T Liberalization and Tech Efficiency BD Hossain

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Trade Liberalisation and Technical Efficiency: Evidence

from Bangladesh Manufacturing IndustriesMA Hossain; ND Karunaratne

Online Publication Date: 01 February 2004

To cite this Article: Hossain, MA and Karunaratne, ND (2004) 'Trade Liberalisationand Technical Efficiency: Evidence from Bangladesh Manufacturing Industries',Journal of Development Studies, 40:3, 87 114

To link to this article: DOI: 10.1080/0022038042000213210URL: http://dx.doi.org/10.1080/0022038042000213210

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Trade Liberalisation and Technical Efficiency:

Evidence from Bangladesh Manufacturing

Industries

M.A. HOSSAIN and N.D. KARUNARATNE

The paper investigates the effects of trade liberalisation on the

technical efficiency of the Bangladesh manufacturing sector by

estimating a combined stochastic frontier-inefficiency model using

panel data for the period 197894 for 25 three-digit levelindustries. The results show that the overall technical efficiency of

the manufacturing sector as well as the technical efficiencies of the

majority of the individual industries has increased over time. The

findings also clearly suggest that trade liberalisation, proxied by

export orientation and capital deepening, has had significant

impact on the reduction of the overall technical inefficiency.Similarly, the scale of operation and the proportion of non-

production labour in total employment appear as important

determinants of technical inefficiency. The evidence also indicates

that both export-promoting and import-substituting industries have

experienced rises in technical efficiencies over time. Besides, the

results are suggestive of neutral technical change, although (at the5 per cent level of significance) the empirical results indicate that

there was no technical change in the manufacturing industries.

Finally, the joint test based on the likelihood ratio (LR) test rejects

the Cobb-Douglas production technology as description of the


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and quality of physical and human capital, technological know-how,

experience, managerial skills, market structure, and the degree ofcompetition, among others. Likewise, changes in government policies such

as the demand management policies, deregulation, trade, and industrial

policies can also affect the technical efficiencies of the firms or industries.

The present study concerns the empirical assessment of the impact of the

trade policy reforms, represented by export orientation and capital

deepening, on the technical (in)efficiencies of 25 three-digit manufacturingindustries of Bangladesh in a panel-data stochastic frontier modelling

framework covering the period 197894. Empirical modelling of the

stochastic frontier production function based on a single cross-section

requires strong explicit assumptions, such as the exponential and the

positive half normal, about the distribution of the statistical noise and the

inefficiency variable terms. These assumptions are not necessary in the caseof panel data modelling. Schmidt and Sickles (1984) point out three major

advantages regarding the use of panel data in the context of frontier

production analysis. First, the panel data approach provides consistent

estimation of the parameters without any particular assumptions about the

distributional specification for the efficiency disturbance. Second, the

assumption that inefficiency and the factor input levels are independent can

be relaxed. And, finally, panel data models can distinguish the technical

inefficiency component of the disturbance from the statistical noise

component at the individual unit more accurately than a single cross-

section. Further, unlike a single cross section, panel data models provide

consistent estimates of the individual technical inefficiencies [ Jondrow et

al., 1982; Kalirajan and Flinn, 1983].1

The efficiency estimates in the present study are based on the application

of the combined stochastic frontier and inefficiency models as suggested by

Battese and Coelli [1995]. Apart from looking at the direction of change in

technical inefficiency as a function of export orientation and capital

deepening among others the st d compares the changes in the mean

88 THE JOURNAL OF DEVELOPMENT STUDIES


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Empirical studies on the effects of trade liberalisation on technical

efficiency provide mixed results. Generally speaking, the literature on tradeliberalisation and efficiency is yet to flourish. It is the dearth of empirical

studies, particularly on Bangladesh, that remains the principal motivating

factor behind the present study. Turning to Bangladesh, the few studies on

the issue indicate very little or no impact on technical efficiency or total

factor productivity over time and/or due to trade liberalisation [Krishna and

Sahota, 1991; Salim, 1999]. However, for reasons explained in the nextsection, the findings of these studies may not be interpreted as consequences

of trade liberalisation. These studies focused on the four-digit level

industries, thereby estimating the technical efficiency of the individual

firms. Micro-level observations are preferable to meso (three-digit

industries) or macro (two-digit industries) level data from the theoretical

point of view as the former avoid the problems of aggregation andheterogeneity. While the problems of heterogeneity and aggregations are

quite serious at the two-digit level, they are much less stringent at the three-

digit level [Meeusen and van den Broeck, 1977] and, therefore, results based

on the three-digit industries may provide useful policy implications

notwithstanding the fact that the micro-level data remain the superior

alternative.2 In terms of coverage, the study includes all the major industries

except the petroleum refining due to data limitations. Coupled with a

relatively long panel, the wider coverage of the study constitutes another

compelling reason for the use of the three-digit level data in this case as a

time series database for the four-digit level industries is hard to construct on

a consistent basis. It is expected that the wider coverage of the industries

should provide better estimates of the overall efficiency than the estimates

based on select industries.

The rest of the paper is organised as follows: Section II provides a brief

analysis of the theory on trade reform and economic efficiency, and

describes the findings of some of the empirical works to date. Section III

describes the data and the ariables sed in the st d Section IV sets o t the

89LIBERALISATION AND EFFICIENCY IN BANGLADESH


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liberalisation is based on the perceived benefits from the division of

labour, widening of the markets, and comparative advantage. The neo-classical economists view international markets not to differ

fundamentally from the domestic markets. Therefore, the usual

implications of a perfectly competitive market also apply to international

trade, which would ensure efficiency in the allocation of resources

[Corden, 1974; Krugman, 1986]. The neo-classical theory rejects

protection as a viable alternative on grounds of adverse intra-industryeffects due to imperfect competition. First, barriers to entry and absence

of foreign competition allow domestic producers to acquire monopoly

power and enjoy supernormal profits thereby failing to achieve economic

efficiency. Secondly, in a monopolistically competitive market,

restrictions on trade may attract a large number of small producers who

operate under increasing cost conditions and thus become inefficient.These two intra-industry effects are considered as more important sources

of welfare loss compared to the conventional comparative advantage

effects [Tybout, de Melo and Corbo, 1991]. From a political economy

standpoint, protection leads to a huge waste of resources by triggering

directly unproductive and profit-seeking (DUP) activities [Bhagwati,

1988; Krueger, 1974]. The new growth theories uphold trade liberalisation

by contending that technological change is endogenous rather than being

exogenous as postulated in the Solow-type neo-classical growth theory

[Romer, 1990; Aghion and Howitt, 1992, 1998]. International trade leads

to a faster diffusion of technology, and hence, higher productivity growth.

Technology is embodied in intermediate goods. New intermediate goods,

if different from or better than the existing ones, will enhance the

productivity of the importing country provided they are exported to other

countries [Grossman and Helpman, 1991; Keller, 2000]. There are also

the spillover effects due to learning-by-doing gains and better

management practices triggered by the new technology leading the firms

to ards the best practice technolog [ K 1987 L 1988



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The Bangladesh Context

Bangladesh adopted a gradualist approach in its transition towards outwardorientation. Since its emergence as an independent country in 1971,

Bangladesh followed an extremely inward-looking development strategy

until the early part of the 1980s. The country launched the outward-oriented

strategy in 1982 by initiating the implementation of the structural

adjustment programmes as per the World Bank and the IMF directives. This

was followed by second and third round changes in 1985/86 and 1991respectively. The industrial and trade policy changes lie at the heart of the

structural adjustment programmes in Bangladesh. The first phase, known as

the New Industrial Policy (NIP), focused on export diversification and

import liberalisation through a system of export performance benefits and

duty drawbacks on inputs. The second and the third round measures were

aimed at further streamlining of the trade policy regime as export, import,and exchange rate policies all underwent substantial overhauling. Continual

devaluation and full convertibility of the domestic currency in the current

account helped reduce the anti-export bias over time [ Hossain and

Karunaratne, 2002]. The provision for unrestricted and duty-free access to

imported inputs, tax rebates on export incomes and concessionary duties on

imported capital machinery have provided further incentives for exports.The import policy regime has been liberalised through successive

reductions in tariff rates and phasing out of the quantitative restrictions.

Thus, Bangladesh provides an excellent case for carrying out a direct

analysis of the effects of trade reforms on the basis of before and after

comparisons. More importantly, the regular surveys of the manufacturing

industries provide a consistent set of data to the end, the lack of which has

often been the source of unreliable and misleading empirical results

[Tybout, de Melo and Corbo, 1991].

Empirical research on the issue so far has been very scanty. Krishna and

Sahota [1991] estimate total factor productivity and technical efficiency for

30 f di it f t i i d t i i l d t f th i d



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three different years (1981, 1987 and 1991) by using four-digit firm level

data. Assuming the Cobb-Douglas production technology as the appropriatedescription of the data set and applying the corrected least squares (COLS)

regression, Salim finds both productive capacity realisation and total factor

productivity to have improved over time, and that openness was a

significant determinant of capacity realisation for the food processing

industry and a sub sector of the textile industry jute. However, an

improvement in capacity realisation and/or total factor productivity per sedoes not imply an improvement in technical efficiency. The present study

thus represents the first attempt of its kind in the context of the Bangladesh

manufacturing sector.

III . DATA AND THE DEFINITIONS OF THE VARIABLES

The data used in this study are compiled from two main official sources of

the Government of Bangladesh, namely, the Bangladesh Statistical

Yearbook (various issues) and the Report on Census of Manufacturing

Industries of Bangladesh known as CMI (various issues). The CMI data are

based on the yearly census conducted across private and public enterprises

employing 10 or more people. Both the sources routinely publish data

according to the International Standard Industrial Classification (ISIC).

Table A1 in the Appendix presents the descriptions of the 25 industries

chosen for this study. The CMI data are available for the period 197496

except 1995 as no survey was undertaken for the financial year 19945. On

the other hand, a consistent database for all the 25 industries considered in

this study are available only from 1978. The study, therefore, chooses the

sample period 197894.The variables used for the empirical analysis are defined as follows.

Output is represented by the gross value added rather than gross output.

One important reason for the preference of value added over gross output is

that it allo s comparisons bet een firms hich ma be heterogeneo s in



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replacement cost of existing machinery and equipment. In the present case,

we follow the argument of Salim and Kalirajan [1999] justifying the use offixed capital assets for Bangladesh on the ground that in a country like

Bangladesh, capital stock is more often used at approximately constant

levels of efficiency for a period far beyond the accounting life measured by

normal depreciation until it is eventually discarded or sold for scrap [Salim

and Kalirajan, 1999: 363].Labouris defined as the number of employees.

Empirical studies have alternatively used the number of employees and thenumber of man-hours for labour inputs. It is, however, highly debatable as

to which measure performs better in empirical research.3 The sources of the

data this study utilises measure labour inputs in terms of production and

non-production workers. Thus labour is represented by the sum total of

production and non-production workers.

Capital deepeningis defined as the ratio of capital to labour and is usedas a proxy for import liberalisation. From the theoretical point view, this can

be considered reasonable as reductions in tariff rates and quantitative

restrictions may lead to an increase in imported capital. It may be pointed

out here that reductions in tariff rates and quantitative restrictions have

constituted an important element in the consolidation and restructuring of

the Bangladesh import regime.

Export orientation is defined as the ratio of annual export to output of

each industry. Since the CMI does not record the share of export of the

individual firms surveyed, this study uses the ratio of the overall exports of

a three-digit industry to the respective level of output. The export figures are

constructed from the relevant four-digit level entries within each three-digit

level industry. The latter can be justified as a proxy for the former since the

CMI covers more than 60 per cent of total manufacturing establishments.Other variables considered in the study are theproportion of non-production

workers in total employment, and intermediate inputs, the latter being

defined as gross output less gross value added.

The o tp t ariable is deflated b the holesale price inde of ind strial



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problems of under coverage and under-reporting may seriously affect the

estimated results especially with aggregated panel data. First, CMI does notreport information on all the firms or enterprises registered or operating in

the economy. Second, in most cases, the number of firms covered and

lumped into the three-digit industries varies over time. And, third, perhaps

most importantly, the same firms are not reported each year. All this may

leave comparisons across aggregated industries and through time

misleading. Assuming that the cross sections of the firms in terms of size areappropriately represented through simple random sampling, the present

study chooses to use the average figures, that is, the value of the variable per

reporting firm.

IV. EMPIRICAL TECHNIQUE

Following the pioneering work of Farrell [1957], the literature on technical

efficiency measures provides a wide variety of models, parametric or non-

parametric, to predict technical efficiency at the firm or industry level. The

core empirical techniques include: (1) the deterministic frontier production

function including the Data Envelopment Analysis (DEA); (2) the

stochastic frontier production function approach (SFA); the stochastic

varying coefficients frontier approach (SVFA); and (4) the Bayesian

approach. Of course, each of the techniques has its variants. However, no

single technique or model can claim absolute superiority over the others.4 In

practice, technical efficiency is generally measured by using either the Data

Envelopment Analysis (DEA) or the Stochastic Frontier Production

Function Approach (SFA). As mentioned earlier, both the models have

advantages and disadvantages.5 Some of the weaknesses are common toboth the models while others are model-specific. However, SFA outscores

DEA on two very important grounds. Unlike DEA, SFA accounts for noise.

The presence of a noise such as the measurement error and other random

factors s ch as eather strikes etc ma affect the placement of the DEA



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As to the practical applications, the use of the DEA has been limited

mostly to the non-profit service sectors where random influences are not animportant issue and where firms do not have a well-defined optimisation

problem such as the profit maximisation or cost minimisation. Conversely,

the SFA has been extensively applied in the fields of agriculture, education,

manufacturing, health, banking services, businesses and other areas. Some

of the applications of SFA to manufacturing include: Pitt and Lee [1981],

Page [1984], Little, Mazumdar and Page [1987], Tybout et al. [1991], Haddad and Harrison [1993], Hill and Kalirajan [1993], Brada et al.

[1997],Lundvall and Battese [2000] andKarunaratne [2001].

As mentioned before, this study applies the combined inefficiency-

stochastic frontier model as suggested inBattese and Coelli [1995], where the

inefficiency effects are specified as functions of other variables. The model

thus avoids the problems associated with the two-stage estimation proceduressuch asPitt and Lee [1981] andKalirajan [1981]. At the same time, it allows

the separate estimates of the technical efficiency changes and technical change

over time. The model is also suitable for testing various hypotheses concerning

the distributions of the inefficiency effects, the structure of the production

technology as well as the technical change. Assuming that the database from

the Bangladesh manufacturing sector can be described by a Translog

production technology, we specify the following stochastic frontier model:

where:Yit= the natural logarithm of value added for the i-th industry in the t-th year

of observation;

Kit= the natural logarithm of capital for the i-th industry in the t-th year of

obser ation;


Yit b0 bKKit bLLit bTTit bKKKit2 bLLLit

2 bTTTit2

bKLKit:Lit bKTKit:Tit bLTLit:Tit vit uit;(1)


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where:

INPit= the natural logarithm of intermediate inputs for the i-th industry inthe t-th year of observation;

KDit= capital deepening defined as the natural logarithm of capital-labour

ratio for the i-th industry in the t-th year of observation;

XORit= export orientation defined as the natural logarithm of the ratio of the

i-th industrys export over output in the t-th year of observation;

NPLit= the natural logarithm of the ratio of non-productive labour to totalemployment for the i-th industry in the t-th year of observation; and

Dj = time-specific dummies for the year 1979 through 1994.

The inclusion of time as an explanatory variable in equation (1) allows

possible shifts of the production frontier over time. However, the

parameters of the input variables are assumed to be time-invariant and

constant over industries. The error terms, vitand uit, capture the deviations

from the production frontier. The first accounts for the statistical noise in

outputs while the second accounts for technical inefficiency in production.

The four industry-specific variables included in the inefficiency model are

intermediate inputs, capital deepening, export orientation, and the

proportion of non-production workers to total employment as specified

above. The inclusion of the interaction variables involving the industry-

specific variables allows for the U-shaped and joint relationships among

these variables and the inefficiency effects.

The coefficients of the intermediate inputs measure the impact of size or

scale of operation on inefficiency. Empirical studies to date alternatively

used value added, sales proceeds, employment, or fixed assets as a proxy for

the size variable. One argument for the use of intermediate inputs as a proxyfor size is that this variable is more highly correlated with output than labour

and capital [Lundvall and Battese, 2000]. Though the quality of labour is

ignored, the capital-labour ratio remains the most commonly used measure

of capital deepening or capital intensit Intermediate inp ts capital



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Other variables that might be relevant to the present context but were not

considered are: effective rate of assistance and effective rate of protection both representing trade liberalisation. Unfortunately, neither of thesevariables can be meaningfully constructed for the three-digit industries from

the available database.

V. EMPIRICAL RESULTS

Parameters Estimates and Hypotheses Testing

The estimates of the parameters of the frontier model and the inefficiency

model as defined by equations (1) and (2) respectively are based on the

maximum-likelihood method as suggested in Battese and Coelli [1993].The estimation is done in the computer package FRONTIER 4.1 [Coelli,

1996]. The estimated parameters are presented in Table A2

6

of theAppendix. Since the results are based on the Translog production function,the individual coefficients in the frontier model cannot be directly

interpreted as elasticities since the elasticities of output with respect to the

inputs depend on the levels of the explanatory variables as well as the

subsets of the parameters.

As to the inefficiency model, larger industries appear to have smaller

values of the inefficiency effects as indicated by the negative and

statistically significant coefficients ofINPt and INPt2 and (INPt. KDt).

Capital deepening has negative coefficients involving the variableKDt2 and

all the three interaction variables. All the other coefficients, except (KDt.

XORt), are statistically significant implying that more capital-intensive

industries have smaller inefficiency effects. If reductions in tariffs and

quantitative restrictions contribute to greater acquisition of capital, theresults can be interpreted as due to trade liberalisation. Export orientation,

the key variable representing trade liberalisation, has negative and

statistically significant coefficients for the variablesXORtand (INPt.XORt)

and negative but not statistically significant coefficients for XOR 2 and



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inefficiency over time. But only the dummies for the years 1983 and 1989

through 1994 have coefficients that are significant at 10 per cent level orless. It follows from above that not all the individual parameters estimates

in the inefficiency model are statistically significant. But a decision to drop

a particular explanatory variable from the model must be based on tests of

hypotheses involving sets of parameters. Table 1 below presents the results

of hypotheses tests concerning some of the parameters as well as the

functional form of the production technology, the distributional form of theinefficiency effects, and the technical change on the basis of the generalised

likelihood-ratio statistics.

To begin with, the null hypothesis that the Cobb-Douglas production

frontier is an adequate representation of the data is rejected at the 5 per cent

level of significance given the assumption of the Translog stochastic

production frontier, which implies that input and substitution elasticities


TABLE 1

GENERALISED LIKELIHOOD RATIO TESTS OF NULL HYPOTHESES FOR

PARAMETERS IN THE STOCHASTIC FRONTIE R PRODUCTION FUNCTION FOR

THE BANGLADESH MANUFACTURING

Null hypothesis, H0 Likelihood-ratio Critical valuetest statistic ( ) Decision

KK=LL=TT= KL =KT=LT= 0 45.74* 2.05, 6 = 12.59 reject H0(Cobb-Douglas function)= 0 = 1 = =30 = 0 244.49* 2.05, 31 = 44.41 reject H0(no inefficiency effects)KT = LT= 0 2.98 2.05, 2 = 5.99 cannot reject H0(there is no technical changeT = TT = KT = LT = 0 8.54 2.05, 4 = 9.49 cannot reject H0(there is neutral technical change)1 =5= 9 =10 = 11 = 0 133.74* 2.05, 5 = 11.07 reject H0(no size effects)2 =6= 9 =12 = 13 = 0 22.20* 2.05, 5 = 11.07 reject H0(no capital deepening effects)

2,df


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vary across industries. Similarly, the null hypothesis of no technical

efficiency effects is rejected, although the value of is relatively low (0.25)meaning that technical inefficiency of production is associated with a small

proportion of total variability. Nonetheless, the rejection of the hypothesis

confirms that given the Translog stochastic frontier model, the average

response function or OLS that assumes all the firms to be fully technically

efficient is not an adequate representation of the data. The null hypothesis

of no (Hicks neutral) technical change cannot be rejected at the 5 per centlevel of significance meaning that there has been no shift in the isoquants

through time (without a change in the shape of the isoquants). Similarly, the

hypothesis of neutral technical change cannot be rejected at the 5 per cent

level of significance. However, the null hypothesis is rejected at the 10 per

cent level indicating shifts in the isoquants through time and in favour of

certain input.

The null hypotheses of no size effects, no capital deepening effects, no

export orientation effects, no proportion of non-productive workers to total

employment effects and no time specific effects are all rejected at the 5 per

cent level of significance. Similarly, the combined null hypothesis of no

capital deepening and exported orientation effects is rejected at the 5 per

cent significance level. On the basis of the results of the hypotheses testing,

we take the frontier model suggested in (1) and (2) and, therefore, the results

that follow as representative of the database used in this study.

A Test for Heterogeneity Among the Industries

As pointed out before, the pooling of aggregate data for the three-digit

industries may not be appropriate because of the existence of heterogeneityamong such broad categories of industries. In the presence of heterogeneity,

a common production technology (such as the translog production function)

may not be an adequate representation of the data. In order to check if a

common prod ction technolog is appropriate for all the 25 ind stries



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the Cobb-Douglas production technology as well as fewer inefficiency

variables is based on the fact that a translog production technology and/orthe inclusion of more inefficiency variables is impracticable given the

sample size (of 17). We then specify and estimate the same model for the

entire panel and construct the following likelihood ratio test:

which has a 2 distribution with 264 (=11 x 24) degrees of freedom. The

likelihood ratio (LR) statistic is calculated to be 106.18 which is far less

than the 2critical(= 302.90) at the 5 per cent level of significance. Thus, the

null hypothesis of the same frontier models for all the individual industries

cannot be rejected. The results, therefore, justify the specification of the

translog frontier model as a common production technology for the pooled

data.

Technical Efficiency Estimates

The computer program FRONTIER 4.1 provides the individual estimates of

technical efficiency for each industry category on a yearly basis as well as

the overall mean efficiency. The individual estimates can be used to

calculate yearly average estimates of technical efficiency for the

manufacturing sector as a whole. We calculate both the simple and weighted

average as well the median estimates, which are presented in Table A3 in the

Appendix labelled TE1 and TE2 and Median respectively.8 The table also

presents the technical efficiency estimates of the individual industries. Both

the simple and weighted average estimates of technical efficiency showsteady upward movement over time. For example, in 1978 TE1 was about

0.34, which rose to about 0.54 in 1987 and to 0.68 in 1994. The

corresponding figures for TE2 are approximately 0.38, 0.58 and 0.75

respecti el Ho e er the median efficienc estimates sho a mi ed


LR 2LLFH0 LLFH1 2LLFP X25

j1

LLFj; (5)


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plastic and plastic products, pottery and chinaware, fabricated metals,

electrical machinery, and transport equipment. Of these, the readymade

garment industry gained the most. Nine of the industries, namely, food

processing, textiles manufacturing, leather footwear, drugs and

pharmaceuticals, other chemicals, rubber and rubber products, non-metallic

mineral products, iron and steel, and non-electrical machinery have gainedmarginally. Of these industries, food processing, textiles manufacturing,

drugs and pharmaceuticals, and other chemicals maintained high levels of

efficiencies throughout the sample period. Of the other three industries,

b h i d d t i ti i t h i l ffi i hil


FIGURE 1

TECHNICAL EFFICIENCY OF BANGLADESH MANUFACTURING, 197894

(BASED ON THE FULL PANEL ESTIMATION)

Note: TE1 and TE2 denote respectively simple and weighted average estimates.


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time in terms of technical efficiency. This implies that the benefits of trade

liberalisation were not confined only to the export-oriented industries ratherthey spread across the board. At the same time, it must also be noted that in

general the import-substituting industries operate at lower levels of

efficiencies.

An Alternative Assessment

The main focus of this study is to examine if trade liberalisation enhancedthe technical efficiencies of the Bangladesh manufacturing industries. As

mentioned earlier, one way to assess the effects of trade liberalisation on

technical efficiency is to compare the estimates of technical efficiency on a

before and after basis for the manufacturing sector as a whole and/or the

individual industries. Now that the Bangladesh trade policy regime has gone

through three clearly distinct phases, namely, the pre-liberalisation period,

the transition period, and the post-transition period, it is worthwhile to

compare the changes in technical efficiency across these time periods. As

presented in columns [2][4] of Table 2 below, the (simple) average overall

technical efficiency is 0.414 for the period 197882, 0.572 for the period

198391 and about 0.700 for the period 19924. The corresponding

weighted average estimates are 0.478, 0.635 and 0.744 respectively. The

median estimates for the three phases are respectively 0.420, 0.550 and0.686. Thus, there is a clear indication of an improvement in technical

efficiency of the Bangladesh manufacturing sector through phases of the

external trade policy regime.

In order to examine further the validity of the claim above, we provide

an alternative assessment of the same by constructing three separate sub-

panels for the three phases of the Bangladesh international trade regime,namely 197882 (pre-liberalisation period), 198391 (transition period) and


TABLE 2

AVERAGE TECHNICAL EFFICIENCY OF THE BANGLADESH MANUFACTURING


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19924 (post-liberalisation period). We then specify a combined frontier

production function and inefficiency model similar to equations (1) and (2)in Section IV for each sub-panel. The parameters estimates and the overall

mean efficiencies for the sub-panels are presented in Table A4 in the

Appendix. Looking at the inefficiency model in each sub-panel, it appears

that all the four industry-specific variables either in levels or in squares or

in combination with another variable have contributed to the reduction in

technical inefficiencies. It would be interesting to compare the mean andindividual technical efficiencies based on the sub-panels estimation with

those based on the full panel estimation. The summary statistics are

presented in the last three columns of Table 2. Thesimple average technical

efficiencies for the three periods are 0.392, 0.531 and 0.554 in ascending

order of the sub-panels. Although, these figures do not exactly match with

the corresponding estimates based on the full panel estimation, they clearly

complement the latter in terms of the direction of changes in technical

efficiencies. Similar observations hold for the weighted average and the

median technical efficiency estimates.

Figure 2 presents the yearly estimates of the simple average, weighted

average and the median estimates of the overall technical efficiencies

obtained from the sub-panels estimation. The simple average (TE1) and the

median estimates show a clear upward tendency throughout the sampleperiod resembling the pattern of the full panel estimation (as in Figure 1).


FIGURE 2

TECHNICAL EFFICIENCY OF BANGLADESH MANUFACTURING, 197894

(BASED ON THE SUB-PANELS ESTIMATION)


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However, the weighted average technical efficiency (TE2) increases until

1982, shows a mixed pattern between 1982 and 1988 and registers a sharprise thereafter while the full panel estimates show an upward tendency over

the entire sample period.

With few exceptions, as presented in Table A5 in the Appendix, the

technical efficiencies of the individual industries based on the sub-panels

compare quite well in terms of the direction of changes with those based on

the full panel estimation. Of the exceptional cases, the most contrastingresults are obtained for the non-metallic mineral products with the full panel

estimation showing more or less a downward tendency while the sub-panels

estimation showing a clear upward movement. Other exceptions include: (a)

beverages (falling throughout and quite sharply after 1984 as opposed to

rising (until 1980) and then falling steadily in the full panel estimation); (b)

leather and leather products (more or less constant throughout the sample

period as opposed to an increasing tendency, especially after 1985, in the

full panel estimation); and (c) iron and steel basic industries (high and

steady throughout as opposed to relatively low technical efficiencies in the

first two years with full panel estimation).

Further Hypotheses TestingWe test the following null hypotheses with respect to the three sub-panels:

(a) the assumption of a common production technology (translog, in this

case) is appropriate for each of the sub-panels;

(b) Cobb-Douglas production technology as opposed to the translog

production technology is the appropriate description of the data foreach sub-panel; and

(c) no inefficiency effects in each of the sub-panels.

Based on the likelihood f nction statistics presented in Tables A2 and A4



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5 per cent level of significance. Thus, the assumption of a common translog

production technology for the sub-panels cannot be rejected. Hypothesis (b)is rejected for all the sub-panels on the basis of the likelihood ratio test. The

test statistics are respectively 41.94, 51.56 and 17.48 for the three sub-

panels, which are to be compared with the 2critical= 12.59. Similarly, the

null hypothesis of no inefficiency effects (= 0) is also rejected for each

sub-panel. As presented in Table A4, the LR test statistics of the one sided

error for the three sub-panels are 153.22, 125.90 and 125.24 respectively

while the corresponding values of the 2critical at the 5 per cent level of

significance are respectively 29.545, 41.977 and 26.983 (from Kodde and

Palm [1986]).

VI. CONCLUSION

This study has undertaken a panel data approach to measure the technical

efficiency of the Bangladesh manufacturing sector as a whole and the

individual technical efficiencies of the majority of the three-digit level

industries. The main objective has been to check if the manufacturing sector

as well as its constituent meso level industries have benefited from

microeconomic reforms in the Bangladesh external trade sector that took

place between 1982 and 1991. The findings of the study can be summarisedas follows. First, alternative measures of the overall technical efficiency

based on the full panel estimation show a rising tendency over time, which

also have support from the overall technical efficiency estimates based on

the three sub-panels representing different phases of the Bangladesh

external trade regime. This is complemented by the technical efficiency

estimates of the individual industries under alternative schemes. Second,export orientation and capital deepening, both representing trade

liberalisation, appear to be associated with reductions in technical

inefficiencies. The same also applies to the other two industry-specific

ariables intermediate inp ts and proportion of non prod ction orkers



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production technology of the Bangladesh manufacturing sector over time.

However, the findings are suggestive of (a non-neutral) technical change tohave occurred in the manufacturing industries. Finally, the study rejects the

Cobb Douglas production technology as an adequate description of the

database used given the assumption of the Translog production technology.

The importance of the trade variables, especially export orientation, in

the reduction of technical inefficiency suggests that improvement in

technical efficiency of the Bangladesh manufacturing sector may be

attributed to the competitive push that trade liberalisation inflicted to the

domestic industries. First, industries with higher export orientation are

exposed to greater international competition than industries with lower

export orientation and/or the import substituting industries. International

competitiveness help reduce X-inefficiency of the export industries by

forcing them to utilise a higher proportion of their productive capacities

and/or adopt new technologies [ Nishimizu and Robinson, 1984]. In the

context of Bangladesh, Salim [1999] finds openness as an important

determinant of capacity realisation for some of the key manufacturing

industries. These results are very well complemented by the present study.

Second, as mentioned in Section II, the new growth theories emphasize that

trade openness provides the domestic producers access to imported capital

embodying new technologies, which in turn enhance capacity utilisationand technological progress [Grossman and Helpman, 1991]. The

significance of capital deepening as a determinant of technical efficiency

and the indication of a possible (non-neutral) technical change in the present

case implicate an improvement in capacity utilisation as well as the

occurrence of technological progress in the Bangladesh manufacturing

sector. The importance of the proportion of non-production workers in totalemployment, which emphasizes the role of human capital, also points to the

competitive push argument. As pointed out earlier, non-production workers

help reduce inefficiency by greater acquisition of new technologies and

combining the prod cti e reso rces more effecti el Se eral empirical



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NOTES

1. For an elaborated discussion, see Kalirajan and Shand [1999].2. The presence of heterogeneity may render the pooling of the aggregative data and, therefore,

the assumption of a single production frontier inappropriate for purposes of estimatingtechnical efficiencies. In the present case, as presented in Section V, the results based on ageneralised likelihood ratio test suggests that a common production technology can indeed

be applied to the pooled data used in this study.3. Denison [1961] finds better results by including man-hours worked in the production

function while Apsden [1990] argues that hours worked may be subject to sampling error as

they are affected by holidays, strikes as well as the lack of a standard unit of measurement.4. This study avoids the description of these alternative techniques since they are well

documented in existing literature ( see, for examples, Bauer [1990] and Kalirajan and Shand[1999].

5. For a detailed list of the relative weaknesses of the two models, see Coelli, Rao and Battese[1998].

6. The table also presents the estimated results based on the Cobb-Douglas production function.7. The authors gratefully acknowledge the suggestion made by an anonymous reviewer of this

journal on this procedure, and to Professor Tim Coelli for the clarification on the hypothesistesting.8. As Coelli, Rao and Battese [1998] point out, the simple average or arithmetic mean may not

be the best estimator if the firms in the sample have significant size differences and/or if thesample is not constructed by simple random sampling. This study uses the amount ofintermediate inputs used as weights.

REFERENCES

Aghion, P. and P. Howitt, 1998,Endogenous Growth Theory, Cambridge, MA: MIT Press.Aghion, P., and P.A. Howitt, 1992, A Model of Growth with Creative Destruction,

Econometrica, Vol.60, pp.32351.Aigner, D.J., Lovell, C.A.K. and P. Schmidt, 1977, Formulation and Estimation of the Stochastic

Frontier Production Function Models,Journal of Econometrics, Vol.6, pp.2137.Apsden, C., 1990, Estimates of Multifactor Productivity, Australia, Occasional Papers,

Canberra: Australian Bureau of Statistics.Bangladesh Bureau of Statistics,Report on Census of Manufacturing Industries of Bangladesh

(various issues), Statistics Division, Ministry of lanning, Government of Bangladesh.Bangladesh Bureau of Statistics, Statistical Yearbook of Bangladesh (various issues), Statistics

Division, Ministry of Planning, Government of Bangladesh.Bangladesh, Government of, Detailed Estimates of Revenue and Receipts (various issues),

Ministry of Finance



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Little, L.M., Mazumdar, D. and J.M. Page, 1987, Small Manufacturing Enterprises: AComparative Analysis of India and Other Economies, London: Oxford University Press.

Lucas, R.E., 1988, On the Mechanics of Economic Development, Journal of MonetaryEconomics, Vol.22, pp.3042.

Lundvall, K. and G.E. Battese, 2000, Firm Size, Age and Efficiency: Evidence from KenyanManufacturing Firms, The Journal of Development Studies, Vol.36, No.3, pp.14663.

Meeusen, W. and J. van den Broeck, 1977, Technical Efficiency and Dimensions of the Firm:Some Results on the Use of Frontier Production Functions,Empirical Economics, Vol.2,

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Human Capital on Total Factor Productivity,Journal of Development Economics, Vol.63,pp.399423.

Nishimizu, M. and S. Robinson, Trade Policy and Productivity Change in Semi-IndustrializedCountries,Journal of Development Economics, Vol.16, pp.177206.

Pack, H., 1988, Industrialization and Trade, in H.B. Chenery and T.N. Srinivassan (eds),Handbook of Development Economics, Amsterdam: North Holland.

Page, J.M., 1984, Firm Size and Technical Efficiency: Applications of Production Frontiers toIndian Survey Data,Journal of Development Economics, Vol.16, Nos.12, pp.283301.

Pitt, M.M. and L.F. Lee, 1981, The Measurement and Sources of Technical Inefficiency in theIndonesian Weaving Industry,Journal of Development Economics, Vol.9, pp.4364.Rahman, S.H., 1995, Trade and Industrialisation in Bangladesh: An Assessment, in G.K.

Helleiner (ed.), Manufacturing for Exports in the Developing World: Problems andPossibilities, London and New York: Routledge.

Rodrick, D., 1988, Imperfect Competition, Scale Economies and Trade Policy in DevelopingCountries, mimeo. Harvard University.

Romer, P., 1990, Endogenous Technical Change, Journal of Political Economy, Vol.98,pp.71102.

Salim, R.A., 1999, Capacity Realization and Productivity Growth in a Developing Country: HasEconomic Reform Had Impact?, Aldershot: Ashgate Publishing Ltd.

Salim, R.A. and K.P. Kalirajan, 1999, Sources of Output Growth in Bangladesh Food ProcessingIndustries: A Decomposition Analysis, The Developing Economies, Vol.XXXVII, No.3,

pp.35574.Seiford, L.M., 1996, Data Envelopment Analysis: The Evolution of the State of the Art,Journal

of Productivity Analysis, Vol.7, pp.99137.Schmidt, Planed and R.C. Sickles, 1984, Production Frontiers and Panel Data, Journal of

Business and Economic Statistics, Vol.2, pp.36774.van den Brooke, J., Koop, G., Osiewalski, J. and M. Steel, 1994, Stochastic Frontier Models: A

Baysian Perspective,Journal of Econometrics, Vol.61, pp.273303.Tybout, J., de Melo, J. and V. Corbo, 1991, The Effects of Trade Reforms on Scale and Technical

Efficiency: New Evidence from Chile, Journal of International Economics, Vol.31,pp 231 50



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APPENDIX

TA BL E A 1

DESCRIPTION OF THE THREE-DIGIT LEVEL INDUSTRIES ACCORDING TO ISTC

Industry Code Description

311 & 312 Food processing313 Beverages314 Tobacco manufacturing321 & 322 Textiles manufacturing

323 Finished garments324 Leather & leather products325 Leather footwear 326 Ginning, pressing & baling of fibres331 Wood & cork products332 Furniture & fixtures341 Paper & paper products342 Printing & publishing

351 Drugs & pharmaceuticals352 Industrial chemicals353 Other chemicals356 Rubber & rubber products357 Plastic products361 Pottery & chinaware362 Glass & glass products369 Non-metallic mineral products371 & 372 Iron & steel basic industries381 & 382 Fabricated metal products383 Non-electrical machinery384 Electrical machinery385 Transport equipment

Source:Bangladesh Statistical Yearbook, 1997.



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TABLE A2

MAXIMUM LIKELIHOOD ESTIMATES FOR PARAMETERS OF TRANSLOG (T L)

STOCHASTIC FRONTIER PRODUCTION FUNCTIONS FOR THE BANGLADESHMANUFACTURING SECTOR (BASED ON THE FULL PANEL ESTIMATION)

Variable description Parameter Coefficient S.E. Asymptoticin natural logs t-statistic

Frontier functionconstant 0 5.27 0.99 5.31*

Kt: capital K 1.39 0.29 4.73*Lt: labour L -0.85 0.32 2.68*Tt: time T 0.0087 0.0056 1.56Kt2 KK 0.031 0.026 1.19Lt2 LL 0.2047 0.0402 5.09*Tt2 TT 0.0039 0.0121 0.33(Kt) x (Lt) KL -0.130 0.046 2.81*(Kt) x (Tt) KT 0.013 0.015 0.86

(Lt) x (Tt) LT 0.0049 0.0141 0.35

Inefficiency modelconstant 0 3.08 0.99 3.09*INPt 1 -0.68 0.26 2.57*KDt 2 0.091 0.364 0.25XORt 3 -0.57 0.31 1.81**

NPLt 4 0.15 0.45 0.32INPt2 5 -0.044 0.023 1.95**KDtv 6 -0.094 0.055 1.71**XORt2 7 -0.050 0.043 1.17

NPLt2 8 -0.220 0.076 2.88*(INPt) x (KDt) 9 -0.150 0.056 2.65*(INPt) x (XORt) 10 0.035 0.033 1.07(INPt) x (NPLt) 11 0.010 0.064 0.14(KDt) x (XORt) 12 -0.101 0.064 1.57(KDt) x (NPLt) 13 -0.31 0.11 2.88*

(XORt) x (NPLt) 14 0.274 0.10 2.66*

Djs(time dummies) -1.11 not available not available

Variance parameters 2 = 2 + 2 0 25 0 03 8 74*

X30

t15

dt



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TABLE A4

MAXIMUM LIKELIHOOD ESTI MATES (MLE) FOR PARAMETERS OF TRANSLOG

(TL) STOCHASTIC FRONTIER PRODUCTION FUNCTIONS FOR THE SUB-PANELS19781982, 19831991 AND 19921994

Variabledescription Sub-Panel t Sub-Panel t Sub-Panel tin natural logs Parameter 197882 statistic 198391 statistic 199294 statistic

Frontier function

constant 0 2.01 1.04 6.85 4.74* 7.28 5.47*Kt: capital K 3.72 6.51* 1.57 3.29* 1.51 4.22*Lt: labour L -1.89 3.82* -1.39 3.06* -1.58 3.82*Tt: time T 0.01 0.35 0.02 0.48 0.02 1.61Kt2 KK 0.10 1.09 -0.04 1.34 0.07 3.25*Lt2 LL 0.39 3.54* 0.11 2.16* 0.23 6.04*Tt2 TT -0.01 0.23 0.00 0.31 -0.00 0.49(Kt) x (Lt) KL -0.66 2.68* -0.06 1.79* -0.29 5.70*(K

t) x (T

t)

KT0.04 1.04 -0.01 0.27 0.01 1.65

(Lt) x (Tt) LT 0.03 0.65 -0.01 0.32 -0.01 0.74

Inefficiency modelconstant 0 -0.36 0.16 2.37 1.99** 6.16 5.96*INPt 1 1.85 2.99* 0.14 0.40 -0.90 3.82*KDt 2 0.23 3.22* -0.14 0.25 -0.83 1.08XORt 3 -0.06 1.38 -0.09 1.62 -1.07 3.44*

NPLt 4 1.35 1.39 0.25 0.41 0.22 0.45INPt2 5 -0.22 5.08* -0.03 1.70** 0.03 1.88**KDt2 6 0.09 0.75 -0.22 2.59* 0.01 0.29XORt2 7 -0.13 1.12 -0.18 1.90** -0.06 1.80*

NPLt2 8 -0.33 3.53* -0.26 1.94** -0.19 3.63*(INPt) x (KDt) 9 -0.24 3.74* 0.02 0.28 -0.07 1.27(INPt) x (XORt) 10 0.02 0.29 -0.16 1.84* -0.01 4.13*(INPt) x (NPLt) 11 -0.28 2.44* -0.04 0.41 0.01 0.07(KDt) x (XORt) 12 0.27 1.81* 0.07 0.78 0.09 1.47

(KDt) x (NPLt) 13 -0.59 2.83* -0.47 2.08* -0.26 3.04*(XORt) x (NPLt) 14 0.39 1.59 0.20 1.56 0.31 2.98

Djs(time dummies) 0.21 not -0.48 not -1.07 not

available available available

XT1

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TABLE A5

TECHNICAL EFFICIENCY ESTIMATES OF VARIOUS THREE-DIGIT MANUFACTURING INDUSTRIES OF BANGLADESH, 19781994

(BASED ON THE SUB-PANELS EST IMATION)

IndustryCode/year 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

311+312 .95 .95 .92 .94 .95 .95 .97 .96 .94 .95 .95 .98 .98 .98 .97 .98 .96313 .83 .78 .84 .61 .41 .57 .41 .21 .20 .24 .17 .19 .15 .11 .13 .16 .16 314 .76 .84 .78 .77 .82 .88 .88 .86 .86 .92 .75 .91 .95 .93 .93 .94 .91321+322 .95 .96 .94 .95 .96 .96 .96 .97 .96 .96 .96 .97 .97 .97 .97 .96 .96 323 .10 .14 .12 .07 .15 .36 .60 .42 .42 .51 .88 .97 .97 .97 .96 .94 .91

324 .69 .74 .62 .49 .43 .50 .60 .49 .48 .54 .50 .78 .70 .57 .61 .56 .54325 .20 .25 .28 .26 .37 .54 .28 .28 .30 .29 .22 .43 .37 .30 .33 .39 .34326 .29 .22 .27 .26 .22 .38 .43 .54 .49 .48 .40 .28 .49 .33 .32 .37 .36 331 .10 .13 .14 .10 .10 .20 .40 .18 .25 .25 .23 .66 .61 .46 .49 .46 .52332 .11 .09 .07 .06 .17 .55 .42 .63 .49 .40 .45 .12 .19 .29 .31 .33 .39341 .46 .53 .65 .55 .37 .42 .41 .42 .47 .38 .46 .66 .72 .70 .73 .75 .69342 .07 .10 .10 .12 .14 .38 .30 .34 .42 .43 .40 .89 .63 .47 .49 .54 .47 351 .57 .66 .75 .75 .81 .83 .94 .93 .90 .75 .77 .92 .89 .82 .79 .76 .72352 .52 .58 .51 .50 .40 .53 .61 .56 .77 .82 .56 .84 .90 .89 .88 .84 .78

353 .29 .37 .38 .41 .35 .91 .95 .95 .91 .89 .75 .74 .75 .55 .65 .74 .76356 .09 .09 .10 .09 .11 .14 .14 .14 .14 .14 .14 .23 .29 .20 .27 .29 .34357 .06 .05 .04 .06 .05 .10 .12 .16 .22 .24 .22 .24 .32 .21 .31 .28 .25361 .06 .09 .09 .10 .08 .13 .19 .12 .13 .18 .20 .27 .29 .27 .28 .31 .29362 .09 .10 .13 .11 .12 .27 .29 .26 .19 .13 .11 .13 .14 .12 .13 .14 .12369 .29 .32 .32 .31 .28 .34 .36 .37 .35 .20 .19 .13 .11 .11 .14 .12 .16 371+372 .87 .95 .93 .72 .74 .65 .88 .61 .82 .81 .75 .94 .92 .86 .88 .89 .81381+382 .09 .10 .10 .11 .25 .25 .27 .28 .26 .27 .26 .45 .73 .57 .66 .62 .54383 .15 .19 .27 .30 .36 .21 .33 .31 .27 .24 .25 .19 .25 .15 .19 .22 .22384 .41 .49 .57 .61 .60 .61 .75 .73 .85 .88 .85 .79 .84 .71 .73 .78 .70385 .28 .38 .36 .39 .52 .62 .96 .94 .93 .94 .80 .47 .88 .55 .64 .77 .72TE1 .368 .404 .411 .386 .390 .491 .538 .506 .521 .514 .489 .576 .610 .532 .552 .566 .545 S.D. .320 .320 .320 .306 .207 .265 .291 .292 .297 .302 .287 .3123 .300 .296 .293 .289 .272TE2 .519 .546 .550 .631 .610 .549 .591 .540 .581 .609 .512 .703 .755 .697 .699 .651 .657Median .280 . 250 .280 .300 .360 .500 .420 .420 .470 .430 .450 .660 .700 .550 .610 .560 .540

Note: TE1 and TE2 represent respectively the simple and the weighted average of the efficiency estimates for the manufacturing sector

as a whole and S.D stands for the standard deviation from the mean value (TE1).

T Liberalization and Tech Efficiency BD Hossain

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