Download 1 THE DETERMINANTS OF INDUSTRIALIZATION IN DEVELOPING

THE DETERMINANTS OF INDUSTRIALIZATION IN DEVELOPING COUNTRIES, 1960-20051
Authors
Francesca Guadagno, UNU-MERIT and Maastricht University, [email protected]
Abstract
This paper goes back to the Cornwall (1977) model for explaining the role of manufacturing in
economic growth and estimates the equation of manufacturing growth. It contributes to the
literature on the hypothesis of manufacturing as an engine of growth by an empirical analysis of the
determinants of industrialization in 74 countries for the period 1960-2005. Results show that
industrialization is faster for larger countries with an undeveloped industrial base and development
strategies based on trade openness, undervaluation, skills and knowledge accumulation. In
particular, while from 1970 to the mid 90s technological backwardness and undervaluation were
the main drivers of industrialization, since 1995 investments in knowledge accumulation have
become increasingly crucial. Some robustness checks corroborate the validity of these results.
Key Words:
Structural Change, Manufacturing, Innovation, Development
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This paper is part of my PhD Dissertation. I thank my supervisor, prof. Bart Verspagen, for guidance and
suggestions and prof. Jan Fagerberg for comments on a previous version.
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1 INTRODUCTION
It is widely recognized that industrialization, intended as the shift from agriculture to manufacturing,
is key to development: hardly any countries have developed without industrializing. This
phenomenon has been so striking to induce some economists to hypothesize that the
manufacturing sector is the engine of economic growth, the so-called “engine of growth argument”
(Kaldor, 1967; Cornwall, 1977).
The debate is quite old and seems outdated if one thinks about the recent success in the service
sector. Services have increased their shares in GDP in both developed and developing countries
and are increasingly seen as the new engine of growth. In developing countries, the share of
services in GDP was already 40% in the 1950s (well higher than the one of manufacturing, 11%)
and increased up to 51% in 2005. In advanced economies, the share of services increased even
more from the 50s to 2005, going from 43% to 70% (Szirmai, 2011). The Indian case is often
brought as an example of successful service-led growth. In India, not only the services sector
greatly contributes to GDP, but distinguishes itself for its dynamism since modern services
(computer, business, and financial services) are the fastest growing category.
The recent economic crisis, coupled with the considerable expansion of the financial service
sector, and the difficulties that many developing countries still encounter to industrialize, brought
manufacturing back in the spotlight. Policy makers in both developed and developing countries are
reconsidering the virtues of manufacturing. Recent empirical work applied cross country and panel
data analysis and found general support to the hypothesis of manufacturing as an engine of growth
(among the most recent, Rodrik, 2008; Fagerberg and Verspagen, 1999, 2002; Szirmai, 2011;
Szirmai and Verpagen, 2011).
This paper goes back to the Cornwall (1977) model for explaining the role of manufacturing in
economic growth and estimates the equation of manufacturing growth. By applying panel data
analysis, it looks at the determinants of industrialization in a large sample of developed and
developing countries from 1960 to 2005. In this way, it contributes to the structural approach in
development economics and to the recent empirical analysis on industrialization and growth. By
introducing technical change as an explanatory variable, it also contributes to the evolutionary
approach to innovation and development.
In the next section, we briefly review the literature on the role of industrialization for development,
the relationship between technical change and economic growth, and the factors behind
industrialization. The dataset is described in section 3 where some descriptive statistics are also
presented. Section 4 discusses the results of analysis of the determinants of industrialization. In
the following section, we expand upon these findings by investigating the role of interdependencies
and innovation systems and the evolution of the determinants over different periods. Some
robustness checks are reported in section 6. Section 7 briefly concludes.
2 LITERATURE REVIEW AND APPROACH
2.1
Literature review
The term industrialization refers to the structural change that backward countries experience in
their development process from an agricultural to an industrial economy, with the profound
changes in the society that this entails (Kuznets, 1973). Old development economists observed
structural differences between key sectors of the economy and developed models of dual economy
with a high productivity, ‘capitalist’, and a low productivity, ‘subsistence’, sector. In this view,
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development is conditional on the movement of labour to the capitalist sector. In fact, beyond
productivity, the manufacturing sector is characterized by economies of scale that, by reducing unit
costs, allow increasing output. In turn, the growth of the output of the manufacturing sector impacts
the growth rate of the economy via backward and forward linkages to non-manufacturing
productivity. Moreover, the manufacturing sector comprises the technology sector, i.e. the sector
responsible for the introduction of innovation (Lewis, 1954; Fei and Ranis, 1964; Cornwall, 1977).
Building upon these theories, the technology gap literature, and the studies of the ‘patterns of
industrialization’ approach, Cornwall (1977) formulated a model for explaining the role of growth of
manufacturing output. Two main equations composed the model:
The first equation explains the output growth in the manufacturing sector and the second the
aggregate output growth rate. That the growth of output depends on the rate of growth of
, is reflected in the coefficient, e1, which is exactly the measure of the
manufacturing output,
power of manufacturing as an engine of growth. The determinants of the growth rate of
, are the level and growth rate of aggregate income, income relative to
manufacturing output,
the most developed economies, and investment. The level of income is introduced to take into
consideration that, when per capita income rises, consumption shifts from goods to service. A
feedback from demand growth is introduced via the income growth rate. The ratio of per capita
income compared with that of high-income countries captures the size of the technology gap: the
larger the gap with the technological frontier, the greater the amount of technology that an
industrializing country can borrow, and so the higher the rate of industrialization. Investments
measure the efforts to develop borrowed and indigenous technologies. Estimations of this model
for the market economies revealed the importance of the manufacturing sector, flexibility (the
ability of the economy to shift towards manufacturing activities), and investments.
Most recent empirical work partially confirmed the engine of growth hypothesis (Tregenna, 2007;
Kuturia and Raj, 2009; Szirmai, 2009; Timmer and de Vries, 2009). On the one hand, in developing
countries, manufacturing is still relevant as an engine of growth when the most dynamic industries
are targeted and investments in skills are undertaken. On the other hand, in developed countries
manufacturing is not anymore an engine of growth (Fagerberg and Verspagen, 1999; Szirmai and
Verspagen, 2011).
Fagerberg and Verspagen (1999) updated and extended upon the work of Cornwall (1977) by
using a larger sample of countries for the period 1973-1990. They find that, in market economies,
the effect of manufacturing on growth had vanished; newly-industrializing countries are instead
those that benefit more from expansions of the manufacturing sector. They test for the importance
of the most dynamic segments of the manufacturing sector (as theorized by Cornwall) and confirm
the importance of flexibility to shift towards manufacturing productions. Szirmai and Verspagen
(2011) study 88 countries for the period 1950-2005 and find that manufacturing had a direct effect
on growth only from 1970 to 1990. Since 1990, the positive effect of manufacturing on growth in
developing countries has become more and more dependent on skills’ accumulation.
These contributions refer implicitly or explicitly to technical change. What has been said about the
relationship between technical change and economic growth?
For a quite long period in economic theory, technical change was conceived as “any kind of shift in
the production function” (Solow, 1957). Thanks to the vagueness of this idea, technological
progress could be called “residual”, i.e. what is left of GDP growth after accounting for labour and
capital growth (total factor productivity), or “measure of our ignorance”. Solow and the other
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economists who contributed to the field of neoclassical growth theory introduced it as an
exogenous variable and did not spend much effort into opening this “black box”.
Following these theories, technological progress was commonly viewed as a typical first-world
activity that has to do with developing new solutions via the exploitation of advanced knowledge
and techniques. So, in an international perspective, what backward countries need in order to
(more or less automatically) converge is accumulating capital. Due to the nature of public good of
knowledge, the process of acquisition of capital (and so of technology and knowledge attached to
it) was regarded as essentially automatic (Veblen, 1915).
However, if it is so automatic, why do economists observe divergence rather than converge
between developed and developing countries?
Notwithstanding the availability of advanced machines and techniques, technology transfer is still
burdensome as it requires investments in physical, financial, and institutional infrastructure
(Gerschenkron, 1962) and efforts to build different types of capabilities (e.g. social capabilities by
Abramovitz, 1986; absorptive capacity by Cohen and Levinthal, 1990; technological capabilities by
Kim, 1980 and Lall, 1992).
Nelson and Pack (1999) develop a model to explain the so-called “Asian miracle”. According to
their analysis, high physical investments and initial conditions are not enough to explain Asian
performance. Absorption of modern technologies and structural change were critical ingredients of
the Asian development process. According to the authors, such technological effort does not take
the form of formal R&D, but rather of “efforts of firm to learn about new opportunities, improve
organization and inventory management, and undertake minor bur cumulatively significant
changes in production processes” (p. 431). The former quote demonstrates how hard it is to
capture technological change.
Innovation is so complex that some scholars describe it as a systemic phenomenon determined by
the interactions of a wide range of factors (geographical, social, historical, political, and so on). The
coevolution of all these factors shape the evolution of capabilities and the chances of successful
innovative performances of countries, regions, and sectors (the so-called system of innovation
approach, as firstly theorized by Freeman, 1987; Nelson, 1993, Lundvall, 1993). At the empirical
level, a great deal of studies tested the role of innovation and innovation system for growth and
development.2
Among the others, Fagerberg and Srholec (2008) explored 115 countries in two periods, 19921994 and 2002-2004, and identified 4 main factors capturing different aspects of capabilities:
innovation system, governance, political system, and openness. Results demonstrate that catching
up is positively affected only by developed innovation systems and good governance, while
openness to trade, FDI ,and the political system do not matter much for development.
2.2
Approach
As the literature review has pointed out, research focused on the relationship between growth and
industrialization, but why do some countries industrialize and some others do not? And, which is
the role of technical change in explaining differences in industrialization? Moreover, if innovation
has a systemic nature, does the role of innovation depend on the interplay of different factors and
characteristics of the country in question? Finally, the global scenario in which countries
industrialize is constantly changing. How did the determinants of industrialization change over
time? In other words, how do developing countries need to adapt their development strategies in
order to meet the evolving conditions for industrialization?
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For an extensive review of the literature about innovation and development, see Fagerberg et al. (2010).
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Based upon the insights of the literature review, we will try to answer these questions by assuming
that industrialization, i.e. the expansion of the manufacturing sector, depends on the installed
manufacturing base, labour costs, trade openness, exchange rate movements, institutions,
absorptive capacity, and technological capability. The size of the domestic market, GDP per capita
as a percentage of US GDP, inflation, terms of trade, and geographical and resource related
variables are introduced as exogenous control variables. The dependent variable, industrialization,
is measured as the first difference of the share of manufacturing in GDP at current prices.
As shown in the previous section, in the Cornwall’s model, the growth rate of manufacturing output
is explained by level and growth rate of aggregate income, income relative to the most developed
economies, and investment. When estimating, Cornwall discusses the fact that the simultaneous
inclusion of variables containing income created collinearity and opts for a model specification
where only the income relative to the most developed economy (US) is present. In our model, we
will follow the same route. As far as investments are concerned, we decided to omit this variable:
investments are endogenous to the shares of manufacturing and would capture the same idea as
our dependent variable. For this reason, investment is accounted for by the variables that would
drive them in the first place: GDP growth, macroeconomic policies (as reflected by inflation and
exchange rates), trade (terms of trade and trade orientations) and well-functioning institutions that
guarantee appropriability of returns and lower risks associated with contract incompleteness and
enforcements.
The income relative to the US and the lagged value of manufacturing capture catching up or
cumulativeness in the industrialization process (respectively negative or positive signs). Income
relative to the US can also be seen as a proxy of technological imitation. Moreover, because it
reflects GDP trends, it captures that feedback effect from economic growth that Cornwall inserted
in his model.
With respect to openness, a very long tradition has linked trade to development. Despite
recognizing the role of demand and technology in industrialization, Chenery et al. (1986) mainly
focused on trade and empirically showed that open development strategies, based on
manufactured exports, lead to fast growth of exports and rapid rates of structural change. This trust
in a positive net effect of industrialization on the balance of trade was not shared by all economists
studying structural change. Economists from ECLAC were (and are) convinced that import
substitution and protection of domestic infant industries are the best strategies to industrialize.
Developing countries’ terms of trade would decline if their structural change would make them
specialize in primary commodities and resource intensive industries, sectors for which developing
countries have comparative advantages (Prebish, 1950; Singer, 1950). More recently, Cimoli and
Katz (2003) reviewed the industrial history of Latin America and concluded that liberalization and
openness in the 80s killed the progresses achieved by inward-oriented policies from the 30s to the
80s especially in technology intensive sectors.
As far as undervaluation is concerned, we follow and expand upon the work by Rodrik (2008)
where he looks at the relationship between the real exchange rate, growth, and industrialization
and concludes that the exchange rate affects economic growth via the expansion of the industrial
sector. Undervaluation, by making the price of tradable goods higher relative to that of non
tradable, encourages the transfer of resources towards the more profitable tradable sector. Since
the tradable sector is mainly made of industrial activities, the effect of the real exchange rate on
growth is, at least partly, channelled by industrialization. Following his work, we expect a positive
and significant coefficient on undervaluation.
Labour costs are a widespread measure of international competitiveness as higher labour costs
make exports more expensive relatively to import, and so slow down economic growth.
Nevertheless, according to the so-called Kaldor paradox (Kaldor, 1978) countries with the fastest
growth in terms of GDP and exports are characterized by faster growth in labour costs. Empirical
studies showed that price competitiveness per se is not the major determinant of international
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competitiveness in the long run (Fagerberg, 1988; Amendola et al., 1993; Fagerberg et al., 2007).
Depending on countries’ industrial specialization, low wages might even be linked to low
productivity, and so to lower competitiveness and growth. Even though the sign on the wage
variable might vary across sectors, overall the effect of labour costs is expected to be negative
(Amable and Verspagen, 1995).
In their work on the hypothesis of manufacturing as an engine of growth, Fagerberg and
Verspagen (1999) and Szirmai and Verspagen (2011) introduce respectively education and R&D
expenditures and education alone to account for human capital accumulation and ‘innovation
systems’ characteristics. We conform to these studies and expect a positive effect of education
and technical change on industrialization.
While there is large consensus that institutions matter for growth, this paper tests whether
institutions impact on growth via industrialization. Institutions have often come to embrace a mare
magnum of concepts. Nelson and Sampat (2001) stress the need for a definition that would make
sense of them as a factor shaping economic growth. Fagerberg and Schrolec (2008) distinguish
between political system and “quality of governance” variables. The former relates to measures of
democracy and rights; the latter to aspects like the easiness of starting and conducting a business.
Their results show that good governance matters for growth, while political systems matter only for
richer countries. Even though we are deeply persuaded by the idea that institutions are to be
accurately defined in relation to the specific phenomena they are meant to explain and that good
governance contributes more than democratic political systems, we are confronted with the fact
that these indicators are not available for long time series. So, in order to preserve the length of our
panel, we chose to rely on political systems variables.
Geographical and resource-related variables are the classic instruments in empirical studies on
institutions and growth (e.g., Acemoglu et al., 2001; Dollar and Kraay, 2003; Lee and Kim, 2009).
While economic growth theory has focussed on physical and human capital accumulation and
technical change, empirical studies argued that these are at best proximate causes of economic
growth. This strand of literature identifies trade, institutions, and geography as deeper
determinants of economic growth. However, if institutions and trade are endogenous, insomuch as
they are mutually shaped and in turn influenced by economic growth, geography is an exogenous
determinant of economic growth. Its relation with growth is both direct, because it influences
agricultural productivity, health conditions, and resource endowments, and indirect via the effect on
trade and institutions (Rodrik et al., 2004).
By creating uncertainty, volatile inflation and terms of trade influence investment choices.
Theoretical literature has studied the impact of uncertainty on growth, as channelled by
investments. In empirical studies, Fagerberg and Verspagen (1999) introduced inflation as an
instrumental variable and Rodrik (2008) used both inflation and terms of trade as additional
exogenous covariates of economic growth when the role of manufacturing is analysed. Rodrik finds
a negative and significant relationship between growth and inflation in developing countries.
Kosakoff and Ramos (2009) described the impact of inflation on the manufacturing sector in
Argentina and how investment decisions and capability accumulation of firms are hampered in
high-inflationary contexts.
3 OVERVIEW OF THE DATA
3.1
Description of the dataset
Data on the share of manufacturing in GDP, population, openness, human capital, and geography
come from the Szirmai and Verspagen database (Szirmai and Verspagen, 2011). In particular, the
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size of the market (POP) is captured by the logarithm of the population; trade policies by the
openness index (exports plus imports as percentage of GDP), and human capital (EDU) by the
average years of schooling for the population of above 15 years of age. Two variables capture
geography: OIL is a dummy variable that indicates the presence of oil in the country and
KGATEMP refers to the climatic zone and is measured as the percentage of land in a temperate
climatic zone, transformed in a binary variable.
Data on wages (WAGE) come from the UNIDO Industrial Statistics Database (INDSTAT2 2011
ISIC Rev.3).3 Exchange rate movements are represented by the index UNDERV, which is built by
following Rodrik (2008) and using the Penn World Table (version 7.0, update June 2011). This
index, taken in logarithmic form, is positive when the currency is undervalued. Data on the terms of
trade (TOT) also come from the PWT 7.0. For inflation (INFL) we rely on WDI (2011).4
Our preferred measure of institutions (DEM) is the Vanhanen index (Vanhanen, 2000), that
compared to other measures, uses indicators taken from quantitative data and not on subjective
evaluations, as the Polity and Freedom House data. The index is built in such a way that low
values correspond to low levels of democracy. Data come from the Quality of Government Dataset
(version of 11/04/2011).
As measures of technical change, we use indicators based on patents and R&D expenditures.
Patent data come from USPTO and WIPO. Data on R&D expenditures over GDP come from the
CANA dataset (Castellacci and Natera, 2011).5 In a first stage, we rely on the number of patents
per capita at USPTO (PATPC). USPTO patents are the most widely used proxy in the literature,
due to their clear advantage in terms of data availability and cross-country comparability. However,
the high degree of novelty required by the USPTO does not match with our idea of innovation effort
in developing countries. This is why, in a second stage, we explore other indicators: the number of
patent per capita granted by national patents’ offices to residents (NATPATPC), R&D expenditures
as a percentage of GDP (R&D) and a measure of technological level developed by Fagerberg
(1988). This measure of technological level (TL) is attractive insofar as it is constructed as the
average of both patents and R&D expenditures (weighted by their standard deviations). National
patent offices’ criteria to grant a patent are much less stringent than USPTO’s, so, by using
national patents, a much broader range of innovations would be captured.
As for R&D, data for most of the developing countries start in 1980, so the length of the panel is
strongly damaged by the introduction of this variable. Notwithstanding this downside, R&D intensity
is a very good indicator of innovation especially for developing countries which might need a
lengthy process of knowledge accumulation and capability building before filing a patent
application. In fact, even though the correlation of patents and R&D expenditures is very high (in
agreement with the literature), it is lower for less developed countries.6
3
Values of wage and salaries (at current prices) have been divided by employment (number of persons
engaged and number of employees).
4
Gaps have been filled in with the IMF WEO dataset (version of 1999 and 2011) and in the case of Chile
and UK with national data sources (respectively, Banco Centrale de Chile and Office of National Statistics).
5
WIPO data start in 1965 and do not include some countries among which Taiwan. Data on R&D
expenditures for Taiwan and Korea were retrieved from other sources (OECD; Taiwan National Science
Council; Lederman and Saenz, 2005).
6
The correlation between USPTO patents and R&D expenditures as a percentage of GDP is 0.86 for the
whole sample but for African countries it decreases to 0.62, and for Latin America to 0.31. The same is true
for national offices’ patents: the overall correlation between USPTO and national offices’ patents is 0.74
overall, for Africa is 0.84, but for Asia is around 0.6, and for Latin America 0.2.
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The resulting dataset is an unbalanced panel of 74 (developed and developing) countries covering
the period 1960-2004. Yearly data have been averaged into 9 5-year periods. The sample varies
per period, depending on data availability.
3.2
Descriptive statistics
Before delving into the econometric analysis, it is useful to look at the descriptive statistics of our
data. This will guide the choice of the type of model to adopt.
Table 1. Descriptive statistics
Variable
Mean
Standard Deviation
Observations
Overall
Between
Within
N
n
T-bar
First difference of the
Manufacturing share in GDP
0.099
2.99
1.32
2.72
650
91
7.14
Lagged value of the
Manufacturing share in GDP
18.12
8.32
7.49
4.06
681
91
7.48
Wage (ln)
-1.12
1.25
1.03
0.78
561
80
7.01
Population (ln)
9.29
1.66
1.65
028
810
91
8.9
Openness
65.77
42.85
37.89
20.07
787
91
8.65
Undervaluation Index
0.008
0.296
0.066
0.287
702
85
8.26
Democracy Index
14.36
13.41
12.29
5.51
748
89
8.4
Education
5.622
2.793
2.477
1.378
737
85
8.7
GDP per capita as a percentage of
US GDP
0.314
.282
0.277
0.065
734
85
8.6
Terms of trade (ln)
0.002
0.103
0.083
0.068
718
85
8.45
Inflation (ln)
2.004
1.25
0.83
0.96
652
83
7.85
USPTO patents per capita
0.015
0.04
0.037
0.017
776
90
8.62
National offices’ patents per capita
0.0522
0.111
0.084
0.062
528
78
6.77
R&D as a percentage of GDP
0.844
0.902
0.884
0.257
309
64
4.82
Depreciated stock of USPTO
patents
0.091
0.283
0.27
0.09
753
84
8.96
The table shows that the within component of the standard deviation of the dependent variable, the
first difference of the manufacturing share in GDP, is larger than the between component. The
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opposite is true for all the explanatory variables but inflation and undervaluation (these variables
are generally characterized by high volatility).
Following the reasoning in Szirmai and Verspagen (2011), we would rather not rely on a fixed
effect model that wipes out between effects because the highest degree of variation in our data
comes from the between rather than the within component. Moreover, if the objective of this
analysis is to understand why some countries industrialized and some others did not it is clear that
our interest lies more in between countries variation rather than within country variation.
Relying on USPTO patent data would be the most conservative choice as its average is the lowest
of the proxies of technological efforts. Nevertheless, a clear reason to prefer USPTO data relates
to data availability (see number of countries and average number of observations per country, Tbar).
4 RESULTS
Our econometric analysis starts with fixed and random effects, between and Hausman and Taylor
(1981) estimations. Results are reported in table 2. Following Baltagi et al. (2003), Jacob and
Osang (2007) and Szirmai and Verspagen (2011), we separately inspected each single
explanatory variable by means of Hausman tests (not reported here) in order to identify which
variables are endogenous. Tests showed that ManL1 and WAGE are endogenous. The dependent
variable is the first difference of the share of manufacturing in GDP.
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Table 2. Determinants of Industrialization, 1960-2005
Fixed Effects
Random Effects
Between
Hausman and Taylor
coef
se
sig
coef
se
sig
coef
se
manL1#
-0.355
0.050
***
-0.157
0.028
***
-0.017
0.043
WAGE#
0.112
0.402
-0.130
0.291
-0.986
0.398
UNDERV
1.038
0.846
0.478
0.718
-1.580
1.545
OPEN
0.034
0.011
coef
se
sig
-0.317
0.032
***
0.081
0.357
1.270
0.626
**
0.022
0.006
0.016
0.008
0.032
0.008
***
DEM
-0.029
0.036
-0.025
0.021
-0.050
0.031
-0.023
0.027
RELUS
1.302
3.014
-2.590
1.678
-0.861
2.155
-0.991
2.360
EDU
0.264
0.386
0.141
0.098
0.066
0.128
0.312
0.203
PATPC
0.858
10.782
-0.259
4.791
0.000
0.000
0.184
7.474
POP
3.022
1.455
0.458
0.147
0.223
0.185
0.989
0.359
TOT
3.038
2.600
1.929
1.536
-3.145
2.977
2.981
2.005
INFL
0.277
0.167
0.017
0.176
0.202
0.238
0.258
0.144
KGATEMP
1.289
0.483
1.409
0.596
1.084
1.415
OIL
0.491
0.642
0.224
0.799
-0.203
2.289
0.040
0.564
4.026
4.921
-0.327
0.534
***
**
***
***
***
sig
**
**
**
***
*
D65-70
-0.551
0.614
D70-75
-2.818
0.684
***
-1.575
0.627
**
3.669
3.805
-2.408
0.622
***
D75-80
-3.699
1.078
***
-1.756
0.736
**
5.569
4.469
-2.944
0.765
***
D80-85
-4.495
1.344
***
-2.096
0.810
***
-1.990
5.377
-3.583
0.896
***
D85-90
-4.535
1.603
***
-1.898
0.799
**
3.852
4.954
-3.559
0.986
***
D90-95
-4.807
1.854
**
-1.942
0.846
**
2.764
2.983
-3.666
1.105
***
D95-00
-6.420
2.094
***
-3.389
0.840
***
3.695
2.976
-5.121
1.184
***
D00-05
-6.408
2.299
***
-2.991
0.895
***
1.001
3.019
-5.013
1.301
***
Constant
-22.095
12.771
*
-0.972
1.782
-6.470
3.402
-4.074
3.543
Rho
0.749
0.084
Obs
438
438
438
438
Countries
74
74
74
74
R2 within
0.308
0.263
0.002
R2 between
0.041
0.355
0.589
R2 overall
0.059
0.284
0.050
*
0.799
Legend: * p<0.10, **p<0.05, *** p<0.010.
Note: Standard errors for fixed and random effects are robust (adjusted for clusters).
The # indicates the variables that were treated as endogenous in Hausman-Taylor.
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Lagged manufacturing, size of domestic market, and trade openness are clear determinants of
industrialization. WAGE is positive but not significant in fixed effects and Hausman and Taylor and
becomes negative and significant in the between specification. Undervaluation and inflation are
positive and significant only in the Hausman and Taylor estimation. Democracy is always negative
and never significant. USPTO patents, education, and RELUS are never significant and sometimes
have unexpected signs. KGATEMP is positive and significant only in the random effects and
between specifications. All period dummies but the first are significant in all specifications except
for the between specification. Note that these results are robust to the inclusion of continent
dummies and to the simultaneous inclusion of both period and continent dummies (estimations not
reported here).
The coefficients on the period dummies suggest that industrializing has become harder over time.
A significance test on whether the coefficients on the period dummies are different from each other
indicates that periods from 1970 to 1995 and periods 1995-2005 could be unified because the
coefficients are not significantly different from each other.
The Hausman test strongly rejects (p=0.0000) the null hypothesis of not significant differences
between the fixed and random effects model and so discourages from using random effects which
would have been our preferred choice given the nature of the data. This is why the Hausman and
Taylor model is chosen and from now on only Hausman and Taylor results will be shown. The
model by Hausman and Taylor combines the advantages of fixed and random effects models
because it allows country effects to be correlated with the explanatory variables and does not
eliminate country time-invariant effects. Nevertheless, in the presence of lagged regressors, the
Hausman and Taylor estimator is inconsistent (and so are both fixed and random effects
estimators). In section 6.1, we will use the GMM approach to validate our results.
Moved by the suspect that USPTO patents per capita do not fully capture technological efforts in
developing countries, we employ different measures of innovation. Table 3 reports Hausman and
Taylor estimation where we test four alternative measures of technical change: the depreciated
USPTO patent stock (introduced as a comparison to the other models), patents granted to
residents by national patent offices, R&D expenditures over GDP, and an indicator of technological
level developed by Fagerberg (1988).
As for the previous estimation, we run Hausman tests to check which of these variables are
endogenous. The tests indicate exogeneity of patent stock and patents granted by the national
patent offices and endogeneity of R&D and the technological level. As suggested by Fagerberg
and Verspagen (1999), education has been dropped in columns 3 and 4 because R&D and
secondary education are too closely related.
11
Table 3. Alternative measures of technical change
Patent Stock
coef
se
sig
National Office
Patents per capita
coef
se
sig
manL1
-0.325
0.034
***
-0.384
0.036
WAGE
0.105
0.359
0.190
0.427
OPEN
0.032
0.008
***
0.035
0.008
***
0.046
0.011
***
0.043
0.011
***
UNDERV
1.195
0.623
*
1.804
0.681
***
2.465
0.887
***
2.396
0.882
***
DEM
-0.025
0.027
-0.015
0.027
0.005
0.038
0.044
0.040
EDU
0.295
0.201
0.416
0.199
RELUS
-0.161
2.360
-2.050
2.311
2.589
4.003
8.540
4.622
*
INFL
0.245
0.144
0.361
0.146
0.320
0.175
*
0.361
0.174
**
TOT
3.050
2.007
2.800
2.180
-8.960
4.580
*
-10.127
4.586
**
POP
1.069
0.354
0.862
0.309
***
1.421
0.744
*
1.769
0.900
**
PATSTOCK
-2.707
2.643
3.667
2.138
*
1.207
0.561
**
13.247
6.286
**
*
***
NATPATPC
***
R&D
TL
coef
se
sig
coef
se
sig
-0.472
0.056
***
-0.492
0.056
***
-0.385
0.421
-0.314
0.410
**
**
R&D
TL
KGATEMP
1.338
1.370
1.056
1.161
-0.605
2.874
-4.686
3.730
OIL
-0.474
2.174
0.226
1.771
-0.958
4.783
-1.523
5.898
D65-70
-0.294
0.533
-0.177
0.585
D70-75
-2.352
0.621
***
-2.615
0.669
***
D75-80
-2.915
0.768
***
-3.233
0.815
***
D80-85
-3.533
0.899
***
-4.266
0.958
***
1.725
0.727
**
D85-90
-3.459
0.986
***
-4.089
1.044
***
0.210
0.446
1.892
0.612
***
D90-95
-3.545
1.105
***
-4.439
1.162
***
0.018
0.502
1.767
0.535
***
D95-00
-4.968
1.180
***
-5.762
1.231
***
-1.164
0.577
**
0.518
0.442
D00-05
-4.876
1.294
***
-6.088
1.359
***
-1.605
0.699
**
Constant
-4.705
3.505
-1.850
3.288
-10.225
7.338
-16.167
9.114
Rho
0.778
0.697
0.947
0.967
Obs
435
372
237
228
Countries
72
68
58
56
Legend: * p<0.10, **p<0.05, *** p<0.010
12
*
In the previous table, the coefficient on USPTO patents was positive but not significant. Here the
stock of USPTO patents is even negatively linked to our measure of industrialization.
As expected, there is a difference between USPTO and patents granted by national patent offices:
the number of patents granted by national offices to residents is positively and significantly
associated to industrialization. The different results on USPTO and national offices patents might
be explained by the fact that developing countries that are expanding manufacturing sectors are
those that have relatively fewer USPTO patents than national patents, and the opposite happens to
developed countries. This would imply that knowledge accumulation is indeed important to
industrializing countries. This result is further corroborated by the fact that both R&D and the
technological level are significant and positive. The introduction of these alternative measures of
innovation does not affect the rest of the results but makes education significant (column 2).
5 INTERDEPENDENCIES AND EVOLUTION OF THE
DETERMINANTS
5.1
Interdependencies and Systems of Innovation
The findings of the previous section indicate that absorptive capacity and technological capabilities
do matter for industrializing countries. Measures based exclusively on USPTO patents are not
significantly related to industrialization, but measures based on R&D and patents granted by
national patent offices are positively and significantly related to industrialization.
In this section we expand upon this piece of evidence by testing whether the impact of innovation
on industrialization depends on the degree of development of a country. The other hypothesis we
want to verify concerns the effects of interdependencies and innovation systems on
industrialization outcomes. Systems of innovation comprise all the economic, social, political,
organizational, institutional, and other factors that permit the development, diffusion, and use of
innovations. These factors are not independent, but tend to support and influence each other: their
coevolution is what shapes innovation patterns. This means that the effect of innovation on
industrialization might be not simply linear but conditional on other factors.
In order to answer the first question, our measures of innovation are interacted with the GDP
relative to the US (RELUS). The second question is tackled by exploring some interaction effects
between variables capturing potentially interdependent policies. For example, one would expect
innovation to have a greater effect on industrialization if the country is open to international trade.
Openness to trade facilitates the acquisition of foreign technologies and allows firms to compete in
international markets where global quality standards apply and global value chains are often
conducive of knowledge spillovers. By the same token, innovation might need high-quality
institutions to guarantee appropriability of the returns of investment in R&D or to ensure that the
most efficient firms are those supported by industrial policies. A last example can be made with
respect to inflation: in countries with high inflation (high uncertainty), entrepreneurs might be
discouraged from long-term investments in skills and technologies.
Results (not reported here) indicate that the effect of innovation does not depend on the level of
development of the country. Moreover, none of the interaction effects capturing policy
interdependencies and innovation systems resulted significant.
13
5.2
Evolution of the determinants of industrialization
In this section we will explore if, and how, the determinants of industrialization changed over time,
i.e. we will investigate whether the estimated coefficients are constant over time. According to our
data, between 1960 and 1975 the share of manufacturing increased in the developing world, but
decreased in developed countries. After 1975 only Asia continued to experience an expansion of
the manufacturing sector, while Africa and Latin America were deindustrializing (Szirmai, 2011).
This evidence suggests that differences in the power of our determinants are likely to emerge from
this analysis.
Furthermore, section 3 of this paper evidenced that industrialization became increasingly difficult
as the coefficients of the time dummies were significant, negative, and decreasing over periods.
Tests on whether the coefficients of the time dummies are significantly different from each other
showed that coefficients for the periods ranging from 1970 to 1995 and from 1995 to 2005 could be
unified.
In this analysis the original 9 periods will be aggregated into 3 sub-periods: 1960-1970, 1970-1995
and 1995-2005. Slope dummies for these 3 sub-periods are created and interacted with all the
explanatory variables. Four models will be estimated: the base model, a model with patents per
capita granted from national offices to residents (NATPATPC), one with R&D and the last with the
index of technological level (TL). In each of these models, the coefficient on each variable will be
interacted with time dummies for each of the three sub-periods. Therefore, three coefficients per
variable are reported.
Table 4 present Hausman and Taylor estimations for these four specifications.
14
Table 4. Estimations for three periods, 1960-1970, 1970-1995, 1995-2005
Base model
manL1 60-70
manL1 70-95
manL1 95-05
WAGE 60-70
WAGE 70-95
WAGE 95-05
LNPOP 60-70
LNPOP 70-95
LNPOP 95-05
EDU 60-70
EDU 70-95
EDU 95-05
OPEN 60-70
OPEN 70-95
OPEN 95-05
DEM 60-70
DEM 70-95
DEM 95-05
RELUS 60-70
RELUS 70-95
RELUS 95-05
UNDERV 60-70
UNDERV 70-95
UNDERV 95-05
TOT 60-70
TOT 70-95
TOT 95-05
INFL 60-70
INFL 70-95
INFL 95-05
PATPC 60-70
PATPC 70-95
PATPC 95-05
NATPATPC 60-70
NATPATPC 70-95
NATPATPC 95-05
R&D 70-95
R&D 95-05
TL 70-95
TL 95-05
D65-70
D70-75
D75-80
D80-85
D85-90
D90-95
D95-00
D00-05
KGATEMP
OIL
Constant
Rho
Obs
Countries
National offices patents
R&D
TL
coef
se
sig
coef
se
sig
coef
se
sig
coef
se
sig
-0.310
-0.307
-0.320
1.319
0.716
-0.124
0.909
0.786
1.009
0.271
0.251
0.271
0.033
0.017
0.034
-0.003
0.010
-0.015
-0.421
-5.649
-2.575
-0.837
1.893
-0.231
3.240
2.302
2.119
1.047
-0.066
-0.092
-11.076
-2.747
0.246
0.078
0.038
0.056
1.105
0.451
0.470
0.368
0.268
0.291
0.271
0.182
0.205
0.014
0.010
0.009
0.051
0.030
0.041
3.692
2.722
3.103
2.145
0.848
1.236
2.526
2.667
5.376
0.345
0.180
0.303
13.552
10.471
8.974
***
***
***
-0.354
-0.462
-0.382
-0.477
0.420
-0.271
0.594
0.869
0.923
0.428
0.447
0.494
0.037
0.023
0.037
-0.014
0.035
-0.033
3.402
-4.092
-0.240
-0.924
1.710
1.266
3.744
0.455
0.316
0.985
0.300
-0.220
0.085
0.044
0.073
1.320
0.497
0.633
0.485
0.408
0.433
0.328
0.261
0.277
0.016
0.012
0.010
0.055
0.032
0.044
4.118
3.150
3.440
2.225
0.883
1.386
2.616
3.167
5.567
0.351
0.194
0.327
***
***
***
-0.448
-0.583
0.060
0.080
***
***
-0.534
-0.628
0.063
0.080
***
***
0.186
-0.597
0.515
0.555
0.081
-0.496
0.490
0.534
**
**
1.156
1.386
0.593
0.587
*
**
1.384
1.532
0.801
0.800
*
*
*
*
**
*
***
0.043
0.060
0.015
0.012
***
***
0.034
0.056
0.015
0.012
**
***
0.018
-0.010
0.040
0.050
0.071
0.010
0.044
0.050
0.138
1.261
4.079
4.231
6.076
8.592
4.974
4.964
1.818
1.396
1.105
1.386
*
1.917
1.723
1.081
1.370
*
-10.107
-5.239
4.987
8.247
**
-13.409
-1.147
4.979
8.342
***
0.174
-0.011
0.211
0.335
0.226
-0.123
0.208
0.317
8.698
5.197
1.424
7.941
4.808
2.456
0.577
1.375
0.857
0.605
12.348
13.296
11.482
7.276
*
2.787
2.776
2.497
0.821
3.394
3.283
3.295
0.465
*
-4.022
-0.937
-11.286
0.957
228
56
3.413
5.069
8.188
-0.331
1.535
1.099
0.292
-0.068
-0.304
-6.133
-6.000
1.322
0.100
-2.277
0.528
438
74
**
***
***
**
*
***
**
**
***
0.559
4.606
4.655
4.675
4.649
4.680
4.969
4.977
0.957
1.333
4.634
0.013
2.832
2.142
0.961
0.829
0.329
-3.535
-4.055
0.628
0.172
-4.611
0.844
372
68
0.602
4.897
4.936
4.947
4.914
4.948
5.419
5.429
1.613
2.519
5.821
Legend: * p<0.10, **p<0.05, *** p<0.010
15
*
*
***
0.165
-0.181
-2.689
-3.367
0.268
-0.541
-6.386
0.907
237
58
0.484
0.556
3.511
3.507
2.381
3.555
6.213
**
The lagged value of manufacturing, the size of the domestic market and openness are constant
determinants of industrialization. Inflation is a positive and significant factor only during the 60s.
Technological backwardness and undervaluation were key determinants of industrialization only
from 1970 to 1995. In column 2, where national patents substitute for USPTO patents, education
becomes significant from 1970 onwards (this result, however, seems to be due to the sample
because when we use these sample to estimate the model in column 1 education is still
significant). Coefficients on patents are never significant, neither granted by USPTO (column 1) nor
by national patent offices (column 2). This is in contrast with estimations in table 3 where the
coefficient on national offices’ patents was positive and significant. As in previous estimations, the
introduction of indicators based on R&D reduces the time series to the period 1980-2005. Results
in columns 3 and 4 show that R&D expenditures and the measure of technological level have
become drivers of structural change after 1995. We ran tests on the equality of coefficients, i.e.
tests to check whether coefficients are significantly different from each other. Results show that
almost all coefficients are not significantly different from each other. This means that these
variables have a pretty stable effect across sub-periods, but matter only in some.
6 ROBUSTNESS CHECK
This section presents a series of robustness checks. The introduction of the lagged regressors
makes fixed effect, random effect, and Hausman and Taylor estimators inconsistent. In section 6.1
we verify the validity of our results by General Methods of Moments estimations that provide
consistent parameter estimates. Provided that endogeneity does not dramatically affect our
estimates, in section 6.2 we experiment with mixed linear models. These models are not often
used in this type of studies but are interesting insofar as they permit to enrich fixed and random
effects models. Finally, as suggested by Haan (2007), different indicators of institutions are tested
(Section 6.3).
6.1
Robustness to GMM
Roodman (2006) suggests that, for a correct implementation of system GMM, a panel should be
characterized by small T and large N (which is our case) and the model should include time
dummies. The standard treatment of endogenous variables is to use lag 2 and deeper for the
transformed equation and lag 1 for the levels equation. Moreover, the number of instruments must
not exceed the number of groups; the p-value of the Hansen test must be higher than 0.1 and
below 0.25, and the AR(2) above 0.1. Other authors instrument endogenous variables with fewer
lags because, if all the lags are used, the number of instruments surpasses the number of groups
and this makes the Sargan and Hansen tests weak and the estimations unreliable. The Sargan
and Hansen tests indicate whether the instruments are jointly valid, i.e. if they are not correlated
with the error term. So, if these tests are weakened, it is hard to gauge the validity of the
instrumental estimation. Roodman (2009) proposes three solutions in the case of instrument
proliferation and weak tests: limiting the set of instruments to certain lags, collapsing the instrument
set, and combining the two former solutions.
Table 5 documents different system GMM estimations. In columns 3 and 4 of table 5, we report the
estimation results adopting the first of the solutions offered by Roodman. In column 3, lag 3
(manL1t-3 and WAGE t-3) are used. In column 4, the lagged value of manufacturing which is the
most troublesome variable as it is the one responsible for the “dynamic panel bias” is instrumented
with all possible lags (two and deeper), while WAGE, which is the other endogenous variable in our
analysis, is instrumented with lag 3. In the last column of table 5 we use with all possible lags for
the two endogenous variables.
16
Before discussing the results of the GMM estimations, we set the credible range for the coefficient
of the lagged regressor, manL1. OLS and fixed effects estimations (reported in columns 1 and 2 of
table 5) define the credible range for the coefficient of the lagged value of the manufacturing share
between -0.124 and -0.355.
Table 5. Exploring robustness with GMM estimators
OLS
Fixed Effects
SYSTEM GMM
(1)
(2)
(3)
coef
se
sig
coef
se
sig
coef
se
sig
coef
se
sig
coef
se
sig
manL1
-0.124
0.023
***
-0.355
0.039
***
-0.152
0.091
*
-0.170
0.069
**
-0.147
0.048
***
WAGE
-0.185
0.244
0.112
0.397
-1.547
1.613
0.608
1.207
0.906
0.430
**
OPEN
0.019
0.005
0.034
0.009
0.022
0.013
0.028
0.011
0.024
0.010
**
UNDERV
0.292
0.594
1.038
0.730
-0.148
1.212
1.007
1.154
1.560
0.958
DEM
-0.027
0.018
-0.029
0.031
-0.015
0.034
-0.021
0.036
-0.009
0.030
EDU
0.117
0.086
0.264
0.305
0.183
0.138
0.164
0.117
0.173
0.112
PATPC
-0.523
4.838
0.858
8.596
0.911
8.744
-1.100
8.339
4.417
11.832
RELUS
-2.529
1.349
1.302
2.920
1.480
5.217
-4.870
3.827
-6.567
2.078
INFL
-0.034
0.125
0.277
0.161
0.168
0.189
0.132
0.198
0.049
0.179
TOT
1.409
1.599
3.038
2.225
1.333
2.636
1.328
2.355
2.035
5.788
POP
0.365
0.119
***
3.022
1.339
0.333
0.277
0.654
0.257
**
0.470
0.214
KGATEMP
1.153
0.413
***
1.175
0.567
1.391
0.680
**
0.896
0.556
OIL
0.488
0.532
0.108
0.948
0.394
1.113
1.402
1.390
D65-70
0.165
0.592
0.352
0.939
0.050
1.008
-0.217
0.793
D70-75
-1.351
0.625
D75-80
-1.482
D80-85
***
*
***
*
**
**
**
***
**
-0.551
0.596
**
-2.818
0.734
***
-0.760
1.103
-1.755
0.843
**
-2.011
0.717
***
0.689
**
-3.699
0.964
***
-0.873
1.830
-2.538
1.325
*
-2.790
0.965
***
-1.774
0.719
**
-4.495
1.164
***
-0.230
2.493
-2.838
1.520
*
-3.276
1.074
***
D85-90
-1.534
0.731
**
-4.535
1.325
***
0.384
2.730
-3.063
1.860
-3.728
1.082
***
D90-95
-1.570
0.782
**
-4.807
1.515
***
0.562
3.020
-3.436
1.973
*
-4.088
1.176
***
D95-00
-3.002
0.787
***
-6.420
1.684
***
-1.077
3.336
-4.980
2.304
**
-5.769
1.217
***
D00-05
-2.547
0.833
***
-6.408
1.861
***
-0.649
3.253
-4.707
2.258
**
-5.411
1.232
***
Constant
-0.602
1.444
-22.095
11.921
*
-5.087
3.918
-0.872
3.410
1.968
2.122
Obs.
438
438
438
438
438
74
74
74
74
Inst.
44
67
90
Hansen
test
0.103
0.097
0.863
AR(2)
0.403
0.394
0.371
Countries
Legend: * p<0.10, **p<0.05, *** p<0.010
Notes: All GMM estimations are two-step estimations with Windmeijer correction. In model (1), lag t-3 are
used. Model (2) instruments the lagged regressor, manL1, with all possible lags (2 and deeper) and the other
endogenous variable, WAGE, with lag t-3. Model (3) uses all possible lags for the two endogenous variables.
17
In the case of system GMM with lags 3 (model 1), the p-value of the Hansen test is 0.103, which
means that the null hypothesis of exogeneity of the instruments is not rejected. The coefficient on
lagged manufacturing is significant as in all previous estimations and falls within the credible range.
Nevertheless, results on the other explanatory variables do not fully confirm previous estimations.
When the set of instruments increases and includes lags 2 and deeper for the lagged value of
manufacturing and lags 3 for WAGE (model 2), the p-value of the Hansen test goes slightly below
0.1 but not enough to reject the null hypothesis. The coefficient on the lagged value of
manufacturing is significant and credible and the rest of the results are by and large robust.
When all available lags are allowed in the model, the number of instruments is higher than the
number of groups and the Hansen test shows clear symptoms of instruments proliferation.
However, the lagged value of manufacturing is significant and still within the credible range.
Openness, inflation, wages, population and RELUS are significant and signs are consistent with
expectations and previous estimations.
Given the criteria mentioned above, it seems that model (2) would be our preferred model. Here
the effect of lagged manufacturing is lower than in Hausman and Taylor estimations and
undervaluation looses significance. Still, expectations on the signs of the variables are by and large
verified.
6.2
Mixed linear models
Do mixed linear models confirm or add on to our results? Mixed linear models permit random
parameter variation to depend on observable variables, i.e. allow explanatory variables to have a
different effect for each country. Among all the variants of mixed models, we apply a random
slopes model in which not only the intercept (as in a random effect model) but also the coefficients
of some variables are allowed to change across countries.
We estimated random slopes models allowing one single variable at a time to have a random
coefficient. We repeated this procedure for each single explanatory variable. In the first instance,
we did not impose restrictions on the correlation of the random effects, i.e. we did not assume that
the random effects are uncorrelated with each other. If the model failed to converge, we assumed
uncorrelated random effects. After each estimation, we checked the p-value of the LR test and
retained only the models for which the null hypothesis was rejected, i.e. random slopes do add
information to the random intercept model.
Results show that adding random slopes for all variables, but openness, USPTO patents per capita
and the size of the population does not add information. In table 6 we report results of a random
intercept model (this is equivalent to a random effect model and is included here for comparison to
the other models) and three random slope models where OPEN, PATPC, and POP respectively
are allowed to have random slopes. Note that a model where these three random slopes are
contemporaneously introduced fails to converge.
18
Table 6. Random slopes
Random Intercept
Random Slope
(1)
(2)
(3)
coef
se
sig
coef
se
sig
coef
se
sig
coef
se
sig
manL1
-0.161
0.025
***
-0.202
0.026
***
-0.170
0.026
***
-0.172
0.026
***
WAGE
-0.124
0.266
0.039
0.266
-0.124
0.273
-0.071
0.264
OPEN
0.022
0.005
0.024
0.008
0.021
0.005
0.025
0.006
UNDERV
0.500
0.597
0.860
0.594
0.601
0.602
0.495
0.590
DEM
-0.025
0.021
-0.012
0.021
-0.025
0.021
-0.029
0.021
EDU
0.145
0.101
0.168
0.096
0.175
0.103
0.157
0.098
PATPC
-0.220
5.527
-1.605
5.188
3.624
7.533
-0.808
5.531
RELUS
-2.600
1.515
-2.685
1.565
-2.845
1.538
-2.455
1.541
INFL
0.023
0.129
0.072
0.128
0.050
0.130
0.015
0.130
TOT
1.981
1.718
1.501
1.658
2.050
1.745
1.733
1.713
POP
0.470
0.142
***
0.519
0.152
***
0.485
0.145
***
0.553
0.152
***
KGATEMP
1.304
0.505
***
1.209
0.500
**
1.274
0.506
**
1.225
0.502
**
OIL
0.489
0.665
0.559
0.627
0.368
0.673
0.496
0.662
D65-70
0.027
0.573
0.015
0.552
-0.021
0.567
0.001
0.571
D70-75
-1.600
0.614
***
-1.712
0.590
***
-1.727
0.612
***
-1.632
0.613
***
D75-80
-1.789
0.692
***
-2.018
0.674
***
-1.918
0.692
***
-1.902
0.690
***
D80-85
-2.136
0.740
***
-2.531
0.729
***
-2.288
0.744
***
-2.290
0.741
***
D85-90
-1.943
0.761
**
-2.409
0.749
***
-2.140
0.767
***
-2.084
0.762
***
D90-95
-1.988
0.822
**
-2.541
0.814
***
-2.176
0.829
***
-2.135
0.824
***
D95-00
-3.436
0.833
***
-4.022
0.828
***
-3.618
0.841
***
-3.618
0.835
***
D00-05
-3.046
0.888
***
-3.791
0.894
***
-3.316
0.899
***
-3.291
0.895
***
Constant
-1.022
1.639
-0.590
1.729
-0.983
1.666
-1.556
1.707
0.030
0.009
15.200
8.049
0.380
0.182
**
1.382
0.552
0.913
0.248
4.011
1.728
***
2.270
0.095
***
2.337
0.093
2.353
0.092
***
-0.942
0.092
**
-0.791
0.318
-0.992
0.015
***
***
*
sd(variable)
sd(cons)
0.773
0.233
sd(Residual)
2.367
0.092
Corr
***
***
*
*
***
***
*
*
***
***
Observations
438
438
438
438
Countries
74
74
74
74
Legend: * p<0.10, **p<0.05, *** p<0.010
19
***
When OPEN, PATPC, and POP are allowed to have random slopes, previous findings are
confirmed. The standard deviations of these variables are always significant and parameter
estimates are pretty robust across the four specifications. Interestingly, in models (1) and (2), the
coefficients on education and RELUS become significant and have the expected sign.
6.3
Alternative measures of institutions
In order to check whether the results on institutions depend on the selected measure, we
experiment three alternative indicators. The first two come from the Polity IV dataset (Marshall and
Jaggers, 2002) and are the democracy index (P_DEM) and the measure of constraint on the
executive (XCONST), which is one of the components of the democracy index. The third,
POLCON, is an index of political credibility developed by Henisz (2000, 2002). This index
measures the feasibility of policy change, i.e. the extent to which a change in the preferences of
any one political actor may lead to a change in government policy. It goes from 0 to 1, with higher
scores associated with less feasibility of policy change.
As in previous estimations, the exogeneity of these variables is checked by means of Hausman
tests. The Polity measure of democracy, P_DEM, is the only exogenous variable.
Estimations results are reported in table 6 and confirm that democracy is negatively and not
significantly linked to industrialization. Yet, indicators that capture decision making processes are
positively associated to industrialization and the Polity indicator of constraint on the executive is
even significant. These results seem to confirm that what matters is not democracy per se, but
rather the presence of functioning decision rules.
20
Table 6. Alternative proxies for institutions, Hausman and Taylor estimations
coef
POLITY
Democracy
se
POLITY
Constraint
se
sig
coef
***
HENISZ
Policy Change
se
sig
sig
coef
-0.312
0.033
***
-0.321
0.033
0.051
0.355
0.041
0.359
manL1
-0.310
0.033
WAGE
0.019
0.356
OPEN
0.031
0.008
***
0.031
0.008
***
0.032
0.008
***
UNDERV
1.173
0.621
*
1.171
0.617
*
1.260
0.627
**
EDU
0.273
0.196
0.254
0.191
0.301
0.206
PATPC
-1.657
7.374
-0.956
7.330
0.726
7.542
RELUS
-0.191
2.352
-0.802
2.329
-1.427
2.338
INFL
0.250
0.145
*
0.226
0.143
0.255
0.144
*
TOT
3.477
2.091
*
3.716
2.080
*
3.617
2.118
*
POP
0.935
0.356
***
0.946
0.348
***
1.014
0.370
***
P_DEM
-0.041
0.069
0.032
0.019
*
0.096
1.045
XCONST
POLCON
***
KGATEMP
0.640
1.320
0.568
1.279
0.915
1.434
OIL
-0.345
2.053
-0.294
1.990
-0.186
2.372
D65-70
-0.302
0.530
-0.363
0.530
-0.341
0.534
D70-75
-2.331
0.626
***
-2.334
0.617
***
-2.347
0.628
***
D75-80
-2.997
0.766
***
-2.963
0.752
***
-2.900
0.775
***
D80-85
-3.487
0.887
***
-3.454
0.877
***
-3.545
0.907
***
D85-90
-3.351
0.972
***
-3.380
0.964
***
-3.580
0.996
***
D90-95
-3.407
1.081
***
-3.484
1.073
***
-3.721
1.111
***
D95-00
-4.918
1.150
***
-5.047
1.141
***
-5.206
1.189
***
D00-05
-4.719
1.264
***
-4.874
1.253
***
-5.105
1.308
***
Constant
-3.788
3.484
-3.719
3.419
-4.395
3.627
Rho
0.760
0.748
0.810
Obs
431
431
437
Countries
71
71
74
Legend: * p<0.10, ** p<0.05, *** p<0.010
21
7 CONCLUSIONS
Since early theories in economic development, manufacturing has been viewed as a special sector
capable of spurring economic growth. Cornwall (1977) formulated a model to explain the role of
growth of manufacturing output for GDP growth. Two main equations constitute this model: the first
explains the growth in manufacturing output, the second the growth of GDP as dependent on the
growth of manufacturing output. Later studies estimated the reduced form of this model and so
analysed the impact of industrialization and some other explanatory variables on growth. The
present paper aimed at contributing to this strand of literature by exploring the determinants of
industrialization in a sample of 74 countries from 1960 to 2005. In order to do so, it looks directly at
the first equation of the Cornwall model, i.e. the one explaining the growth of the manufacturing
output, before it feeds into the equation of economic growth. In this way, it investigates why some
countries industrialized and some others did not. If industrialization is a crucial step towards
development, this study becomes relevant for the economic and political debate on growth and
development.
A measure of industrialization, the first difference of the share
regressed over a set of variables that existing literature
industrialization. Results show that industrialization is faster
undeveloped industrial base and development strategies
undervaluation, skills and knowledge accumulation.
of manufacturing in GDP, was
identifies as factors behind
for larger countries with an
based on trade openness,
Trade openness positively affects the expansion of the manufacturing sector. This result found only
mild support in previous empirical studies (among others, Fagerberg and Verspagen, 1999, 2007;
Rodrik et al., 2004; Fagerberg and Schrolec, 2008; Szirmai and Verspagen, 2011) and so seems
to suggest that trade stimulates the expansion of the manufacturing sector, instead of affecting
directly economic growth. According to our findings, price competitiveness is only partly supported
as a driver of industrialization. Exchange rate undervaluation matters but wages do not. On the one
hand, Rodrik (2008) provided evidence for undervaluation as a successful factor for
industrialization. On the other hand, that labour costs do not constitute factors of competitiveness
is consistent with the Schumpeterian view of competitiveness based on innovation rather than
costs.7 Inflation is positively and significantly related to industrialization and this contradicts the
theories according to which growth and industrialization strictly require low inflation. Finally,
institutions, as reflected in democracy indexes, are not determinants of industrializations but an
indicator of constraint on the executive is positively and significantly associated to our measure of
industrialization. These results confirm previous findings about the modest impact of democratic
political systems on growth in developing countries (Fagerberg and Schrolec, 2008).
Studies of the evolutionary approach to innovation and development claim that catching up
demands the acquisition of technological capabilities (see the experiences of Korea and the other
Asian NICs). Nevertheless, empirical work on manufacturing and growth has seldom looked at the
role of R&D expenditures and patents. This paper shows that technical change matters for
industrialization. In particular, innovation measured by R&D expenditures, patents granted by
national patent offices and an indicator of technological level based on USPTO patents and R&D
expenditures is positively and significantly related to industrialization.
Our analysis also demonstrates that industrializing has become more difficult over time. An
investigation of the evolution of the determinants of industrialization over different sub-periods
evidences that underdeveloped manufacturing bases, trade openness, and the size of the
domestic market are constant determinant of industrialization throughout the whole panel.
Undervaluation and technological backwardness, instead, mattered only in the period from 1970 to
7
This hypothesis was empirically verified by Fagerberg (1988) and Fagerberg et al. (2007).
22
1995, while absorptive capacity and technological capabilities, as measured by R&D expenditures
and technological level, became critical only after 1995.
Extant literature has found a similar pattern for GDP growth and has explained it with the
decreasing role of imitation and diffusion and the greater requirements in terms of skills and
technological capabilities that characterize modern capitalism (Fagerberg and Verspagen, 1999,
2002, 2007). Our results confirm these findings and show that these patterns are not only relevant
for GDP growth but also to manufacturing growth. This has potentially important implications as to
how we perceive the manufacturing sector and how we gauge the hypothesis of manufacturing as
an engine of growth in modern times.
Acknowledging that innovation has a systemic nature, we inquired into the role of innovation
depends upon the interplay of different factors, such as the trade-orientation of the country or other
macroeconomic factors such as inflation and undervaluation. Against expectations, none of these
interactions terms turned out to be significant. One direction for future research is to find other
methodologies to test for policy interdependencies that are likely to play an important role, despite
the results of our econometric analysis.
This study is limited by data availability, especially with respect to wages and institutions. For the
first variable, it would be useful to find reliable data sources to fill the gaps in the UNIDO dataset.
For the second variable, it would be interesting to go beyond democracy indexes and explore more
“governance-related” institutional variables. Different dependent variables, like labour productivity,
could also be tested.
Future research can also analyse the role of innovation by disaggregating the share of
manufacturing into sectoral shares. In this way, it becomes possible to investigate the determinants
of structural change towards more knowledge intensive manufacturing sectors. Connected to this
point it is the role of manufacturing as an engine of growth. Which types of industrialization
strategies (also in terms of sector targets) are more conducive to economic growth? And which of
these strategies can guarantee sustained economic growth over time?
23
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