* .13
SUR -0.092** 0.039 -0.005 0.061 3.571* 0.209
TCD 0.016** 0.008 -1.153* 0.403 1.105* 0.136
TGO 0.067* 0.006 0.622* 0.151 0.999* 0.477
TON -.018* .002 -.038 .038 1.659* .246
TUN 0.140* 0.006 -0.061 0.055 4.660* 0.169
TUR 0.024** 0.012 0.175* 0.04 2.246* 0.093
TZA 0.043* 0.016 -0.433* 0.138 0.637* 0.054
UGA 0.112* 0.01 1.072* 0.2 2.168* 0.223
URY 0.008 0.013 0.716* 0.052 1.061* 0.017
VCT .072* .008 .052** .027 3.629* .073
VNM 0.127* 0.006 0.206* 0.068 7.664* 0.531
WSM 0.035* 0.005 0.396* 0.007 3.091* 0.152
ZAF 0.185* 0.008 -0.431* 0.011 -0.124* 0.151
ZMB 0.007 0.009 0.227* 0.081 1.462* 0.102
ZWE .067*** .039 .223 .260 2.132 1.586



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.





30


Table 3: Nonparametric Median Estimates by Year



Table 3.a: Low Skill- and Technology-Intensive Manufactures

Dependent variable: GDP per capita


(international $, 2005 Constant Prices)_ lnGDPPCPenn
Year lncnsexp Rank lniqi Rank lncger Rank


(1) (2) (3) (4) (5) (6)


1995
-0.006
(.006) 6


0.090**
(.034) 11


0.731*
(.161) 13


1996
-0.005
(.006) 7


0.089**
(.037) 10


0.700*
(.159) 12


1997
-0.005
(.006) 8


0.091**
(.036) 13


0.670*
(.159) 9


1998
-0.005
(.006) 9


0.091**
(.035) 12


0.641*
(.155) 7


1999
-0.005
(.006) 10


0.089*
(.030) 9


0.614*
(.152) 5


2000
-0.004
(.006) 11


0.083**
(.035) 5


0.591*
(.156) 2


2001
-0.004
(.005) 12


0.087**
(.037) 7


0.580*
(.160) 1


2002
-0.004
(.005) 13


0.084**
(.040) 6


0.608*
(.156) 4


2003
-0.006
(.007) 5


0.087**
(.039) 8


0.600*
(.153) 3


2004
-0.009
(.008) 1


0.083**
(.035) 4


0.615*
(.149) 6


2005
-0.008
(.009) 3


0.079**
(.031) 1


0.649*
(.155) 8


2006
-0.008
(.008) 4


0.082*-
(.030) 2


0.683*
(.158) 11


2007
-0.009
(.008) 2


0.082*
(.028) 3


0.682*
(.162) 10



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.
Higher rank indicates higher absolute value of the estimates.






31


Table 3.b: Medium Skill- and Technology Intensive Manufactures

Dependent variable: GDP per capita


(international $, 2005 Constant Prices)_ lnGDPPCPenn
Year lndnsexp Rank lniqi Rank lncger Rank


(1) (2) (3) (4) (5) (6)


1995
0.014
(.009) 8


0.116*
(.039) 6


0.809*
(.154) 13


1996
0.012
(.009) 4


0.122*
(.036) 8


0.808*
(.157) 12


1997
0.015
(.01) 10


0.136*
(.034) 12


0.798*
(.156) 11


1998
0.014
(.010) 7


0.138*
(.035) 13


0.795*
(.156) 10


1999
0.013
(.011) 6


0.126*
(.036) 10


0.789*
(.154) 9


2000
0.011
(.01) 3


0.122*
(.036) 9


0.733*
(.135) 8


2001
0.017***


(.010) 13
0.117*
(.036) 7


0.674*
(.140) 4


2002
0.015
(.011) 11


0.126*
(.043) 11


0.662*
(.137) 2


2003
0.016**
(.010) 12


0.115*
(.044) 5


0.644*
(.129) 1


2004
0.010
(.011) 2


0.114*
(.037) 3


0.674*
(.125) 3


2005
0.010
(.010) 1


0.089**
(.041) 1


0.686*
(.128) 5


2006
0.014***


(.008) 9
0.097**
(.038) 2


0.702*
(.126) 6


2007
0.013***


(.008) 5
0.114*
(.041) 4


0.723*
(.117) 7



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.
Higher rank indicates higher absolute value of the estimates.






32


Table 3.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


Year lnensexp Rank Lniqi Rank lncger Rank
(1) (2) (3) (4) (5) (6)


1995
0.037*
(.009)* 5


0.098
(.061) 1


1.429*
(.228)* 10


1996
0.038*
(.013)* 6


0.124***
(.076) 2


1.262*
(.201)* 3


1997
0.04*
(.010) 8


0.13**
(.051)** 4


1.351*
(.202)* 7


1998
0.039*
(.009)* 7


0.130**
(.063)** 5


1.305*
(.154)* 4


1999
0.034*
(.012)* 3


0.186*
(.045)* 11


1.433*
(.200)* 11


2000
0.035*
(.010)* 4


0.138**
(.060)** 7


1.251*
(.222)* 2


2001
0.026*
(.009)* 1


0.150**
(.063)** 8


1.390*
(.176)* 8


2002
0.031*
(.01)* 2


0.137**
(.060)** 6


1.244*
(.190)* 1


2003
0.040*
(.009)* 9


0.169***
(.073)** 10


1.345*
(.190)* 6


2004
0.043*
(.014)* 10


0.127*
(.054)* 3


1.337*
(.178)* 5


2005
0.047*
(.013)* 11


0.15*
(.053)* 9


1.404*
(.141)* 9


2006
0.055*
(.012)* 12


0.215*
(.060)* 12


1.556*
(.130)* 13


2007
0.063*
(.015)* 13


0.321*
(.065)* 13


1.545*
(.169)* 12



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.
Higher rank indicates higher absolute value of the estimates.






33


Table 4: Nonparametric Median Estimates by Region



Table 4.a: Low Skill- and Technology Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lncnsexp lniqi lncger




(1) (2) (3)


Asia -0.027** (.012)
-0.061*
(.006)


0.964*
(.062)


Americas
-0.010*
(.002)


0.253*
(.011)


1.009*
(.105)


Africa
0.001
(.002)


0.080*
(.008)


0.395*
(.046)





Table 4.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lndnsexp lniqi Lncger




(1) (2) (3)


Asia 0.028* (.009)
-0.017
(.014)


1.211*
(.109)


Americas
0.031*
(.004)


0.246*
(.01)


0.833*
(.121)


Africa
0.007*
(.002)


0.078*
(.015)


0.471*
(.035)





Table 4.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lnensexp lniqi lncger




(1) (2) (3)


Asia 0.061* (.008)*
-0.046*
(.017)*


2.030*
(.072)*


Americas
0.055*
(.005)*


0.644*
(.022)*


1.478*
(.048)*


Africa
0.025*
(.003)*


0.084*
(.022)*


0.689*
(.066)*



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.






34


Table 5: Nonparametric Median Estimates by Emerging Country Group



Table 5.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lncnsexp lniqi lncger




(1) (2) (3)


South
-0.006*
(.002)


0.100*
(.012)


0.535*
(.035)


Emerging
South


-0.007***
(.004)


0.023
(.030)


1.119*
(.024)





Table 5.b: Medium Skill- and Technology Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lndnsexp lniqi lncger





(1) (2) (3)


South
0.005*
(.002)


0.133*
(.014)


0.549*
(.025)


Emerging
South


0.090*
(.012)


0.079*
(.023)


1.156*
(.068)





Table 5.c: High Skill- and Technology Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lnensexp lniqi lncger




(1) (2) (3)


South
.031*
(.003)


0.184*
(.024)*


1.311*
(.058)*


Emerging
South


0.086*
(.007)*


0.105*
(.035)*


1.687*
(.089)*



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.









35


Table 6:Nonparametric Median Estimates by Income Group



Table 6.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lncnsexp lniqi lncger




(1) (2) (3)


Non-ldcsids
-0.007*
(.002)


0.080*
(.011)


0.880*
(.060)


Ldcsids
-0.004**


(.002)
0.090*
(.009)


0.565*
(.045)





Table 6.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lndnsexp lniqi lncger




(1) (2) (3)


Non-ldcsids
0.043*
(.007)


0.117*
(.021)


0.899*
(.103)


ldcsids
0.005*
(.002)


0.121*
(.017)


0.531*
(.046)





Table 6.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lnensexp lniqi lncger





(1) (2) (3)


Non-ldcsids
0.067*
(.007)


0.17*
(.027)


1.558*
(.070)


ldcsids
0.026*
(.003)


0.152*
(.025)


1.099*
(.083)



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.










36


Table 7: Extended Model: Nonparametric First, Second and Third Quartile Estimates



Table 7.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lncnsexp lniqi lncger lnpcrdbofgdp lnwavg




(1) (2) (3) (4) (5)
1st quartile -0.142*


(.011)
-0.061*
(.016)


-0.004
(.056)


0.008
(.007)


-0.106*
(.005)


Median -0.007
(.008)


0.190*
(.018)


0.732*
(.055)


0.160*
(.016)


-0.036*
(.003)


3rd quartile 0.120*
(.014)


0.585*
(.043)


1.528*
(.068)


0.479*
(.015)


0.021*
(.003)


Parametric 0.03**
(.015)


0.438*
(.069)


1.032*
(.071)


0.425*
(.026)


-0.182*
(.024)





Table 7.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lndnsexp lniqi lncger lnpcrdbofgdp lnwavg




(1) (2) (3) (4) (5)
1st quartile -0.049**


(.015)
-0.067*
(.019)


0.097
(.211)


-0.066*
(.025)


-0.055*
(.015)


Median 0.004
(.006)


0.060***
(.034)


0.533*
(.105)


0.063**
(.032)


-0.015**
(.007)


3rd quartile 0.044*
(.016)


0.337*
(.089)


0.979*
(.178)


0.156*
(.045)


0.016**
(.007)


Parametric 0.173*
(.016)


0.399*
(.063)


0.943*
(.067)


0.371*
(.024)


-0.166*
(.020)





Table 7.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn


lnensexp lniqi lncger lnpcrdbofgdp lnwavg




(1) (2) (3) (4) (5)
1st quartile -0.012*


(.001)
-0.069*
(.004)


-0.040
(.091)


-0.045*
(.003)


-0.060*
(.003)


Median 0.006*
(.001)


0.036*
(.007)


0.453*
(.038)


0.052*
(.014)


-0.021*
(.002)


3rd quartile 0.034*
(.002)


0.278*
(.041)


1.166*
(.069)


0.221*
(.012)


0.010*
(.001)


Parametric 0.012*
(.011)


0.421*
(.065)


1.022*
(.068)


0.361*
(.026)


-0.152*
(.021)



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.






37


Table 8: Extended Model: Impact of Covariates on GDP Per Capita by Country



Dependent variable: GDP per capita (international $, 2005 Constant Prices)_ lnGDPPCPenn


ccode lnensexp* se lniqi ** se lncger *+ se lnpcrdbofgdp + se lnwavg ++ se


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


ARG 0.205 0.037 0.590 0.122 0.117 0.120 -0.163 0.007 -0.028 0.000


BEN -0.017 0.001 -0.061 0.014 -0.450 0.006 0.934 0.007 -0.081 0.000


BFA -0.003 0.003 -0.088 0.000 0.008 0.085 0.730 0.017 0.197 0.002


BGD 0.038 0.003 0.623 0.078 1.542 0.163 0.123 0.053 -0.004 0.007


BHS 0.076 0.000 -0.093 0.021 0.286 0.039 -0.030 0.007 0.007 0.008


BLZ -0.025 0.002 0.014 0.090 2.521 0.275 0.051 0.017 0.007 0.009


BOL -0.005 0.000 -0.034 0.011 0.665 0.021 0.145 0.013 -0.066 0.022


BRA 0.005 0.000 0.699 0.013 -0.280 0.008 0.019 0.001 -0.058 0.002


BTN -0.007 0.015 0.057 0.011 0.241 0.034 0.222 0.015 0.012 0.004


CAF 0.013 0.000 0.459 0.010 0.686 0.086 0.459 0.023 0.026 0.025


CHL 0.051 0.000 -0.070 0.000 0.291 0.046 0.263 0.051 -0.050 0.002


CIV -0.011 0.000 0.067 0.000 0.284 0.030 -0.061 0.008 0.027 0.000


CMR -0.034 0.000 -0.471 0.047 0.813 0.066 -0.780 0.045 -0.029 0.014


COL 0.011 0.000 -0.051 0.005 2.029 0.022 0.270 0.005 -0.058 0.001


CRI 0.031 0.001 0.093 0.005 0.837 0.018 0.032 0.002 -0.023 0.001


DOM 0.019 0.015 0.968 0.092 0.974 0.042 0.029 0.002 -0.029 0.002


EGY -0.061 0.003 0.405 0.013 -0.485 0.046 0.298 0.025 0.013 0.001


FJI -0.001 0.000 0.705 0.134 0.185 0.430 0.202 0.189 0.017 0.035


GHA 0.000 0.008 0.140 0.003 -1.570 0.036 -0.041 0.007 -0.090 0.003


GMB -0.011 0.000 0.072 0.007 0.311 0.007 0.142 0.002 -0.021 0.000


GTM 0.005 0.004 -0.136 0.013 1.113 0.072 -0.072 0.003 -0.064 0.000


GUY -0.052 0.003 -0.010 0.002 0.981 0.024 -0.055 0.005 0.011 0.001


HND 0.007 0.000 0.056 0.019 0.677 0.123 0.125 0.043 0.048 0.004


IDN 0.018 0.010 0.082 0.003 0.379 0.011 0.068 0.014 -0.004 0.001


IND 0.036 0.054 0.320 0.009 -0.309 0.016 0.161 0.014 -0.091 0.001


JAM 0.030 0.000 0.325 0.005 1.481 0.043 -0.083 0.012 0.006 0.000


JOR 0.039 0.000 -0.166 0.026 0.491 0.060 0.443 0.041 -0.166 0.002


KEN -0.003 0.004 -0.153 0.203 1.007 0.164 0.068 0.037 -0.242 0.181


KHM 0.058 0.007 0.602 0.008 1.006 0.024 -0.193 0.004 -0.013 0.001


KOR 0.103 0.003 -0.037 0.000 1.700 0.002 0.004 0.001 -0.089 0.000


LAO 0.006 0.001 0.013 0.021 0.076 0.040 0.093 0.020 -0.042 0.008


LKA 0.176 0.000 -0.398 0.002 3.401 0.001 -0.212 0.004 -0.038 0.000


MAR -0.014 0.002 -0.058 0.016 1.085 0.024 -0.045 0.003 -0.117 0.000


MDG -0.018 0.004 0.320 0.063 -0.243 0.004 -0.088 0.050 -0.004 0.015


MEX 0.386 0.004 0.446 0.033 -1.777 0.131 -0.111 0.014 0.006 0.003


MLI 0.001 0.005 0.063 0.007 1.044 0.029 0.079 0.000 -0.441 0.000


MNG 0.000 0.001 -0.014 0.044 0.220 0.025 0.218 0.052 0.003 0.005


MOZ 0.001 0.010 0.138 0.022 1.589 0.053 0.441 0.001 -0.003 0.003


MUS -0.036 0.003 -0.197 0.008 1.350 0.011 0.010 0.013 0.008 0.001





38


Dependent variable: GDP per capita (international $, 2005 Constant Prices)_ lnGDPPCPenn


ccode lnensexp* se lniqi ** se lncger *+ se lnpcrdbofgdp + se lnwavg ++ se


MWI 0.018 0.000 -0.141 0.154 0.570 0.141 0.007 0.015 -0.034 0.034


MYS 0.230 0.005 0.335 0.009 0.304 0.040 0.184 0.023 -0.031 0.000


NER -0.013 0.008 -0.026 0.038 1.246 0.020 -0.037 0.011 0.004 0.003


NIC -0.002 0.001 -0.067 0.001 0.452 0.019 0.113 0.003 0.045 0.000


NPL 0.002 0.000 -0.139 0.023 0.556 0.002 0.088 0.011 -0.013 0.000


PAK -0.025 0.001 -0.089 0.003 0.197 0.009 -0.101 0.012 -0.029 0.004


PAN -0.222 0.002 0.001 0.012 0.941 0.007 0.324 0.005 0.060 0.000


PER -0.003 0.001 1.719 0.022 2.376 0.012 -0.379 0.003 0.229 0.001


PHL -0.123 0.002 0.291 0.010 -0.505 0.024 0.004 0.000 -0.029 0.000


PRY 0.026 0.002 0.171 0.006 -0.943 0.065 0.008 0.008 -0.175 0.004


RWA 0.017 0.006 0.010 0.003 -4.445 0.039 -0.047 0.001 -0.273 0.000


SEN 0.002 0.000 0.041 0.008 0.026 0.068 0.292 0.044 -0.036 0.011


SLV -0.046 0.001 0.054 0.019 1.590 0.080 0.293 0.012 0.005 0.000


SUR 0.162 0.014 -0.076 0.007 0.422 0.004 -0.052 0.002 0.021 0.001


TCD 0.013 0.003 0.048 0.002 -3.895 0.005 0.137 0.000 -0.048 0.000


TGO 0.061 0.011 -0.544 0.001 -1.418 0.001 -0.042 0.003 -0.249 0.001


TUN 0.126 0.001 0.014 0.022 -1.186 0.340 0.085 0.008 -0.067 0.016


TUR -0.020 0.000 -0.143 0.043 0.468 0.008 0.037 0.025 0.006 0.009


TZA 0.011 0.002 0.080 0.027 -0.782 0.094 0.226 0.002 -0.046 0.004


UGA 0.040 0.002 0.467 0.003 6.266 0.035 0.516 0.001 -0.031 0.000


URY -0.335 0.014 -0.084 0.003 -0.238 0.035 0.281 0.001 -0.071 0.001


VNM 0.056 0.018 0.027 0.001 0.362 0.022 0.051 0.043 0.063 0.005


WSM -0.007 0.000 -0.049 0.087 1.759 0.161 0.017 0.006 -0.010 0.009


ZAF 0.009 0.000 0.012 0.043 4.956 0.984 0.147 0.050 -0.010 0.006


ZMB 0.008 0.009 0.429 0.093 1.880 0.081 0.336 0.010 0.168 0.019

Notes: Standard errors are in parentheses.


Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
Lower rank indicates higher absolute value of the estimates
*All nonparametric median estimates are significant at the 95% level with the exception of BFA,
BTN, DOM, GHA, GTM, IND, KEN, MLI, MNG, MOZ, NER, ZMB-
**All nonparametric median estimates are significant at the 95% level with the exception of BLZ,
KEN, LAO, MNG, MWI, NER, PAN, TUN, WSM, ZAF-
*+ All nonparametric median estimates are significant at the 95% level with the exception of ARG,
BFA, FJI, SEN.
+ All nonparametric median estimates are significant at the 95% level with the exception of FJI,
MUS, MWI, PRY, TUR, VNM.
++ All nonparametric median estimates are significant at the 95% level with the exception of BGD,
BHS, BLZ, CAF, FJI, KEN, MDG, MNG, MOZ, MWI, NER, TUR, WSM.







39


Table 9: Extended Model: Nonparametric Median Estimates by Year



Table 9.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita (international $, 2005 Constant Prices)_ lnGDPPCPenn


Year lncnsexp Rank lniqi Rank lncger Rank lnpcrd bofgdp Rank lnwavg Rank


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


1995
0.002
(.023) 7


0.291*
(.088) 13


0.922*
(.166) 12


0.159*
(.054) 8


-0.034***
(.018) 6


1996
0.024
(.029) 13


0.197*
(.061) 7


0.889*
(.174) 11


0.137*
(.04) 2


-0.025
(.015) 12


1997
0.018
(.034) 12


0.145***
(.078) 2


0.816*
(.200) 9


0.154*
(.049) 6


-0.016
(.021) 13


1998
0.014
(.018) 11


0.203*
(.073) 8


0.689*
(.144) 7


0.147*
(.04) 4


-0.029**
(.012) 10


1999
0.011
(.026) 10


0.228*
(.08) 11


0.636*
(.159) 5


0.144*
(.042) 3


-0.033*
(.012) 8


2000
0.010
(.03) 9


0.175**
(.079) 5


0.614*
(.137) 4


0.149*
(.043) 5


-0.036*
(.010) 5


2001
-0.023
(.022) 4


0.174*
(.041) 4


0.792*
(.175) 8


0.116**
(.055) 1


-0.033*
(.010) 7


2002
0.008
(.026) 8


0.187*
(.06) 6


0.572*
(.149) 2


0.195*
(.059) 9


-0.052*
(.010) 1


2003
-0.024
(.028) 3


0.203*
(.063) 9


0.573*
(.185) 3


0.157*
(.059) 7


-0.044*
(.012) 2


2004
-0.035
(.035) 1


0.136***
(.072) 1


0.638*
(.226) 6


0.251*
(.05) 13


-0.04*
(.013) 4


2005
-0.022
(.016) 5


0.165*
(.06) 3


0.568*
(.184) 1


0.205*
(.049) 10


-0.027***
(.016) 11


2006
-0.031
(.030) 2


0.213*
(.057) 10


0.889*
(.13) 10


0.207*
(.056) 11


-0.031**
(.015) 9


2007
-0.020
(.034) 6


0.232*
(.066) 12


0.941*
(.135) 13


0.244*
(.062) 12


-0.041*
(.009) 3



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.
Higher rank indicates higher absolute value of the estimates.






40


Table 9.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita (international $, 2005 Constant Prices)_ lnGDPPCPenn


Year lndnsexp Rank lniqi Rank lncger Rank
lnpcrd
bofgdp Rank lnwavg Rank


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


1995
0.003
(.008) 5


0.053**
(.090) 11


0.549*
(.090) 2


0.038
(.030) 12


-0.007
(.007) 3


1996
0.004
(.009) 8


0.048
(.036) 12


0.544*
(.126) 3


0.040
(.03) 11


-0.004
(.008) 1


1997
0.003
(.007) 6


0.061
(.041) 8


0.580*
(.104) 1


0.037
(.031) 13


-0.006
(.008) 2


1998
0.007
(.006) 13


0.061
(.048) 7


0.526*
(.081) 6


0.048
(.029) 8


-0.013
(.009) 4


1999 0.006 10 0.062 6 0.540 4 0.046 10 -0.018 9


2000
.006


(.009) 11
.056


(.045) 9
.53*


(.108) 5
.053**
(.024) 7


-.018**
(.008) 8


2001
.006


(.006) 12
.07**
(.033) 4


.498*
(.1) 7


.073*
(.026) 4


-.020*
(.008) 11


2002
.004


(.007) 7
.053*
(.031) 10


.498*
(.104) 8


.060
(.028) 6


-.02***
(.01) 13


2003
.005


(.006) 9
.077**
(.03) 3


.399*
(.095) 13


.046**
(.031) 9


-.02**
(.008) 12


2004
.001


(.008) 4
.07**
(.032) 5


.417*
(.113) 10


.067*
(.030) 5


-.019**
(.007) 10


2005
-.002
(.006) 2


.077*
(.030) 2


.413*
(.129) 12


.086**
(.033) 2


-.015**
(.007) 6


2006
-.001
(.007) 3


.092***
(.023) 1


.416*
(.123) 11


.083*
(.035) 3


-.015*
(.005) 5


2007
-.003
(.007) 1


.044***
(.026) 13


.476*
(.137) 9


.1*
(.03) 1


-.016*
(.004) 7



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.
Higher rank indicates higher absolute value of the estimates.






41


Table 9.c: High Skill- and Technology-Intensive Manufactures

Dependent variable: GDP per capita (international $, 2005 Constant Prices)_ lnGDPPCPenn


Year lnensexp Rank Lniqi Rank lncger Rank lnpcrd
bofgdp


Rank lnwavg Rank


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


1995
0.01***
(.006) 12


0.059***
(.03) 12


0.529*
(.15) 13


0.037
(.03) 3


-0.020**
(.009) 6


1996
0.011**
(.005) 13


0.064**
(.027) 13


0.495*
(.148) 12


0.037
(.03) 2


-0.019**
(.009) 10


1997
0.009**
(.003) 11


0.04***
(.023) 8


0.48*
(.132) 11


0.036
(.032) 1


-0.010
(.01) 13


1998
0.006***


(.003) 10
0.02
(.03) 1


0.455*
(.116) 7


0.039
(.03) 4


-0.024*
(.008) 3


1999
0.006*
(.003) 8


0.020
(.025) 2


0.443*
(.109) 5


0.069*
(.027) 9


-0.024*
(.008) 5


2000
0.003
(.003) 1


0.026
(.025) 4


0.456*
(.151) 8


0.063**
(.032) 8


-0.024*
(.005) 4


2001
0.005
(.003) 6


0.026
(.017) 3


0.451*
(.154) 6


0.059**
(.031) 7


-0.027*
(.007) 1


2002
0.004
(.004) 4


0.030
(.024) 5


0.459*
(.147) 10


0.055**
(.029) 6


-0.026*
(.007) 2


2003
0.003
(.003) 2


0.050**
(.025) 10


0.456*
(.171) 9


0.079**
(.032) 13


-0.020**
(.007) 9


2004
0.004
(.004) 3


0.053**
(.025) 11


0.422*
(.148) 1


0.074**
(.032) 12


-0.017**
(.007) 12


2005
0.004
(.004) 5


0.042**
(.018) 9


0.441*
(.15) 4


0.072*
(.028) 11


-0.020**
(.009) 7


2006
0.006
(.003) 9


0.037**
(.018) 6


0.423*
(.151) 2


0.070**
(.03) 10


-0.019**
(.008) 11


2007
0.005
(.004) 7


0.038
(.026) 7


0.427*
(.128) 3


0.051
(.032) 5


-0.020**
(.008) 8



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.
Higher rank indicates higher absolute value of the estimates.







42


Table 10: Extended Model: Nonparametric Median Estimates by Region



Table 10.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lncnsexp lniqi lncger lnpcrdbofgdp lnwavg


Asia -0.031** (.014)
0.084*
(.024)


0.987*
(.061)


0.188*
(.030)


-0.042*
(.010)


Americas
-0.007
(.015)


0.537*
(.067)


0.755*
(.129)


0.074*
(.014)


-0.067*
(.006)


Africa
0.010*
(.011)


0.101*
(.024)


0.518*
(.061)


0.275*
(.038)


-0.007
(.005)





Table 10.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lndnsexp lniqi lncger lnpcrdbofgdp lnwavg


Asia -0.018*** (.01)
-0.044*
(.011)


0.718*
(.055)


0.142*
(.013)


-0.055*
(.004)


Americas
-0.001
(.003)


0.266*
(.032)


0.647*
(.05)


0.034*
(.004)


-0.023*
(.002)


Africa
0.010*
(.001)


0.050*
(.005)


0.291*
(.05)


0.000
(.015)


0.005**
(.002)





Table 10.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lnensexp lniqi lncger lnpcrdbofgdp lnwavg


Asia 0.009 (.005)
-0.062*
(.011)


0.971*
(.054)


0.077*
(.012)


-0.064*
(.010)


Americas
0.007*
(.002)


0.168*
(.049)


0.561*
(.078)


0.030*
(.003)


-0.025*
(.002)


Africa
0.002
(.001)


0.048*
(.007)


0.291*
(.017)


0.096*
(.023)


-0.001
(.001)



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.






43


Table 11: Extended Model: Nonparametric Median Estimates by Emerging Country Group



Table 11.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lncnsexp lniqi lncger lnpcrdbofgdp lnwavg


South
0.010
(.008)


0.173*
(.028)


0.523*
(.046)


0.217*
(.019)


-0.031*
(.004)


Emerging
South


-0.055*
(.017)


0.227*
(.040)


1.260*
(.069)


0.105*
(.011)


-0.038*
(.003)





Table 11.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lndnsexp lniqi lncger lnpcrdbofgdp lnwavg


South
-0.003***


(.001)
0.068*
(.01)


0.452*
(.033)


0.069*
(.014)


0.003
(.002)


Emerging
South


0.033*
(.009)


0.046*
(.011)


0.686*
(.087)


0.039*
(.009)


-0.054*
(.001)





Table 11.c: High Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lnensexp lniqi lncger lnpcrdbofgdp lnwavg


South
0.003*
(.000)


0.033*
(.008)


0.424*
(.024)


0.060*
(.015)


-0.004***
(.002)


Emerging
South


0.010*
(.001)


0.042*
(.014)


0.672*
(.149)


0.038*
(.018)


-0.047*
(.004)



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.





44


Table 12: Extended Model: Nonparametric Median Estimates by Income Group



Table 12.a: Low Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lncnsexp lniqi lncger lnpcrdbofgdp lnwavg


Non-ldcsids
-0.031*
(.010)


0.298*
(.031)


0.856*
(.074)


0.129*
(.010)


-0.052*
(.004)


ldcsids
0.024**
(.010)


0.065*
(.022)


0.560*
(.07)


0.276*
(.044)


-0.009
(.007)





Table 12.b: Medium Skill- and Technology-Intensive Manufactures


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lndnsexp lniqi lncger lnpcrdbofgdp lnwavg


Non-ldcsids
0.004
(.003)


0.086*
(.010)


0.414*
(.044)


0.056*
(.008)


-0.030*
(.005)


ldcsids
0.002
(.002)


0.025**
(.010)


0.510*
(.045)


0.071**
(.031)


0.007*
(.001)





Table 12.c: High Skill- and Technology-Intensive Manufactures by ldcid


Dependent variable: GDP per capita
(international $, 2005 Constant Prices)_ lnGDPPCPenn




lnensexp lniqi lncger lnpcrdbofgdp lnwavg


Non-ldcsids


0.005*


(.001)
0.064*
(.009)


0.547*
(.065)


0.066*
(.014)


-0.034*
(.003)


ldcsids
0.006*
(.002)


0.004
(.011)


0.426*
(.022)


0.038
(.026)


0.003**
(.001)



Notes: Standard errors are in parentheses.
Standard errors of nonparametric estimates are obtained from bootstrapping (seed 10101)
* significant at 1% level, ** significant at 5% level, *** significant at 10% level.







45



Annex tables 




Table A1. List of countries in sample


CCode Country Region Group Income Group
AFG Afghanistan* Asia South ldcsids
ARG Argentina Americas Emerging South Non-ldcsids
BHS Bahamas, The Americas South ldcsids
BGD Bangladesh Asia South ldcsids
BLZ Belize Americas South Non-ldcsids
BEN Benin Africa South ldcsids
BTN Bhutan Asia South ldcsids
BOL Bolivia, Plurinational State of Americas South Non-ldcsids
BRA Brazil Americas Emerging South Non-ldcsids
BFA Burkina Faso Africa South ldcsids
BDI Burundi* Africa South ldcsids
KHM Cambodia Asia South ldcsids
CMR Cameroon Africa South Non-ldcsids
CPV Cape Verde* Africa South ldcsids
CAF Central African Republic Africa South ldcsids
TCD Chad Africa South ldcsids
CHL Chile Americas Emerging South Non-ldcsids
CHN China* Asia Emerging South Non-ldcsids
COL Colombia Americas Emerging South Non-ldcsids
COM Comoros* Africa South ldcsids
CRI Costa Rica Americas South Non-ldcsids
CIV Côte d’Ivoire Africa South Non-ldcsids
CUB Cuba* Americas South Non-ldcsids
DJI Djibouti* Africa South ldcsids
DMA Dominica* Americas South ldcsids
DOM Dominican Republic Americas Emerging South Non-ldcsids
EGY Egypt Africa Emerging South Non-ldcsids
SLV El Salvador Americas South Non-ldcsids
ERI Eritrea* Africa South ldcsids
ETH Ethiopia* Africa South ldcsids
FJI Fiji Asia South ldcsids
GMB Gambia, The Africa South ldcsids
GHA Ghana Africa South Non-ldcsids
GRD Grenada* Americas South ldcsids
GTM Guatemala Americas South Non-ldcsids
GIN Guinea* Africa South ldcsids
GNB Guinea-Bissau* Africa South ldcsids
GUY Guyana Americas South Non-ldcsids
HND Honduras Americas South Non-ldcsids
IND India Asia Emerging South Non-ldcsids
IDN Indonesia Asia Emerging South Non-ldcsids
JAM Jamaica Americas South ldcsids
JOR Jordan Asia Emerging South Non-ldcsids
KEN Kenya Africa Emerging South Non-ldcsids
KOR Korea, Republic of Asia Emerging South Non-ldcsids
LAO Lao People’s Dem. Rep. Asia South ldcsids
LBN Lebanon* Asia Emerging South Non-ldcsids





46


CCode Country Region Group Income Group
LSO Lesotho* Africa South ldcsids
LBR Liberia* Africa South ldcsids
MDG Madagascar Africa South ldcsids
MWI Malawi Africa South ldcsids
MYS Malaysia Asia Emerging South Non-ldcsids
MLI Mali Africa South ldcsids
MUS Mauritius Africa South ldcsids
MEX Mexico Americas Emerging South Non-ldcsids
MNG Mongolia Asia South Non-ldcsids
MAR Morocco Africa Emerging South Non-ldcsids
MOZ Mozambique Africa South ldcsids
NAM Namibia* Africa South Non-ldcsids
NPL Nepal Asia South ldcsids
NIC Nicaragua Americas South Non-ldcsids
NER Niger Africa South ldcsids
PAK Pakistan Asia Emerging South Non-ldcsids
PAN Panama Americas South Non-ldcsids
PRY Paraguay Americas South Non-ldcsids
PER Peru Americas Emerging South Non-ldcsids
PHL Philippines Asia Emerging South Non-ldcsids
RWA Rwanda Africa South ldcsids
KNA Saint Kitts and Nevis* Americas South ldcsids


VCT
Saint Vincent and the
Grenadines* Americas South ldcsids


WSM Samoa Asia South ldcsids
STP Sao Tome and Principe* Africa South ldcsids
SEN Senegal Africa South ldcsids
SYC Seychelles* Africa South ldcsids
SLB Solomon Islands* Asia South ldcsids
ZAF South Africa Africa Emerging South Non-ldcsids
LKA Sri Lanka Asia South Non-ldcsids
SUR Suriname Americas South Non-ldcsids
TZA Tanzania, United Republic of Africa South ldcsids
TGO Togo Africa South ldcsids
TON Tonga* Asia South ldcsids
TUN Tunisia Africa Emerging South Non-ldcsids
TUR Turkey Asia Emerging South Non-ldcsids
UGA Uganda Africa South ldcsids
URY Uruguay Americas Emerging South Non-ldcsids
VNM Viet Nam Asia Emerging South Non-ldcsids
ZMB Zambia Africa South ldcsids
ZWE Zimbabwe* Africa South Non-ldcsids



Note: ldcsids: Least developed countries and small island developing States.
* not included in the extended model

Source: United Nations






47


Table A2. Description and sources of variables


Variable/code Description Source
GDPPCpenn GDP per capita (international $, 2005


Constant Prices, Chain series)


PWT 6.3, Center for International
Comparisons of Production, Income
and Prices at the University of
Pennsylvania


CNSEXP Share of low skill- and technology-intensive
manufactures as a percentage of total
merchandise exports


UN COMTRADE HS 4-digit,
processed by UNCTAD


DNSEXP Share of medium skill- and technology-
intensive manufactures as a percentage of
total merchandise exports


UN COMTRADE HS 4-digit,
processed by UNCTAD


ENSEXP Share of high skill- and technology-
intensive manufactures as a percentage of
total merchandise exports


UN COMTRADE HS 4-digit,
processed by UNCTAD


IQI Institutional Quality Index
i. Economic IQI Legal and property rights Economic Freedom Index dataset
Law and order PRS Group ICRG database
Bureaucratic quality PRS Group ICRG database
Corruption PRS Group ICRG database
Democratic accountability PRS Group ICRG database
Government stability PRS Group ICRG database
Independent judiciary POLCON Henisz Dataset
Regulation Economic Freedom Index dataset
ii. Social IQI Press freedom Economic Freedom Index dataset
Civil liberties Economic Freedom Index dataset
Physical integrity index CIRI Human Rights Data Project
Empowerment rights index CIRI Human Rights Data Project
Freedom of association CIRI Human Rights Data Project
Women’s political rights CIRI Human Rights Data Project
Women’s economic rights CIRI Human Rights Data Project
Women’s social rights CIRI Human Rights Data Project
iii. Political IQI Executive constraint Polity IV Project
Political rights Economic Freedom Index dataset
Index of democracy PRIO Dataset
Polity score Polity IV Project
Lower legislative POLCON Henisz Dataset
Upper legislative POLCON Henisz Dataset
Independent sub-federal units POLCON Henisz Dataset
CGER Combined gross enrolment ratio UNESCO Education Database
PCRDBOFGDP Private credit by deposit money banks and


other financial institutions as a percent of
GDP


World Bank Financial Structure
Dataset, World Bank 2009


WAVG Average of effectively applied rates by
trading partners weighted by the total
imports of trading partner countries


UNCTAD Trade Analysis and
Information System (TRAINS)
Database



Note: All variables are converted in logs, denoted by “ln” in the text, tables and figures.








49


UNCTAD Study Series on


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51


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geography, 2008, 50 p.

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UNCTAD Study series on


POLICY ISSUES IN INTERNATIONAL TRADE
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(Study series no. 48: Export structure and economic performance in developing countries:
Evidence from nonparametric methodology)


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