Human capital spillovers, productivity and growth in the manufacturing sector of Pakistan.
Hamid, Abdul ; Pichler, J. Hanns
Manufacturing is an important sector of Pakistan's economy.
The main focus of this paper is to analyse the major factors of
value-added growth and productivity in the manufacturing sector by using
Translog Production Technology over the period 1971-72 to 2004-05. The
empirical findings show that the contribution of productivity and human
capital is around one-third of the total value-added growth in
manufacturing sector which is less than the contribution attributed to
these factors in developed and many other developing countries.
Conventional factors like capital and labour are still the mainstay
in the value-added growth of Pakistan's manufacturing sector.
JFL classification: O1, 03, O4, O14, O15, O31
Keywords: Human Capital Spillovers, Total Factor Productivity,
Absolute and Relative Shares
I. INTRODUCTION
Manufacturing sector has been playing an important role in the
economy of Pakistan. In 2005-06 its contribution to GDP and employment
amounted to 18 percent and 14 percent respectively. It also plays a
vital role in exports whose composition over time has changed
significantly from primary commodities to manufactures and
semi-manufactures with their share in total exports having nearly
tripled, from 28 percent in 1972-73 to 79 percent in 2004-05. (1)
Development therefore of the manufacturing sector will have far reaching
impact on exports, employment prospects, development of agriculture (by
providing machinery and other inputs like fertilisers, etc.) and other
sectors by bringing technological changes and absorbing technological
spillovers from abroad.
This paper aims at measurement of contribution of factor inputs,
technological change and technical efficiency to value-added growth in
the manufacturing sector together with measurement of total factor
productivity (TFP) change index.
The layout of the paper is as follow: a review of literature is
presented in Section II. Section III discusses the methodology,
variables and data sources. Discussion of empirical findings and
comparisons with other relevant findings is given in Section IV. Summary
and conclusions with relevant policy suggestions are presented in
Section V followed by references.
II. REVIEW OF LITERATURE
The following review is intended to provide an overview of the
broad aspects of topical literature relating to the subject of this
paper on a selected basis.
Abramovitz (1956) did an empirical study for the US labour market
for the period 1900-1950 and concluded that almost two-third of the
increase in labour productivity could not be explained by increase in
factor inputs. Solow (1957), Schultz (1964), Ferguson (1965), Hulten
(1973), and Kendrick (1973) reported similar results for subsequent
periods.
Perelman (1995) did an analysis for eleven OECD countries for the
period 19701987 and measured technical efficiency, technological
progress and total factor productivity (TFP) growth for the
manufacturing industries. According to his findings TFP growth was 1.6
percent and technological progress was 1.8 percent during the sample
period. He found that the main contributor in Japan's productivity
growth in manufacturing was the efficiency factor.
Coe, et al. (1997) used data of 77 countries over the period
1971-1990 for measuring technological change and development with
spillovers on productivity and growth. They concluded that a developing
country can benefit more from the technological progress and innovations
occurring in the world and can boost its productivity by importing a
larger variety of intermediate products and capital equipments with new
technology and innovations.
Kruger (2003) measured TFP for a sample of 87 countries for the
period 19601990. Technological progress contributed about 66 percent of
TFP growth in OECD/EU/G7 economies, while the share of technical
efficiency was one third of TFP.
Kumbhakar (2003) used panel data for 450 manufacturing industries
in US for the period 1959-92 to measure TFP and technical change. His
results show that capital productivity increased by 6.5 percent.
Romer (1986) proposed that development and growth were driven by
the accumulation of knowledge. He termed knowledge as a basic form of
capital with investment in knowledge and R&D leading to increasing
marginal returns of factor inputs. He held that knowledge had a
"natural externality" and positive spillovers within and
outside the economy. (2) Romer (1990) assumed four basic factors of
production in an endogenous growth model: i.e., capital, labour, human
capital and an index for the level of technology. According to him, key
to growth and sustained development was an adequate stock of human
capital.
He and Liu (2006) measured investment-specific technological change
and dynamics of skill accumulation for the US for post Second World War
period 19492000. According to their analysis, US placed great importance
on skill accumulation and on job training. (3) Investment-specific
technological change and technical efficiency contributed about 62
percent to average output growth per hour over the period.
Kim and Lee (2006) measured TFP, technological change and technical
efficiency for 49 countries for the period 1965-1990. Their analysis
found that East Asian countries led the world in technical efficiency
and productivity growth leading to higher economic growth. For example
Korea, Taiwan, Hong Kong and Japan grew at the high rates of 8.51
percent, 8.62 percent, 8.03 percent and 5.74 percent respectively during
1965 to 1990. Technological development, human capital accumulation and
technical efficiency were the major contributors in their higher and
continuous growth rates during the reference period. Hong Kong, Japan,
Taiwan and Korea showed the highest TFP growth rates of 3.85 percent,
3.53 percent, 2.85 percent and 2.18 percent respectively during the
period 1965-1990.
Robinson (1971) estimated technological change, technical
efficiency and spillovers caused by human resource accumulation for 39
developing countries and found that, on average, the share of
productivity in total growth was 15 percent in these countries. This is
a much smaller percentage attributable to technological change,
technical efficiency and human capital accumulation than that in
developed countries which is over 50 percent in most of the cases.
Yanrui (1995) estimated technical efficiency for agriculture and
manufacturing sectors of China. According to his estimations,
technological change and efficiency contributed about 53 percent in the
state industrial sector, 58 percent in the rural industrial sector, and
55 percent in agriculture in the Chinese economy.
Zheng, et al. (2003) measured TFP in Chinese state-owned
enterprises (SOEs) for the period 1980-1994. Their findings show that
technical progress contributed significantly in the TFP growth for
Chinese SOEs during the reference period and its annual average growth
rate was as high as 10 percent. Technical efficiency ranges between 50
to 80 percent during the reference period. TFP grew at significant rates
of 3 percent to 12 percent during 1980-1989, and at 3 percent to 8
percent during 1990-1994. Education was found to play a significant role
in technical efficiency.
Ruhul (2006) found that in food manufacturing sector in Bangladesh,
efficiency ranged between 60 to 81 percent which could be increased by
19 to 39 percent through human capital accumulation in the form of
education and on job training.
Cheema (1978) found high productivity growth rate and significant
contribution in the manufacturing sector of Pakistan while Ahmed (1980)
who estimated productivity growth for the period 1958-70 found low gains
in labour productivity. Kemal (1981) analysed the impact of
technological change and technical efficiency for the period 1959-60 to
1969-70. He found overall decreasing returns to scale. Kemal and Ahmed
(1992) estimated technological change, efficiency growth and
productivity for agriculture and manufacturing as well as for the whole
economy of Pakistan, but their studies suffer from certain limitations
due to use of various functional forms to get estimates of technical
efficiency without determining which form was appropriate for which
industry.
Kemal, et al. (2002) analysed technological change, technical
efficiency and TFP for Pakistan. According to their estimates, TFP grew
at a rate of 1.66 percent for the period 1964-65 to 2000-01 and its
share in growth of GDP was roughly one-third during the period. TFP in
the manufacturing sector showed an average growth of 3.21 percent during
1964-65 to 2000-01 and 4 percent during the sixties mainly through the
process of learning by doing coupled with improved export
competitiveness.
Khan (1989) measured elasticity of substitutions between inputs,
technical progress and returns to scale in the manufacturing sector of
Pakistan by using two-level CES production function. He calculated low
elasticity between labour, capital and energy and found that the
manufacturing sector was exhibiting decreasing returns to scale having
experienced disembodied technical progress at the rate of 3.7 percent
per annum.
Mahmood (1989) used Translog Cost Function to estimate derived
demand for factors in the large-scale manufacturing sector of Pakistan.
The estimations found that capital and energy were complementary, and
labour, capital and energy were substitutes. The lifting of any subsidy
on energy and capital would tend to reduce the energy and capital
intensity and, in turn, would increase the labour-intensity in the large
scale manufacturing sector of Pakistan. According to his results,
adoption of such a policy could help in reducing the burden of
unemployment.
Mahmood (1992) further used Translog Cost Function to estimate the
effects of change in government's pricing policy and external price
shocks on factors demand for the industrial sector of Pakistan.
According to this study, the skills of the labour force improve with
technological advancements and growth in income. He found that
production and non-production workers were high substitutes in the
pre-energy shock period and had become marginally stronger substitutes
in the post-energy shock period.
Ali and Hamid (1996) measured technological change, technical
efficiency, productivity and their impact on input demand for
agriculture and manufacturing in Pakistan. They found that major
contributors to value-added growth in both sectors were primarily
traditional factors of production.
Tariq, et al. (1997) estimated factor substitution, technical
efficiency and employment generation in large scale manufacturing
industries of Pakistan and found that technological change was capital
intensive and labour saving.
Mahmood and Siddiqui (2000) measured TFP for manufacturing in
Pakistan over the period 1972 to 1997. They found that increased
expenditure on R&D, growth of scientific and technical manpower and
growth in knowledge and human capital had a significantly positive
impact on TFP growth in manufacturing. Knowledge and human capital were
found to explain 30 percent and 18 percent of the variance in TFP,
respectively, They also found positive and significant impact of
openness and trade liberalisation on TFP.
This review of relevant literature reveals that human capital,
technological change and technical efficiency are important sources of
growth in the developed countries but these factors have exhibited less
importance in developing countries like Pakistan. As a result, only a
few studies on human capital accumulation, technological progress and
technical efficiency are available especially on Pakistan, and those few
also suffer from certain analytical limitations using e.g., Hicks
neutral technological change assuming that technological change is
happening at a constant rate. In the present research, technological
change, technical efficiency, and productivity growth are measured for
the manufacturing sector of Pakistan. Besides this, the study also
endeavours to measure the major determinants of growth and productivity
and the absolute and relative shares of these determinants of
value-added growth and TFP in the manufacturing sector. Empirical
analysis with international comparisons will be made in this background
with related policy implications and conclusions.
III. METHODOLOGY
The following factors are assumed to be the major contributors to
value-added growth in manufacturing:
Y = f( A, L, K, H) ... ... ... ... ... ... ... (1)
Y = Value-added growth in manufacturing.
L = Labour employed in manufacturing.
K = Capital stock in manufacturing.
A = Level of technology.
H = Human resources in manufacturing. (4)
Labour and capital have since long been considered as among the
most important factors of production in the literature. The more recent
studies like Romer (1986, 1990), He and Liu (2006) and Yanuri (2006),
etc., have also used the level of technology and human capital as
important factors in the analysis of total factor productivity. This
paper also assumes both of these factors as key contributors to
value-added growth in manufacturing along with traditional factors of
labour and capital. In order to measure the major factors contributing
to value-added growth, technological change and technical efficiency
(which in-builds overtime due to human capital formation), the Translog
production function for labour, capital, human capital and technology
can be written as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
The following homogeneity constraints are implied in the Translog
production function:
([[alpha].sub.LK] = [[alpha].sub.KL], [[alpha].sub.HK] =
[[alpha].sub.KH], [[alpha].sub.HL] = [[alpha].sub.LH)]
[summation]([[alpha].sub.K], [[alpha].sub.L], [[alpha].sub.H] = 1
[[alpha].sub.KK], [[alpha].sub.KL], [[alpha].sub.KH] = 0
[[alpha].sub.KL], [[alpha].sub.LL], [[alpha].sub.LH] = 0
[[alpha].sub.KH], [[alpha].sub.LH], [[alpha].sub.HH] = 0
Subject to these homogeneity constraints, the Translog production
function will be estimated in conjunction with a cost share function
with cross-equation restrictions imposed, a method suggested by Berndt
and Christensen (1973). Labour, capital and human capital cost share
equations are derived as:
[CS.sub.L] = [partial derivative]lnY/[partial derivative]lnL =
[[alpha].sub.L], [[alpha].sub.LL]lnL + [[alpha].sub.LK]lnK +
[[alpha].sub.LH]lnH + [u.sub.it] ... ... (3)
[CS.sub.K] = [partial derivative]lnY/[partial derivative]lnK =
[[alpha].sub.K], [[alpha].sub.KK]lnK + [[alpha].sub.KL]lnL +
[[alpha].sub.KH]lnH + [u.sub.it] ... ... (4)
[CS.sub.L] = [partial derivative]lnY/[partial derivative]lnH =
[[alpha].sub.H], [[alpha].sub.HH]lnL + [[alpha].sub.HL]lnL +
[[alpha].sub.HK]lnH + [u.sub.it] ... ... (5)
Where
[CS.sub.L], [CS.sub.K], [CS.sub.H] are the labour, capital and
human capital shares of total cost, respectively. (5)
The cost share equations will be estimated by applying
"Seemingly Unrelated Regressions" [Zellner (1962)]. The
Translog production function can also be estimated by using a Stochastic
Frontier Approach (SFA), adopting a more flexible approach for
restriction conditions. (6)
Following Baltagi and Griffin (1988) and Kumbhakar (2003), single
output Translog cost function in the form of time trend model is written
as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where
[[beta].sub.jk] = [[beta].sub.kj],
[[summation].sub.j][[beta].sub.j] = 1,
[[summation].sub.j][[beta].sub.jk] = 0,
[[summation].sub.j][[beta].sub.jy] = 0, and
[[summation].sub.j][[beta].sub.jt] = 0
The first restriction is due to symmetry; the rest due to the fact
that cost function is homogenous of degree one in the input prices.
where
C = total cost
Y = output
[P.sub.j] = jth input price
From the Translog cost function given in Equation 9, technological
change is measured as follows (technological change being defined as the
percentage change in the total cost over time ceteris paribus):
-[partial derivative ln[C.sub.it]/[partial derivative]t =
-[[beta].sub.t] + [[beta].sub.tt]t +
[[summation].sub.j][[beta].sub.jt]ln[P.sub.jit] +
[[beta].sub.yt]ln[Y.sub.it]] ... ... ... (7)
Measurement of Total Factor Productivity (TFP) Change Index
The TFP change index is defined as the difference between rate of
change of output and rate of change of inputs:
[TFP.sup.*] = [y.sup.*-] [x.sup.*] ... ... ... ... ... ... ... (8)
Where
[TEP.sup.*] TFP = total factor productivity change index
[y.sup.*] = rate of change of output
[x.sup.*] = rate of change of inputs
TFP growth can be estimated by subtracting the contribution of
measured inputs growth from output growth.
Measurement of Absolute and Relative Contribution
The method for calculation of absolute contribution was introduced
by Hicks (1979) and calculation of relative contribution by
Hadjimichael, et al. (1995). The absolute share of any factor of
production towards growth can be found by multiplying the estimated
coefficient of the explanatory variable by standard deviation of the
respective explanatory variable. The relative contribution for each
independent variable can be measured by dividing its estimated absolute
share by the standard deviation of the dependent variable. The relative
share of variables will be unit free.
Data and Variables Description
The above model for the measurement of major factors contributing
to value-added growth and productivity, technological change, technical
efficiency and relative and absolute share of factors in the
manufacturing sector of Pakistan is based on the following variables and
data sources (data series covers the period from 1972-73 to 200607). All
data is converted on 1980-81 constant market prices. Real value-added in
manufacturing sector on constant market prices is used and data sources
for value-added include the Pakistan Economic Survey (1990-91 and
2007-08) and Pakistan (1999) 50 Years of Pakistan in Statistics: Volume
I-IV. The number of employed workers in manufacturing is used as a
labour input and data on the variable is taken from Pakistan Economic
Survey (1990-91 and 2007-08) and Pakistan (1999) 50 Years of Pakistan in
Statistics: Volume I-IV. Enrolment in the secondary, higher and other
categories like professional, vocational colleges, universities and
other institutes as a ratio to total employed labour force in the
manufacturing is used as a proxy to measure the impact of human capital
(7) in the manufacturing; data sources include Annual Education
Statistics (Various Issues); Pakistan Statistical Years Book (Various
Issues); Pakistan Economic Survey (1990-91 and 2007-08); Pakistan (1999)
50 Years of Pakistan in Statistics: Volume I-IV; Human Development
Report, UNDP (2007) and World Development Report (2007).
Capital Stock
Capital stock is measured by using perpetual inventory method as
per following equation:
[K.sub.t] = [I.sub.t] + (1 - [phi])[K.sub.t-1] ... ... ... ... ...
... (9)
Where
[K.sub.t] = Capital Stock in current year.
[K.sub.t-1] = Capital Stock in previous year.
[I.sub.t] = Current Year Investment or Gross Fixed Capital
Formation.
[phi] = Depreciation rate.
For estimating the initial capital stock K(0), the method used by
Nehru and Dhareshwar (1993) and Khan (2006) is being followed. The
capital stock series will be generated in the following way:
[K.sub.t] = [I.sub.t] + [(1 - [phi]).sup.t] K(0) +
[summation][I.sub.t-i] [(1 - [phi]).sup.t] ... ... ... ... (10)
Where
I = o to t-1
K(0) = initial capital stock in year zero.
Nehru and Dhareshwar (1993) and Khan (2006) used a modified
Harberger (1978) method to estimate K(0). The value of investment for
the first year is estimated by way of a linear regression equation of
the log of investment against time. The estimated value of investment
for the base or zero year is used to calculate K(0) as per following
equation:
K(0)= [I.sub.t] /(gr + [phi]) ... ... ... ... ... ... (11)
Where
gr = Compound value-added growth rate
[phi] = Depreciation rate
Various depreciation rates have been used in empirical studies.
Here 5 percent capital depreciation rate is assumed. Several other
studies used 4 percent depreciation rate e.g., Nehru and Dhareshwar
(1993), Collins and Bosworth (1997), Khan (2006), etc.
IV. EMPIRICAL FINDINGS
The measurement of major determinants of value-added growth and the
contribution of factor inputs, technological change and technical
efficiency to value-added growth in the manufacturing sector of Pakistan
is presented in this section. It also includes calculation of relative
and absolute shares of factor inputs in the value-added and the
measurement of total factor productivity change index (TFPI).
Translog Production Function Estimates for the Manufacturing Sector
Table 1 presents the estimated results for Translog Production
Function in manufacturing. Zellener's Seemingly Unrelated
Regression Equations (SURE) technique has been used to find the
estimations. All the results are according to theoretical expectations.
The estimated coefficient for technology is 0.02, a positive
contribution towards the value-added growth in manufacturing. The
estimated t-value shows that the coefficient is significant at 1 percent
level of significance. The estimated coefficient for capital stock (K)
is 0.65 and has a 1 percent level of significance. The estimated
coefficient for labour and human capital are 0.15 and 0.20 respectively.
(8) The contribution of factor inputs, technological change and
technical efficiency to the value added in manufacturing is presented in
Table 2. The estimated share for capital (K), Labour (L) and Human
Capital (HK) are taken from Translog production function estimations
presented in Table 1 and these are 0.65, 0.15 and 0.20 respectively. The
weighted growth rates for K, L and HK are measured by multiplying
average growth rates of these variables by their respective estimated
coefficients in Table 1. The calculated weighted growth rates for K, L
and HK are 3.83 percent, 0.48 percent and 1.00 percent respectively. The
share of total factor inputs growth in value-added of manufacturing
accounted for 5.31 percent. The share of technological change and
technical efficiency in value-added is the difference between the
average value-added growth (6.6 percent) and total factor inputs
weighted growth rates (5.31 percent). This estimated TFP contribution
accounted for 1.29 percent. The estimated shares as percentage of total
value-added growth in manufacturing show that capital stock contributes
the maximum (58 percent) while labour and human capital contributes 07
percent and 15 percent respectively. One reason for this insignificant
role of human capital is that technical and vocational training is given
low priority and is not of that quality in Pakistan which enables human
capital to bring new significant technological changes and can compete
internationally and absorb technological spillovers from the advanced
world. The total contribution of input factors accounts for 80 percent.
The contribution of technological change and technical efficiency (TFP)
is 20 percent and the contribution of TFP and human capital in
value-added growth rate of manufacturing accounts for 35 percent.
Measurement of Absolute and Relative Contribution to Manufacturing
Value-added and Calculation of TFP Change Index
The estimated results are depicted in Tables 3 and 4 respectively.
The absolute shares for capital, labour and human capital are 0.398,
0.039 and 0.076 respectively. The relative shares for these explanatory
variables follow the same pattern which for capital, labour and human
capital are 0.621, 0.061 and 0.119 respectively. The measurement of
absolute and relative shares show that value-added growth in
manufacturing depends more on physical factors of production and less on
human capital.
Table 4 presents the calculations for total factor productivity
(TFP) change index in the manufacturing sector from the estimations
based on Translog production function given in Table 1. Column (2) in
Table 4 shows overtime rate of change of value-added in the
manufacturing sector, while columns (3), (4) and (5) show overtime
weighted rate of change of inputs i.e. weighted rate of change in
capital, labour and human capital. The aggregated weighted rate of
change of manufacturing inputs is presented in column (6). The
difference between column (2) and column (6) i.e. difference between
rate of change of value-added and the rate of change of aggregated
weighted inputs is given in column (7) which is the overtime TFP change.
The three years moving average growth counts for the sample period are
measured at 0.0644, 0.0496 and 0.0147 for value-added, weighted
aggregated inputs and TFP respectively. The last column in Table 4 shows
the TFP Index which has changed from 100 in 1972-73 to 147.11 in
2004-05.
V. SUMMARY AND CONCLUSIONS
The findings of this study show that conventional factors of
production are still the mainstay for value-added growth in
manufacturing, contributing about 65 percent of the total value-added
growth, while the shares of human capital, technological changes and
technical efficiency were measured at 14 percent and 22 percent
respectively. TFP along with human capital was contributing around 35
percent to the total growth in the manufacturing sector. This is
significant but not up to the required level as in case of developed and
in some developing countries its share has been reported at over 50
percent. Based on the empirical findings, the following recommendations
and conclusions may be derived:
* Human capital should be given top priority by allocating more
resources to education, training, health and to other measures along
with physical factors so that human capital can properly be used for
enhancing growth of the economy.
* Education policies should be devised according to the requirement
of the economy and technical, vocational and professional education must
be given top priority as the manufacturing sector has very high need for
technical and vocational labour force.
* As openness of the economy is important for raising value-added
growth and attracting more technological innovations and spillovers,
there is need to search for new markets for Pakistani products through
international publicity and interaction with other countries, especially
with less developed and neighbouring countries.
REFERENCES
Abramovitz, Modrd (1956) Resource and Output Trends in the United
States since 1870. American Economic Review 46:2, 5-23.
Aghion, P. and Peter Howitt (1992) A Model of Growth through
Creative Destruction. Econometrica 60:2, 323-351.
Ahmed, M. A. (1980) Factors Which Hinder or Help Productivity
Improvements in Asian Region. Country Paper for Pakistan. Asian
Productivity Organisation, Tokyo.
Aigner, A., C. A. K. Lovell, and P. Schmidt (1977) Formulation and
Estimation of Stochastic Frontier Production Function Models. Journal of
Econometrics 86, 21-37.
Ali, K. and Abdul Hamid (1996) Technical Change, Technical
Efficiency, and Their Impact on Input Demand in the Agricultural and
Manufacturing Sectors of Pakistan. The Pakistan Development Review 35:3,
215-228.
Baltagi, B. H. and J. M. Griffin (1988) A General Index of
Technical Change. Journal of Political Economy 96, 20-41.
Batra, Geeta and Hong Tan (2003) SME Technical Efficiency and its
Correlates: Cross-National Evidence and Policy Implications. World Bank
Institute (Working Paper).
Berndt, E. R. and L. R. Christensen (1973) The Translog Function
and the Substitution of Equipment, Structures, and Labour in US
Manufacturing 1929-68. Journal of Econometrics 1, 81-114.
Cheema, Aftab Ahmad (1978) Productivity Trends in the Manufacturing
Industries. The Pakistan Development Review 17:1, 55-65.
Coe, D., E. Helpman, and A. Hoffmeister (1997) North-South R&D
Spillovers. Economic Journal 107, 134-149.
Collins, S. and B. P. Bosworth (1997) Economic Growth in South
Asia: Accumulation Versus Assimilation. Brookings Papers on Economic
Activity 2, 135-203.
Ferguson, C. (1965) Time Series Production Functions and
Technological Progress in American Manufacturing Industry. Journal of
Political Economy 73:2, 135-47.
Grossman, G. and E. Helpman (1991) Quality Ladders in the Theory of
Growth. Review of Economic Studies 58, 43-61.
Hadjimichael, M. T., D. Ghura, M. Muhleisen, R. Nord, and E. M.
Ucer (1995) Sub-Saharan Africa Growth, Savings and Investment, 1986-93.
Washington, DC. International Monetary Fund.
Harberger, A. (1978) Perspectives on Capital and Technology in Less
Developed Countries. In M. Artis and A. Nobay (eds.) Contemporary
Economic Analysis. London: Croom Helm.
He, Hui and Zheng Liu (2006) Investment-Specific Technical Change
and the Dynamics of Skill Accumulation and Wage Inequality. University
of Minnesota and Emory University, February.
Hicks, N. L. (1979) Growth vs. Basic Needs: Is there a Trade-off?
World Development 7, 985-994.
Hulten, C. R. (1973) The Measurement of Total Factor Productivity
in United States Manufacturing: 1948-66. PhD Dissertation, University of
California, Berkeley.
Keller, Wolfgang and Stephen R. Yeaple (2003) Multinational
Enterprises, Intentional Trade and Productivity Growth: Firm Level
Evidence from the United States. Brown University. (Working Paper).
Kemal, A. R. (1981) Substitution Elasticities in the Large Scale
Manufacturing Industries of Pakistan. The Pakistan Development Review
20:1, 1-36.
Kemal, A. R. (1993) Sources of Growth in Pakistan. Report on
Economic and Social Well-being for the Eighth Five Year Plan. Islamabad,
Pakistan Institute of Development Economics.
Kemal, A. R. and Islam Ahmed (1992) Report of the Sub-Committee on
Sources of Growth in Pakistan, Islamabad. Pakistan Institute of
Development Economics, Islamabad, Pakistan.
Kemal, A. R., Musleh ud Din, and Usman Qadir (2002) Global Research
Project: Pakistan Country Report, Global Research Project Workshop,
Dhaka.
Kendrick, John W. (1973) Postwar Productivity Trends in United
States: 1948-66. New York: National Bureau of Economic Research.
Khan, Ashfaque H. (1989) The Two-level CES Production Function for
the Manufacturing Sector of Pakistan. The Pakistan Development Review
28: 1, 1-12.
Khan, Safdar Ullah (2006) Macro Determinants of Total Factor
Productivity in Pakistan. State Bank of Pakistan. (Working Papers,
February, No. 10.)
Kim, Sangho and Young Hoon Lee (2006) The Productivity Debate of
East-Asia Revisited: A Stochastic Frontier Approach. Applied Economics
38, 1697-1706.
Kruger, Jens J. (2003) The Global Trends of Total Factor
Productivity: Evidence from the Nonparametric Malmquist Index Approach.
Oxford Economic Papers 55, 265-286.
Kumbhakar, Subal C. (2003) Factor Productivity and Technical
Change. Applied Economics Letters 10, 291-297.
Mahmood, Z. (1989) Derived Demand for Factors in the Large-scale
Manufacturing Sector of Pakistan. The Pakistan Development Review 28: 4,
731-742.
Mahmood, Z. (1992) Factor Price Shocks, Factor Substitution and
Their Implications for Policy. International Economic Journal 6: 4,
63-73.
Mahmood, Z. and Rehana Siddiqui (2000) State of Technology and
Productivity in Pakistan's Manufacturing Industries: Some Strategic
Directions to Build Technological Competence. The Pakistan Development
Review 39:1, 1-21.
Meeusen, W. and J. Van Den Broeck (1997) Efficiency Estimation from
Cobb-Douglas Production Function with Composed Error. International
Economic Review 18, 435-444.
Nehru, V. and A. Dhareshwar (1993) A New Database on Physical
Capital Stock: Source, Methodology and Result. Revista De Analisis
Economico 8, 37-59.
Nikaido, Yuko (2004) Technical Efficiency of Small Scale Industry
Application of Stochastic Production Frontier Model. Economic and
Political Weekly February, 7.
Pakistan, Government of (1999) 50 Years of Pakistan in Statistics.
Volume I-IV. Federal Bureau of Statistics, Islamabad.
Pakistan, Government of (Various Issues) Pakistan Education
Statistics. Academy of Education Planning and Management, Ministry of
Education, Islamabad.
Pakistan, Government of (Various Issues) Census of Manufacturing
Industries. Statistics Division, Federal Bureau of Statistics.
Pakistan, Government of (Various Issues) Labour Force Survey.
Statistics Division, Federal Bureau of Statistics.
Pakistan, Government of (Various Issues) Pakistan Economic Survey.
Finance Division.
Pakistan, Government of (Various Issues) Pakistan Statistical Year
Book. Statistics Division, Federal Bureau of Statistics.
Perelman, S. (1995) R&D, Technological Progress and Efficiency
Change in Industrial Activities. Review of Income and Wealth 41,
349-366.
Perli, R. and P. Sakellaris (1998) Human Capital Formation and
Business Cycle Persistence. Journal of Monetary Economics 42, 67-92.
Rattso, J. and H. Stokke (2003) Learning by Doing and Domestic and
Foreign Technology Spillovers in Thailand: Some Empirical Evidence.
Nordic Journal of Political Economy 29:1, 47-66.
Robinson, Sherman (1971) Sources of Growth in Less Developed
Countries: A Cross-Section Study. Quarterly Journal of Economics 85:3,
391-408.
Romer, P. M. (1990) Endogenous Technical Change. Journal of
Political Economy 98, S71-S102.
Romer, P. (1986) Increasing Returns and Long-run Growth. Journal of
Political Economy 94, 1002-1037.
Ruhul, A. Salim (2006) Measuring Productive Efficiency
Incorporating Firms' Heterogeneity: An Empirical Analysis. Journal
of Economic Development 31:1, 135-147.
Schultz, T. W. (1962) Reflections on Investment in Man. Journal of
Political Economy 70, 1-8.
Schultz, T. W. (1964) Transforming Traditional Agriculture. New
Haven: Yale University Press.
Solow, R. M. (1957) Technical Change and Aggregate Production
Function. Review of Economics and Statistics 39:3, 312-320.
State Bank of Pakistan (Various Issues) Annual Reports. Karachi:
State Bank of Pakistan.
Tariq, S., Karamat Ali, and Abdul Hamid (1997) Factor Substitution,
Technical Efficiency and Employment Generation Prospects in the Large
Scale Manufacturing Industries of Pakistan. Journal of Research (Hum.)
14, 141-152.
Taymaz, E. and Bulin Saatci (1997) Technical Change and Efficiency
in Turkish Manufacturing Industries. Journal of Productivity Analysis 8,
461-475.
UNDP (2006) Human Development Report 2006. New York: Oxford
University Press.
World Bank (Various Issues) World Development Reports. World Bank.
Yanrui, Wu (1995) Productivity Growth, Technological Progress, and
Technical Efficiency Change in China: A Three Sector Analysis. Journal
of Comparative Economics 21:2, 207-229.
Zellner, A. (1962) An Efficient Method of Estimation Seemingly
Unrelated Regressions and Tests for Aggregation Bias. Journal of
American Statistical Association, June.
Zheng, J. Xiaoxuan Liu and Arne Bigsten (2003) Efficiency,
Technical Progress, and Best Practice in Chinese State Enterprises
(1980-1994). Journal of Comparative Economics 31:1, 134-152.
(1) Pakistan Economic Survey 2005-06 and Labour Force Survey
2005-06, Government of Pakistan.
(2) Knowledge is a non-rival good as knowledge of one thing can
simultaneously be used by others without additional cost.
(3) Perli and Sakellaris (1998) estimated that expenditures related
to on the job training (OJT) in 1987 were about $165 billion, while
total educational expenditure was about $331 billion. These numbers
suggest that OJT expenditure may account for as much as half of total
educational expenditure in the US.
(4) Human resource development activities like education,
professional and vocational training, R&D activities etc.
(5) The coefficients of Translog function can also be estimated
from Equation (2) by using OLS technique. However, there may occur
multicollinearity problem (as labour and capital increase with a
specific ratio which results in the existence of a relationship between
two explanatory variables and this specific relationship causes
multicollinearity problem). In order to overcome this problem we have
estimated cost share equations by applying SURE. Parameters of variable
H can be estimated from cost share of labour and cost share of capital
by using equality constraints.
(6) Stochastic Frontier Approach (SFA) was introduced by Aigner, et
al. (1977) and Meeusen and Van den Broeck (1977).
(7) Five years lag enrolment was used for secondary education and
four years lag for enrolment in other categories like professional and
vocational institutions and enrolment in universities etc.
(8) The value of coefficient for human capital is calculated from
the constraints as explained in methodology.
Abdul Hamid <ahamid_dcma@hotmal.com> is Income Distribution
Specialist in the Centre for Poverty Reduction and Social Policy
Development (CPRSPD). A joint project of UNDP and Planning Commission,
Government of Pakistan. J. Hanns Pichler <jhp@wu-wien.ac.at> is
Professor of Economics at Vienna University of Economics and Business,
Austria.
Table 1
Translog Production Function Estimates for the
Manufacturing Sector
(1971-72 to 2000-01)
Dependent Variable
Independent
Variables Value-Added
Constant 3.52 ***
(77.68)
A 0.02
(7.95)
In [K.sub.it] 0.650 ***
(2.82)
In [L.sub.it] 0.15
(0.84)
In [H.sub.it] 0.20 (c]
(In [K.sub.it]) (2) 0.023
(1.18)
(In [L.sub.it]) (2) -0.0008
(0.60)
(In [H.sub.it]) (2) 0.0038 (e)
lnL*InK -0.0055
(0.38)
InL*1nH -0.0031
(0.22)
InK*lnH -0.0007
(0.04)
[R.sup.2] 0.93
Adj-[R.sup.2] 0.92
SER 0.005
DW- stat 1.66
N 30
Notes: Values in parenthesis are t-ratios.
*** Significant at 1 percent level.
** Significant at 5 percent level.
* Significant at 10 percent level.
The Method of Estimation is Zellner's Seemingly .
Unrelated Regression Technique (SURE).
e= Values of parameters derived from constraints .
Where:
SER= Standard Error of Regression;
DW-stat= Durbin-Watson stat; N= Number of Observations;
K= Capital Stock; L= Labour Employed; H= Human Capital
Table 2
The Contribution of Factor Inputs, Technological Change and
Technical Efficiency (TFP) to Value-added Growth in Manufacturing
Sector (Calculated frona Translog Production Function Estimates
in Table 1)
Shares
Estimated Based
Variables on Translog PF
Value-added Growth Rate 6.60
Labour Growth Rate 3.17
Capital Growth Rate 5.89
HK Growth Rate 5.02
Share of Capital in Value-added 0.65
Share of Labour in Value-added 0.15
Share of HK in Value-added 0.20
Weighted Capital Growth Rate 3.83
Weighted Labour Growth Rate 0.48
Weighted HK Growth Rate 1.00
Total Factor Input Growth
Share in Value-added Growth 5.31
Technological and Technical
Growth (TFP) Share in 1.29
Value-added Growth
Major Determinants of Growth as
Percentage of Value-added Growth
Capital Contribution 58
Labour Contribution 7
HK Contribution 15
Total Factor Inputs Contribution 80
Technological Change and
Technical Efficiency (TFP) 20
Contribution
Total 100
Share of HK, Technological Change
and Technical Efficiency 35
Source: Estimations is Table 1.
Table 3
Absolute and Relative Contributions of Major Determinants of
Growth to Manufacturing Value-added
Absolute Relative
Estimated Contribution Contribution
Standard Estimated to to
Variables Deviations Coefficients Value-added Value-added
Ln(K) 0.612 0.650 0.398 0.621
Ln(L) 0.260 0.150 0.039 0.061
Ln(H) 0.381 0.200 0.076 0.119
SD of
Dependent
Variable
(Y) 0.641 -- -- --
Where:
Y= Value-added in Manufacturing;
K= Capital Stock in Manufacturing;
L= Labour Employed in Manufacturing;
HK= Human Capital; and
SD= Standard Deviation; Estimated co-efficients are taken from
Table 1.
Table 4
Manufacturing TFP Change Index (1972-73 to 2004-05)
Based on Translog PF Estimates (3-Years Moving Average)
Aggregated
Year Y * K * L * HK * Inputs
1971-72
1972-73
1973-74 0.0501 0.0232 0.0157 0.0041 0.0430
1974-75 0.0269 0.0233 0.0422 0.0041 0.0697
1975-76 0.0125 0.0464 0.0110 0.0082 0.0656
1976-77 0.0452 0.0713 0.0117 0.0126 0.0957
1977-78 0.0675 0.0880 0.0119 0.0156 0.1154
1978-79 0.0949 0.0858 0.0079 0.0152 0.1089
1979-80 0.0970 0.0781 0.0043 0.0138 0.0963
1980-81 0.1166 0.0621 0.0004 0.0110 0.0735
1981-82 0.1031 0.0484 0.0009 0.0086 0.0578
1982-83 0.0935 0.0410 0.0032 0.0073 0.0515
1983-84 0.0769 0.0417 0.0052 0.0074 0.0543
1984-85 0.0783 0.0426 0.0016 0.0075 0.0518
1985-86 0.0771 0.0424 0.0087 0.0075 0.0585
1986-87 0.0841 0.0340 -0.0004 0.0060 0.0396
1987-88 0.0704 0.0259 0.0043 0.0046 0.0348
1988-89 0.0643 0.0200 -0.0031 0.0035 0.0205
1989-90 0.0516 0.0225 -0.0004 0.0040 0.0261
1990-91 0.0673 0.0268 0.0008 0.0047 0.0323
1991-92 0.0654 0.0355 0.0012 0.0063 0.0430
1992-93 0.0637 0.0400 -0.0085 0.0071 0.0386
1993-94 0.0478 0.0426 -0.0092 0.0075 0.0410
1994-95 0.0461 0.0309 -0.0132 0.0055 0.0233
1995-96 0.0322 0.0237 0.0149 0.0042 0.0428
1996-97 0.0143 0.0172 0.0067 0.0030 0.0269
1997-98 0.0112 0.0192 0.0071 0.0034 0.0297
1998-99 0.0128 0.0186 0.0074 0.0033 0.0293
1999-00 0.0464 0.0177 0.0114 0.0031 0.0322
2000-01 0.0505 0.0148 0.0332 0.0026 0.0505
2001-02 0.0698 0.0142 0.0188 0.0025 0.0355
2002-03 0.0908 0.0169 0.0219 0.0030 0.0418
2003-04 0.1156 0.0194 0.0041 0.0034 0.0270
2004-05 0.1151 0.0206 0.0072 0.0036 0.0314
Average 0.0644 0.0361 0.0072 0.0064 0.0496
Year TFP * TFPI
1971-72
1972-73 100.00
1973-74 0.0071 100.71
1974-75 -0.0428 96.43
1975-76 -0.0531 91.12
1976-77 -0.0504 86.08
1977-78 -0.0479 81.29
1978-79 -0.0140 79.89
1979-80 0.0008 79.96
1980-81 0.0431 84.28
1981-82 0.0453 88.81
1982-83 0.0420 93.01
1983-84 0.0227 95.28
1984-85 0.0265 97.93
1985-86 0.0185 99.78
1986-87 0.0445 104.24
1987-88 0.0356 107.80
1988-89 0.0438 112.18
1989-90 0.0255 114.73
1990-91 0.0350 118.23
1991-92 0.0225 120.48
1992-93 0.0251 122.99
1993-94 0.0068 123.67
1994-95 0.0229 125.96
1995-96 -0.0106 124.90
1996-97 -0.0126 123.64
1997-98 -0.0185 121.79
1998-99 -0.0165 120.14
1999-00 0.0142 121.56
2000-01 -0.0001 121.55
2001-02 0.0342 124.97
2002-03 0.0490 129.87
2003-04 0.0887 138.74
2004-05 0.0837 147.11
Average 0.0147
Notes:
Y= Manufacturing Value-added; K= Capital Stock;
L= Labour Employed; HK= Human Capital;
TFP= Total Factor Productivity;
TFPI= Total Factor Productivity Index;
Where over dotes show the change over time;
Growth rates for K, L and HK are weighted growth rates;
Weights are taken from estimated Translog Production Function in
Table 1.
