An analysis of technology adoption by export-oriented manufacturers in Pakistan.
Mahmood, Tariq ; Din, Musleh Ud ; Ghani, Ejaz 等
This paper analyses the issue of technology adoption by
export-oriented enterprises based on survey data. Using the Rank Model
of technology adoption, the paper explores the role of several firm
level characteristics that can influence firm's decision to adopt
new technology. The results show that younger and bigger firms have a
higher probability of technology adoption. Firms that have obtained
certifications to product and process standards demonstrate a higher
likelihood of technology adoption. Domestically-owned firms are found to
have a higher probability of technology adoption as compared with
foreign-owned firms. The empirical findings underscore the need for
policy options to encourage export-oriented enterprises to adopt new
technology including, for example, fiscal incentives to encourage
research and development activities, and up-gradation of physical
infrastructure for product testing to facilitate certifications.
JEL classification: C25, L60, 014
Keywords: Manufacturing Industries, Probit Model, Technology
Adoption.
1. INTRODUCTION
The last two decades have witnessed a remarkable spread of
technology in all spheres of economic activity. The change has been so
rapid that firms are finding it difficult to keep pace with
ever-changing market situations. The issue of technology adoption is
particularly relevant for export-oriented manufacturers who face tough
competition in international markets and must maintain a competitive
edge by adopting latest product and process technologies to meet the
requirements of upscale global markets. It is generally believed that
Pakistani firms have lagged behind their competitors in international
markets in terms of technological advancement and consequently
Pakistan's exports continue to remain concentrated in low
value-added and low quality product segments. However, the question of
technology adoption by export- oriented manufacturers has received
little attention in the empirical literature. This study is an attempt
to explore the determinants of technology adoption by export-oriented
manufacturing firms in Pakistan based on a survey of such firms in four
major export categories including textiles and apparel, leather and
leather products, agro- food processing and fisheries.
According to Woodside and Biemens (2005), the term technology
adoption refers to the decision-making process of an individual firm.
(1) Technology adoption is a complex phenomenon and depends in large
measure on firm characteristics and the economic environment under which
the firms operate. This study focuses on firm characteristics that are
believed to influence the probability of firms' decisions whether
to invest in technology or not. The data relating to technology adoption
is seldom available in a developing country like Pakistan. However the
survey conducted by Pakistan Institute of Development Economics in
collaboration with United Nations Industrial Development Organisation
(UNIDO) contains a binary response question (2) which has been used as a
dependent variable in a Logit model for estimation of probabilities of
firms' decision to invest in technology. The rest of the paper is
organised as follows: Section 2 describes the data and sets out the
methodology. Section 3 provides a discussion of the empirical results
whereas Section 4 contains conclusions and policy recommendations.
2. DATA AND METHODOLOGY
This study is based on a survey of export-oriented firms conducted
by the Pakistan Institute of Development Economics (PIDE) in
collaboration with the United Nations Industrial Development
Organisation. The sample covers 157 exporting firms in four major
sectors viz. textiles, leather, agro-food processing and fisheries
located in Sindh and Punjab provinces. 3 Various sub-sectors are covered
under each of the major export segment: in the textiles, yarn, fabrics,
knitwear, garments and bed sheets and towels; in leather, tanning,
footwear and leather products; in agro-food processing, horticulture
products, and rice; and the fisheries comprise various types of fish
processing enterprises and fish exporters. (4)
Various models have been used in the literature to model
firms' decisions pertaining to technology adoption. In this paper,
we employ the rank model of technology adoption. (5) This model is based
on the observation that the decision to adopt a particular technology is
a choice made by a particular firm that is influenced by a range of
firm's characteristics including the age and size of the
enterprise, volume of sales, location, and type of ownership. These
characteristics are assumed to determine a threshold level and the
adoption of technology is likely to occur if this threshold is crossed.
The rank model of technology adoption has a sound theoretical basis in
that it is built upon the profit maximising behaviour of a firm. The
empirical implementation of the rank model is carried out in terms of a
binary choice model.
The choice of technology adoption is a discrete choice. Firms
either invest of do not invest in new technologies. Due to this
categorical nature of dependent variable, the ordinary least squares
method will not produce the best linear unbiased estimator i.e., OLS
estimate are biased and inefficient. This situation calls for the use of
one of the binary dependent variable techniques. In the literature two
most commonly used techniques are Logit and Probit models. The basic
difference between these two techniques lies in the assumption about the
distribution of the error term. In the Logit model, errors are assumed
to follow the logistic distribution, whereas in the Probit model errors
are assumed to follow the standard normal distribution. (6) In this
paper we use the Logit estimation technique.
This function has two useful characteristics in the present
context. First, the value of the function is limited between 0 and 1, as
necessary for a probability model. Second, the distribution of the
function follows an S-shaped curve, exhibiting a typical technology
adoption pattern (Figure 1).
[FIGURE 1 OMITTED]
The impact of an event on the probability depends on the initial
probability of the event. If the Z moves from point A to point B, the
probability of the event increases by a very small amount. However, a
movement of equal magnitude from point C to point D increases the
probability of the event by a relatively larger amount. Again the change
in the value of the probability is small as Z moves from point E to F.
This is a typical behaviour of technology adoption; at initial stages
adoption occurs at a slow pace, gradually it picks up momentum, and
slows down again as adoption process approaches a saturation point.
The logistic function is given by:
[P.sub.i] = [e.sup.z]/(1+[e.sup.z])
Where [P.sub.i] is the probability of a binary outcome (adoption or
non-adoption of new technology by the firm i, and Z = [beta]X, where
vector X represents firms' characteristics, and [beta] is a vector
of coefficients. The unknown parameters can be estimated by Maximum
Likelihood Method. The natural log of odds ratios is given by:
[Z.sub.i] = ln[[P.sub.i]/(1 - [P.sub.i])]
Since these probabilities are not directly observable, we proxy
these by a binary variable Yi which takes a value of 1 if the ith firm
makes an investment in new technology and 0 otherwise. Using Yi as a
dependent variable we estimate the following model:
[y.sub.i] = [[beta].sub.0] + [[beta].sub.1]ln[Age.sub.i] +
[[beta].sub.2]D[size.sub.i] + [[beta].sub.3][Location.sub.i] +
[[beta].sub.4]ln[Sales.sub.i] + [[beta].sub.5][Cert.sub.i] +
[[beta].sub.6][Own.sub.i] + [v.sub.i]
Where
Age = Age of firm in years.
Dsize = Dummy variable with a value of 1 for large sized
enterprises and 0 otherwise.
Location = Dummy variable taking a value of 1 if the firm is
located in Karachi and 0 otherwise.
Sales = Sales in US$.
Certi = Dummy variable taking a value of 1 if the firm is certified
and 0 otherwise.
Own = Dummy variable taking a value of 1 if the firm is
domestically owned and 0 otherwise.
The age of the firm can affect the probability of investment in new
technology in two ways. On the one hand, older firms that are more
experienced and are better cognizant of the market opportunities and
requirements could be more inclined to invest in new technology to
maintain their competitive strengths acquired over a longer period of
time. Also, older firms may in fact need to invest in new technology to
replace their older machinery and equipment. One may, however, argue
that newer firms having a modern outlook may be more likely to invest in
new technology. The empirical evidence in the literature is mixed: Parhi
(2008) finds a positive effect of age on technology adoption whereas
Fariaa, et al. (2002) report a negative relationship between firms'
age and probability of technology adoption.
Firm size can also influence a firm's decision to adopt new
technology. The theoretical relationship between firm size and
probability of investing in new technology is ambiguous. On the other
hand, there are many reasons to expect positive relationship between
firm size and investment. Large firms enjoy economies of scale in
production, have a relatively higher capacity for taking risks, and have
better financial positions all of which contribute to higher probability
of investment on new technology. On the other hand, smaller firms may be
more inclined to invest in new technology because of their desire to
establish a toehold in the market and to enhance their scale based on
newer technology. Empirical studies on the role of firm size in
technology adoption find mixed evidence: Bartoloni and Baussola (2001)
find positive relationship between firm size and technology adoption,
whereas other studies have shown a higher probability of technology
adoption by smaller firms [e.g. Oster (1982)].
Spatial clustering of economic activity and its role in interactive
learning processes is important in technology adoption. The new
literature on economic geography explicitly incorporates the role of
geographical location in economic development process. (7) Positive
externalities of such location include "cluster development"
which leads to establishment of networks for dissemination of
information so that 'best practices' in one cluster can foster
demonstration effect in others. To capture such advantages, we use
Location as a dummy variable which takes a value of 1 if the firm is
located in Karachi--the city being the biggest commercial hub in
Pakistan and still the only major port is believed to offer such
geographical advantages. Fariaa, et al. (2002) find that firms located
in industrialised districts have an 8 percent greater probability of
adopting technology than those located in poor regions. However there
are some negative externalities like congestion which may divert
investment away from such a location. Sign and significance of this
variable will reflect the net effect of these positive and negative
externalities.
The firm's level of sales is likely to positively affect the
probability of investment in new technology. Firms with larger sales
have a better capacity as well as better motivation to invest in new
technology to retain their market share through improving product and
process technologies. Hence we expect this variable to have a positive
sign.
Recent years have witnessed a growing demand from buyers for
certification of conformity with standards and technical regulations.
(8) The emerging trade environment under the umbrella of the World Trade
Organisation (WTO) also calls for adherence to standards and norms such
as quality certification as well as certification of conformity with
health, labour, and environment standards. Such certifications
demonstrate compliance with product safety and quality and manufacturers
having such certifications are expected to perform better in export
markets. Export-oriented firms that have obtained product and process
certifications may be better inclined to upgrade their technology owing
to their awareness of the benefits of new product and process
technologies. We, therefore, expect that firms that are certified are
more likely to invest in new technology and hence this variable is
expected to have a positive sign in the Logit regression.
Ownership is also expected to play an important role in influencing
a firm's decision to adopt new technology. We argue that
domestic-owned firms are more likely to adopt new technology as compared
with foreign-owned firms not least because of the technology gap they
face and their drive to catch up with their foreign-owned counterparts.
The foreign-owned firms, on the other hand, may be less likely to invest
in new technology owing to their better technological base as compared
with domestic-owned firms. Hence odds are in favour of domestic
ownership having a higher probability of technology adoption.
3. MODEL ESTIMATION
The specified model has been estimated as a Logit regression (9)
(Table 1). The null hypothesis that all the slope coefficients are
simultaneously equal to zero is tested in terms of the likelihood ratio
(LR) statistic. Given the null hypothesis, the LR statistic follows the
[chi square] distribution with degrees of freedom equal to the number of
explanatory variables. The results indicate that the null hypothesis is
rejected. McFadden R-squared turns out to be about 0.32. However, as the
theory suggests, in binary dependent models goodness of fit is of
secondary importance. What actually matters is the expected signs of the
coefficients and their statistical and/or practical significance.
The variable 'age' has a negative and significant
coefficient implying that relatively new entrants are more likely to
invest in new technology whereas the older firms are less inclined to
invest in new technology. As expected, the coefficient of
'sales' is positive and significant, indicating that firms
with large sales volumes have a higher probability to invest in new
technology due to their better capacity to undertake such investments.
This is because firms with large sales volumes. The dummy variable for
firm size also turns out to be positive and significant showing that
larger firms have a higher likelihood of investment in new technology to
enhance economies of scale and achieve technological efficiency.
The coefficient of the dummy variable for certification is
significant with a positive sign implying that being certified to
international quality standards increases the probability of a
firm's technology adoption. Firms that have obtained product and
process standards have a better awareness about the benefits of new
technology in terms of product quality and process efficiency. Hence
such firms have a better likelihood of investing in new technology to
maintain their competitive strengths. The location dummy turns out to be
positive but insignificant, implying that clustering and other
locational advantages do not significantly affect the firm's
likelihood of investing in new technology.
The dummy variable for ownership has a positive and significant
coefficient implying that domestically-owned firms are more likely to
invest in new technology. As argued earlier, domestic firms may have a
greater need for new technology as compared with foreign-owned firms and
hence their probability of investing in new technology is higher.
Alternatively, this result also implies that foreign-owned firms are
less likely to invest in new technology. Mansfield (1994) argues that
foreign-owned firms may not be inclined towards investing in new
technology in developing countries as they are more concerned with their
intellectual property rights and lax enforcement of intellectual
property rights in developing countries acts as a potential deterrent to
investment in new technology by foreign firms.
In the Logit regression, the marginal effects provide a good
approximation to the magnitude of change in the dependent variable due
to a change in the independent variable (Table 2). The predicted
probability of a firm investing in new technology is 0.87 for large,
certified and domestically-owned firms, evaluated at average values of
firm's age and volume of sales. An increase of one year in
firm's age reduces the predicted probability of investing in new
technology by 8 percent, holding other independent variables constant at
the mean values. Similarly, certified firms are 16 percent more likely
that non-certified firms to invest in new technology, holding other
variables at their mean values.
The empirical findings have several policy implications. First,
there is a need to provide a supportive environment to new
export-oriented enterprises as these enterprises are likely to play a
leading role in adoption of new technology. A key initiative could be
the provision of tax credit on research and development expenditure.
This would provide an incentive to such enterprises to upgrade and
maintain their technological competencies. Second, there is a need to
create a level playing field between domestic and foreign investors.
Various incentives that are routinely provided to foreign investors
should also be extended to domestic enterprises especially when the
latter are more likely to invest in new technology in line with market
requirements. Third, technical certifications not only help exporters to
gain market share but are also instrumental in encouraging firms to
adopt new technology. Unfortunately, however, obtaining certifications
of conformity to various product and process standards has been
highlighted as a major constraint in Pakistan. There is, therefore, a
need to facilitate certifications through fiscal incentives as well as
through helping to upgrade and establish the necessary physical
infrastructure for technical testing.
4. CONCLUDING REMARKS
This paper has analysed the factors influencing the probability of
technology adoption by export-oriented firms using survey data of
export-oriented enterprises. Employing the rank model of technology
adoption, firm-specific characteristics such as age, volume of sales,
firm size, type of ownership, certification to standards, and
geographical location have been explored as possible factors influencing
firms' decision to adopt new technology. The results show that
younger and bigger firms have a higher probability of technology
adoption. Similarly, firms with higher sales ate more likely to adopt
new technology. Firms that have obtained certifications of conformity
with international product and process standards demonstrate a higher
likelihood of technology adoption. Domestically-owned firms are found to
have a higher probability of technology adoption as compared with
foreign-owned firms due perhaps to the use of lower-end technology by
the domestic firms in relation to the foreign-owned firms.
The empirical findings have important policy implications. First,
new enterprises have demonstrated a higher likelihood of technology
adoption and thus need to be nurtured through proper fiscal incentives
for technology adoption including tax credits for research and
development activities. Second, the domestically-owned enterprises
should be offered the same incentives package as are made available to
foreign-owned firms to enable them to continue investing in better
product and process technologies. Finally, facilitation of certification
to technical standards can be instrumental in promoting adoption of new
technology by the export-oriented enterprises.
APPENDIX
Dependent Variable: Yi
Method: ML--Binary Probit
Variable Coefficient Std. Error z-Statistic Prob.
C -1.77 1.07 -1.66 0.10
LACE -0.42 0.21 -1.99 0.05
DSIZE 0.97 0.37 2.59 0.01
LOCATION 0.28 0.33 0.85 0.40
LSALES 0.28 0.11 2.60 0.01
CERT 0.58 0.37 1.58 0.11
OWN 0.68 0.47 1.44 0.15
LR statistic (6 df) 35.24 McFadden R-squared 0.31
Probability (LR scat) 0.00
Comments
The paper titled 'An Analysis of Technology Adoption by
Export-oriented Manufacturers in Pakistan.' It is an excellent
paper that analyses the issue of technology adoption by 4 major export
industries of Pakistan including textile, leather product, agro-food and
fisheries. The authors have identified 6 characteristics; age, size,
location, volume of sales, status of certification and ownership that
may affect technology adoption. The conclusion is that young and large
firms adopt new technology more often than old and small ones. Location
has insignificant effect while certification and ownership by locals
have positive effect.
Following points may be useful for further improvement of this
paper.
* Technology adoption and ownership have been taken as binary
variables; yes or no and domestic or foreign respectively. They can
better be defined as percentage of total annual expenditures spent on
new technology adoption and percentage share of foreign ownership in a
given firm respectively.
* Both size and volume of sales represent the size of firm,
therefore one of them may be omitted.
* Spatial clustering generates external economies of scale as best
practices in one firm foster demonstration effect for others. Keeping
this fact in view, taking Karachi as the only clustering location seems
somewhat inappropriate. It is quite possible in other cities as well.
* The authors may consider ranking of 4 sectors considered in the
research with respect to technology adoption. That is they want to
answer the question; which sector adopted new technology at first and
which one at the last.
* Some financial variables like debt equity ratio, price earning
ratio and return on equity should have been included in the list of
explanatory variables.
Policy implications of this research are very clear and worth
serious consideration of policy-makers.
M. Mazhar Iqbal
Quaid-i-Azam University, Islamabad.
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(1) Technology adoption is distinct from technology diffusion.
Sarkar (1998) defines technology diffusion as a "mechanism that
spreads successful varieties of products and processes through economic
structure and displaces wholly or partly the existing inferior
varieties". See also Rogers (1995) for a similar distinction.
(2) The statement of the question is: "Please indicate whether
or not you have made investment in the past three years in issues such
as process technology, packaging, product design, that were necessary to
meet specific client/market requirements."
(3) The sample was selected from a universe of 1300 exporters using
the stratified sampling approach.
(4) See PIDE (2007) for further details.
(5) The rank model was first propounded by David (1969) and was
further developed by David (1975): Davies (1979); and Ireland and
Stoneman (1986). The model and its variants have been extensively used
in studies of technology adoption at the firm level; see for instance
David and Olsen (1984); Banoloni and Baussola (2001): Fariaa, et al.
(2002) and Parhi (2008). For a detailed survey of this literature [see
Sarkar (1998) and Geroski (2000)].
(6) The choice between the Logit and Probit models is largely one
of convenience and convention, since the substantive results are
generally indistinguishable [Long (1997), p. 83].
(7) See, for instance, Krugman (1995).
(8) Standards of certification are ISO9000, ISO14000, HACCP,
SA8000, OHSAS, EUREPGAP, and Traceability.
(9) A Probit model has also been estimated, but the results are
very similar (see Appendix).
Tariq Mahmood <
[email protected]> is Senior Research
Economist at the Pakistan Institute of Development Economics, lslamabad.
Musleh ud Din <
[email protected]> is Joint Director at the
Pakistan Institute of Development Economics, Islamabad. Ejaz Ghani
<
[email protected]> is Chief of Research at the Pakistan Institute
of Development Economics, Islamabad.
Table 1
Results of Logit Regression Model
Dependent Variable: Yi
Method: ML--Binary Logit
Variable Coefficient Std. Error z-Statistic Prob.
C -3.67 1.96 -1.87 0.06
LAGE -0.73 0.38 -1.94 0.05
DSIZE 1.75 0.67 2.60 0.01
LOCATION 0.66 0.61 1.09 0.28
LSALES 0.53 0.21 2.55 0.01
CERT 1.14 0.64 1.78 0.08
OWNERSHIP 1.44 0.85 1.69 0.09
LR statistic (6 df) 36.29 McFadden R-squared 0.32
Probability(LR stat) 0.00
Table 2
Marginal Effects of the Logit Regression
Marginal effects after logit
y = Pr(Investment) (predict)
= 0.87
Variable dy/dx Std. Err. z P>[absolute value of z] X
Lage -0.08 0.04 -2.06 0.04 2.90
DSize * 0.24 0.11 2.28 0.02 0.64
Location * 0.08 0.07 1.08 0.28 0.50
Isales 0.06 0.02 2.61 0.01 7.87
Cert * 0.16 0.11 1.49 0.14 0.78
Own * 0.23 0.17 1.38 0.17 0.85
(*) dy/dx is for discrete change of dummy variable from 0 to 1.
