T Liberalization And Tech Efficiency Bd Hossain

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Trade Liberalisation and Technical Efficiency: Evidence from Bangladesh Manufacturing Industries MA Hossain; ND Karunaratne Online Publication Date: 01 February 2004 To cite this Article: Hossain, MA and Karunaratne, ND (2004) 'Trade Liberalisation and Technical Efficiency: Evidence from Bangladesh Manufacturing Industries', Journal of Development Studies, 40:3, 87 — 114 To link to this article: DOI: 10.1080/0022038042000213210 URL: 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. KARUNARAT NE

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 1978–94 for 25 three-digit level industries. 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 nonproduction 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 the 5 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 database given the specification of the translog production technology. I. INTRODUCTION

The notion of efficiency lies at the core of economics, and an improvement in technical efficiency is considered as an important source of growth of output [Leibenstein, 1966]. The degree of efficiency determines whether a firm might survive or stagnate and fail over time [Jovanovic, 1982]. The degree of efficiency in turn depends on a host of factors such as the amount The Journal of Development Studies, Vol.40, No.3, February 2004, pp.87–114 ISSN 0022-0388 print DOI: 10.1080/0022038042000213210 © 2004 Taylor & Francis Ltd.

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and quality of physical and human capital, technological know-how, experience, managerial skills, market structure, and the degree of competition, 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 manufacturing industries of Bangladesh in a panel-data stochastic frontier modelling framework covering the period 1978–94. 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 case of 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 crosssection. 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 study compares the changes in the mean technical efficiency for the manufacturing sector as a whole as well as the individual industries on a year-to-year basis. The estimates of the yearly technical efficiency of the individual industries provide a basis for comparing the technical efficiency gains (losses) of the more exportoriented industries with those of the less export-oriented ones and, for that matter, the relative efficiency gains (losses) of import substituting industries to export promoting industries. Finally, we construct three sub-panels distinguishing the pre-liberalisation (1978–82), the transition (1983–91) and the post-liberalisation (1992–4) periods of the external trade regime of Bangladesh, and thus compare the overall mean technical efficiency between these periods.

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Empirical studies on the effects of trade liberalisation on technical efficiency provide mixed results. Generally speaking, the literature on trade liberalisation 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 next section, 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 and heterogeneity. While the problems of heterogeneity and aggregations are quite serious at the two-digit level, they are much less stringent at the threedigit 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 variables used in the study. Section IV sets out the suggested econometric model. Section V analyses the empirical results. Finally, Section VI provides the summary and concluding remarks. I I . THEORY AND EVIDENCE

The implications of a liberal trade regime for growth, total factor productivity, and economic efficiency are derived from the neo-classical economic theory, particularly the neo-classical trade theory. The arguments also draw support from the political economy perspectives on protection as well as the recent theories of growth known as the endogenous growth theories. The neo-classical case for trade

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liberalisation is based on the perceived benefits from the division of labour, widening of the markets, and comparative advantage. The neoclassical 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-industry effects 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 towards the best-practice technology [Krugman, 1987; Lucas, 1988; Young, 1991]. Early empirical evidence shows almost no significant positive relationship between trade liberalisation and technical efficiency [Bhagwati, 1988; Pack, 1988; Rodrick, 1988]. For Chile, Tybout, de Melo and Corbo [1991] find only a small improvement in the overall productivity growth of the manufacturing sector across the restricted and the liberalised trade regimes. In a recent cross-country study, Miller and Upadhyay [2000], however, show that both openness and outward-orientation have positive and significant impact on total factor productivity. Similarly, in a panel data modelling, Karunaratne [2001] finds the Australian manufacturing sector to have experienced a reduction in technical inefficiency over time.

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The Bangladesh Context Bangladesh adopted a gradualist approach in its transition towards outward orientation. 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 1991 respectively. 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 four-digit manufacturing industries using panel data for the period 1974/75–1983/84. The findings, based on the ordinary least squares method with the production technology defined as either Cobb-Douglas or Translog, suggest no substantial improvement in either total factor productivity or technical efficiency over time. However, as mentioned earlier, the results may not be interpreted as sequels to trade liberalisation for at least two reasons. First, the study covers the period until 1983/84, while microeconomic reforms in Bangladesh got underway only in 1982. Second, ordinary least squares are not an appropriate option to dealing with the case at hand. Salim [1999], on the other hand, estimates productive capacity realisation and total factor productivity for three key manufacturing industries – food processing, textiles, and chemicals – for

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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 appropriate description 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 se does 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. I I I . DATA AND THE DEFINITIONS OF THE VARI A BLES

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 1974–96 except 1995 as no survey was undertaken for the financial year 1994–5. 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 1978–94. 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 allows comparisons between firms, which may be heterogeneous in raw material use [Griliches and Ringstad, 1971]. The use of gross output necessitates the inclusion of raw material as an input variable in the model, which might obscure the role of physical and human capital in productivity growth. Secondly, unlike gross output, the value added accounts for the differences and changes in the quality of inputs [Salim and Kalirajan, 1999]. While net value added might be more appropriate than gross value added, the CMI estimates of the former are likely to be flawed because of the arbitrary nature of deductions from gross output [Salim, 1999]. Capital is defined as the gross fixed assets representing the aggregate book values of land, buildings, machinery, tools, transport, and office equipment. In empirical analysis, capital is often represented by the

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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 of fixed 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]. Labour is defined as the number of employees. Empirical studies have alternatively used the number of employees and the number 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 deepening is defined as the ratio of capital to labour and is used as 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 the proportion of non-production workers in total employment, and intermediate inputs, the latter being defined as gross output less gross value added. The output variable is deflated by the wholesale price index of industrial products, capital is deflated by the wholesale price index of manufacturing that excludes fuel and lighting, and intermediate inputs is deflated by the wholesale price index of raw materials. All the variables are expressed in 1990 prices. Before we conclude this section, a word on the limitations of the CMI data. The reliability of the CMI data in general is questionable as they suffer from various problems such as the under coverage, under-reporting or misreporting, and measurement errors. While the problems of random errors in data observation, especially of the dependent variable can be taken care of by applying appropriate econometric technique such as the stochastic frontier production function approach [Caves and Barton 1990], the

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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 not report 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 are appropriately represented through simple random sampling, the present study chooses to use the average figures, that is, the value of the variable per reporting firm. I V. EMP IRICAL TECHNIQUE

Following the pioneering work of Farrell [1957], the literature on technical efficiency measures provides a wide variety of models, parametric or nonparametric, 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 to both 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 such as weather, strikes etc., may affect the placement of the DEA frontier more than the SFA. By including a random error term in the model in addition to the inefficiency effect, the SFA ‘accounts for the presence of measurement error in output or the combined effects of unspecified explanatory variables in the production function’ [Coelli, Rao and Battese, 1998: 198]. Secondly, SFA can be used to conduct a list of hypotheses concerning the existence of inefficiency, the structure of the production technology as well as the distributional form of the inefficiency effects. The country concerned here represents a case where the data are likely to be highly influenced by measurement errors. This is even more likely with survey-based data, as in the present case, that are compiled on the basis of the information supplied by the respondent firms or industries.

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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 an important 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] and Karunaratne [2001]. As mentioned before, this study applies the combined inefficiencystochastic frontier model as suggested in Battese 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 procedures such as Pitt and Lee [1981] and Kalirajan [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: Yit ¼ b0 þ bK Kit þ bL Lit þ bT Tit þ bKK ðKit Þ2 þ bLL ðLit Þ2 þ bTT ðTit Þ2 þbKL ðKit :Lit Þ þ bKT ðKit :Tit Þ þ bLT ðLit :Tit Þ þ vit
uit ;

(1)

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 observation; Lit = the natural logarithm of labour for the i-th industry in the t-th year of observation; T = time; vit = random variables assumed to be iid with mean zero and variance s2v . uit = non-negative random variables assumed to be independently distributed with mean, µit, and variance, σ2, where mit ¼ d0 þ d1 INPit þ d2 KDit þ d3 XORit þ d4 NPLit þ d5 ðINPit Þ2 þ d6 ðKDit Þ2 2

2

(2)

þd7 ðXORit Þ þ d8 ðNPLit Þ þ d9 ðINPit :KDit Þ þ d10 ðINPit :XORit Þ þ d11 ðINPit :NPLit Þ 30 X dj Djit ; þd12 ðKDit :XORit Þ þ d13 ðKDit :NPLit Þ þ d14 ðXORit :NPLit Þ þ j¼15

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where: INPit = the natural logarithm of intermediate inputs for the i-th industry in the 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 industry’s export over output in the t-th year of observation; NPLit = the natural logarithm of the ratio of non-productive labour to total employment 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, vit and 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 industryspecific 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 proxy for 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 intensity. Intermediate inputs, capital deepening, and export orientation are all expected to have negative coefficients in order for them to be interpreted as inefficiency-reducing instruments. The sign of the coefficient of the proportion of non-production labours in total employment is, however, indeterminate. Some argue that the non-productive workers such as the managers, the labour relations and R&D personnel, and the engineers may contribute to the effective acquisition and combination of the productive resources [Campbell, 1984], thereby help reduce inefficiency. Others argue that a rise in the ratio of nonproduction workers inflicts rigidities in the production process that may slow down the speed of adjustment to changes in demand and hence contribute to inefficiency.

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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 these variables 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 A26 of the Appendix. 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 of INPt and INPt2 and (INPt. KDt). Capital deepening has negative coefficients involving the variable KDt2 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, the results can be interpreted as due to trade liberalisation. Export orientation, the key variable representing trade liberalisation, has negative and statistically significant coefficients for the variables XORt and (INPt. XORt) and negative but not statistically significant coefficients for XORt2 and (KDt. XORt). This tends to imply that the more export-oriented industries have lower inefficiency effects and, for that matter, greater efficiencies. However, when combined with the proportion of non-production labour to total employment, exportation orientation appears to have adversely affected the efficiencies of the more export-oriented industries and/or the industries with higher proportion of non-productive workers. The coefficients of the variables NPLt2 and (KDt. NPLt) are indicative of a greater positive contribution to the reduction of inefficiencies of the industries with the higher proportion of non-production labour to total employment. As to the time effects, most of the time dummies have negative signs, with a sum total of (-)0.77, indicating a reduction in

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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 or less. 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 the inefficiency 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 TAB L E 1 G E N E R A L I S E D L I KE L I HOOD RAT I O T E S T S OF N U LL H Y PO TH ESES FO R PARAMETERS IN THE STOCHASTIC FRONTIER PRODUCTION FUNCTION FOR THE BANGLADESH MANUFACTURING

Null hypothesis, H0

Likelihood-ratio test statistic

βKK=βLL=βTT= βKL =βKT=βLT = 0 45.74* (Cobb-Douglas function) γ = δ0 = δ1 = …=δ30 = 0 244.49* (no inefficiency effects) βKT = βLT = 0 2.98 (there is no technical change βT = βTT = βKT = βLT = 0 8.54 (there is neutral technical change) δ1 =δ5= δ9 =δ10 = δ11 = 0 133.74* (no size effects) δ2 =δ6= δ9 =δ12 = δ13 = 0 22.20* (no capital deepening effects) δ3 =δ7= δ10 =δ12 = δ14 = 0 46.28* (no export-orientation effects) δ4 =δ8= δ11 =δ13 = δ14 = 0 49.82* (no NPL effects) δ2=δ3=δ6 =δ7 =δ9 =δ10 =δ12 =δ13=δ14 =0 43.88* (no capital deepening and export orientation effects) δ15 = δ16 = …=δ30 = 0 33.10* (no time effects)

Critical 2value ( χ α ,df )

Decision

χ2.05, 6 = 12.59

reject H0

χ2.05, 31 = 44.41

reject H0

χ2.05, 2 = 5.99

cannot reject H0

χ2.05, 4 = 9.49

cannot reject H0

χ2.05, 5 = 11.07

reject H0

χ2.05, 5 = 11.07

reject H0

χ2.05, 5 = 11.07

reject H0

χ2.05, 5 = 11.07

reject H0

χ2.05, 9 = 16.92

reject H0

χ2.05, 16 = 26.30

reject H0

Notes: Critical values for the tests of hypotheses, excepting γ = 0, are obtained from the appropriate chi-square distribution. The critical value for testing the null hypothesis of γ = 0 is taken from Kodde and Palm, 1986. * indicates that the value of the generalised likelihood-ratio statistic exceeds the critical value at 5 per cent level of significance.

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vary across industries. Similarly, the null hypothesis of no technical efficiency effects is rejected, although the value of γ 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 cent level 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 heterogeneity among 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 production technology is appropriate for all the 25 industries considered in the present case, we carry out a likelihood ratio test by specifying and estimating a Cobb-Douglas frontier production function along with an inefficiency model for each of the industries separately as follows:7 Ytj ¼ b0 þ bK Ktj þ bL Ltj þ bT Tj þ vtj
utj mtj ¼ d0 þ d1 INPtj þ d2 KDtj þ d3 XORtj þ d4 NPLtj þ d5 ðINPit Þ

(3) 2

(4)

where the subscript j denotes the j-th industry. The variables and the random terms have the same explanations as in equations (1) and (2). The choice of

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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/or the 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: 25 X LR ¼
2ðLLFH0
LLFH1 Þ ¼
2ðLLFP
LLFj Þ; (5) j¼1

which has a χ2 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 χ 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 show steady 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 respectively. However, the median efficiency estimates show a mixed pattern until 1987 and in general indicate lower technical efficiency than the other two measures. The median efficiency registers a sharp rise in 1988 (0.58 against about 0.41 for the year 1987) and thereafter shows an increasing tendency. Nonetheless, the general impression remains that the overall technical efficiency seems to have increased overtime. The scenario is visualised in Figure 1 below. Turning to the individual industries, 13 out of the 25, that is, 52 per cent of the industries have experienced significant improvements in technical efficiencies. These industries are: tobacco manufacturing, ready-made garments, leather and leather products, wood and cork products, furniture, paper and paper products, printing and publishing, industrial chemicals,

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F I GURE 1 TECHNICAL E FFICIENCY OF BANGLADESH MANUFACTURING, 1978–94 ( BAS E D ON T HE F UL L PA NE L E S T IMATIO N )

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

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 gained marginally. 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, beverages have experienced deterioration in technical efficiency while ginning and pressing, and non-electrical machinery maintain more or less stable levels of efficiencies. Over the years, textiles manufacturing, food processing including tea, leather and leather products, non-metallic mineral products, and chemical and chemical products and, since the mid-1980s, readymade garments remained the most export-oriented industries of Bangladesh. As can be seen from Table A3, these industries either operate at high levels of technical efficiencies or have experienced substantial increases thereof. However, some of the import-substituting industries, e.g., tobacco manufacturing, paper and paper products, wood and cork products, pottery and chinaware, electrical machinery, transport equipment, and iron and steel all gained over

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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 rather they 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 enhanced the 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 1978–82, 0.572 for the period 1983–91 and about 0.700 for the period 1992–4. 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 and 0.686. 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 subpanels for the three phases of the Bangladesh international trade regime, namely 1978–82 (pre-liberalisation period), 1983–91 (transition period) and TAB L E 2 AVERAGE TECHNICAL E FFICIENCY OF THE BANGLADESH MANUFACTURING S E C TO R ACROS S P HAS E S OF T HE T RADE PO LIC Y REG IME

Technical efficiencies based on Full panel estimation Sub-panels estimation Period 1978–82 1983–91 1992–94 1983–94 Overall

Simple average

Weighted average

Median

Simple average

Weighted average

Median

0.414 0.572 0.700 0.607 0.548

0.478 0.635 0.744 0.663 0.608

0.420 0.550 0.686 0.606 0.536

0.392 0.531 0.554 0.537 0.494

0.571 0.615 0.669 0.629 0.612

0.280 0.480 0.540 0.490 0.430

Source: author’s calculations from the estimated results.

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1992–4 (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 and individual 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. The simple 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 sample period resembling the pattern of the full panel estimation (as in Figure 1). F I GURE 2 TECHNICAL E FFICIENCY OF BANGLADESH MANUFACTURING, 1978–94 (BASED ON THE SUB - PA NE L S E S T IMATIO N )

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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 sharp rise 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 contrasting results 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 Testing We 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 for each sub-panel; and (c) no inefficiency effects in each of the sub-panels. Based on the likelihood function statistics presented in Tables A2 and A4, a likelihood ratio test similar to equation (5) above produces a test statistic of (-2{-295.52-[-64.09-144.68-55.39]}) = 62.72. The value of the χ2critical is higher than the calculated value for degrees of freedom 50 and above at a level of significance of 5 per cent or less, which suggests that the null hypothesis (a) cannot be rejected. Since the number of parameters estimated in the full panel and the sub-panels are not identical (because of the inclusion of the time dummies), for a better comparison we re-estimate the full panel and the sub-panels with identical number of parameters by dropping the time dummies from the inefficiency models. This exercise produces a test statistic of (-2{-313.52-[-71.89-159.24-61.47]}) = 41.84, which is less the χ2critical = 67.50 for 50 (=2 x 25) degrees of freedom at the

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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 subpanels, which are to be compared with the χ2critical = 12.59. Similarly, the null hypothesis of no inefficiency effects (γ = 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 χ2critical 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 summarised as 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 variables – intermediate inputs, and proportion of non-production workers to total employment. Third, with reference to the full panel estimation, most of the industries (22 out of a total of 25) have experienced rises in technical efficiencies over time either significantly or marginally. Fourth, majority of the export-oriented industries have either maintained a high degree of technical efficiency or gained over time. Fifth, some of the importsubstituting industries also significantly benefited from the trade policy shift, although import-substituting industries in general have lower technical efficiencies. Thus, if inter-temporal increases in technical efficiencies are described as due to trade liberalisation, arguably, the benefits of trade liberalisation have spread across the board. Sixth, the study finds no (Hick’s) neutral technical change to have occurred in the

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production technology of the Bangladesh manufacturing sector over time. However, the findings are suggestive of (a non-neutral) technical change to have 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 utilisation and 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 total employment, 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 productive resources more effectively. Several empirical studies indicate that various measures of the real effective exchange rate of the Bangladesh currency have undergone depreciation over time. As a result, the anti-export bias has also shown a downward tendency through time [Rahman, 1995; Hossain and Karunaratne, 2002]. This forms another dimension of international competitiveness facing the Bangladesh export industries. As such, it is reasonable to conclude that competitive push has played an important role in enhancing the technical efficiency of the Bangladesh manufacturing sector.

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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 estimating technical efficiencies. In the present case, as presented in Section V, the results based on a generalised 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 hypothesis testing. 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 the sample is not constructed by simple random sampling. This study uses the amount of intermediate 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.323–51. 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.21–37. 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. Battese, G.E. and T.J. Coelli, 1993, ‘A Stochastic Frontier Production Function Incorporating a Model for Technical Inefficiency Effects’, Working Papers in Econometrics and Applied Statistics, No. 69, Department of Econometrics, Armidale: University of New England. Battese, G.E. and T.J. Coelli, 1995, ‘A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data’, Empirical Economics, Vol.20, pp.325–32. Bauer, P.W., 1990, ‘Recent Developments in the Economic Estimation of Frontier’, Journal of Econometrics, Vol.46, pp.39–56. Bhagwati, J.N., 1988, ‘Export-Promoting Trade Strategy: Issues and Evidence’, World Bank Research Observer, Vol.1, pp.27–58. Brada, J.C., King, A.E. and C.Y. Ma, 1997, ‘Industrial Economics of the Transition: Determinants of Enterprise Efficiency in Czechoslovakia and Hungary’, Oxford Economic Papers, Vol.49, No. 1, pp.4–27. Cambell, C., 1984, ‘Correlates of Residual Growth in New Zealand Manufacturing Industries, 1952–73’, Paper presented to Economics Section ANZAAS, Canberra.

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Caves, R.E. and D.R. Barton, 1990, Efficiency in U.S. Manufacturing Industries, Cambridge, MA: MIT Press. Charnes, A., Cooper, W.W., Lewin, A.Y. and L.M. Seiford, 1995, Data Envelopment Analysis: Theory, Methodology and Applications, Boston: Kluwer Academic Publishers. Coelli, T.J., 1995, ‘Estimators and Hypothesis Tests for a Schochastic Frontier Function: A Monte Carlo Analysis’, Journal of Productivity Analysis, Vol.6, pp.247–68. Coelli, T.J., 1996, ‘A Guide to Frontier Version 4.1: A Computer Program for Frontier Production Function Estimation’, CEPA Working Paper 96/07, Department of Econometrics, University of New England, Armidale. Coelli, T., Rao, D.S.P. and G.E. Battese, 1998, An Introduction to Efficiency and Productivity Analysis, Boston/Dordrecht/London: Kluwer Academic Publishers. Corden, W.M., 1974, Trade Policy and Economic Welfare, Clarendon Press, Oxford. Denison, E., 1961, ‘Measurement of Labour Input: Some Questions of Definition and Adequacy of Data’, Output, Input and Productivity Measurement, Studies in Income and Wealth, NBER, Vol.25. Farrel, M.J., 1957, The Measurement of Productive Efficiency’, Journal of the Royal Statistical Society, Series A, Vol.120, pp.253–81. Griliches, Z. and V. Ringstad, 1971, Economies of Scale and the Form of the Production Function, Amsterdam: North-Holland. Grossman, G. and E. Helpman, 1991, Innovation and Growth in the Global Economy, Cambridge, MA: MIT Press. Haddad, M. and A. Harrison, 1993, ‘Are There Positive Spillovers from Direct Foreign Investment? Evidence from Panel Data from Morocco’, Journal of Development Economics, Vol.42, No.1, pp.51–74. Hill, H. and K.P. Kalirajan, 1993, ‘Small Enterprise and Firm-Level Technical Efficiency in the Indonesian Garment Industry’, Applied Economics, Vol.25, No.9, pp.1127–50. Hossain, M.A. and N.D. Karunaratne, 2002, ‘Export Response to the Reduction of Anti-Export Bias: Empirics from Bangladesh’, Discussion Papers in Economics, No. 303, School of Economics, University of Queensland, Brisbane. Jondrow, J., Lovell, C.A.K., Materov, I.S. and P. Schmidt, 1982, ‘On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model’, Journal of Econometrics, Vol.19, pp.233–9. Jovanovic, B., 1982, ‘Selection and Evolution of Industries’, Econometrica, Vol.50, No. 3, pp.649–70. Kalirajan, K.P., 1981, ‘An Econometric Analysis of Yield Variability in Paddy Production’, Canadian Journal of Agricultural Economics, Vol.29, pp.283–94. Kalirajan, K.P. and J.C. Flinn, 1983, ‘The Measurement of Farm-Specific Technical Efficiency’, Pakistan Journal of Applied Economics, Vol.2, pp.167–80. Kalirajan, K.P. and R.T. Shand, 1999, ‘Frontier Production Functions and Technical Efficiency Measures’, Journal of Economic Surveys, Vol.13, No.2, pp.149–71. Karunaratne, N.D., 2001, ‘Microeconomic Reforms and Technical Efficiency in Australian Manufacturing’, (Mimeo), Brisbane: School of Economics, University of Queensland. Keller, W., 2000, ‘Do Trade Pattern and Technology Flows Affect Productivity Growth?’, The World Bank Economic Review, Vol.14, No.1, pp.17–47. Kodde, D.A. and F.C. Palm,1986, ‘Wald Criteria for Jointly Testing Equality and Inequality Restrictions’, Econometrica, Vol.54, pp.1243–8. Krishna, K.L. and G.S. Sahota, 1991, ‘Technical Efficiency in Bangladesh Manufacturing Industries’, Bangladesh Development Studies, Vol.19, pp.89–106. Krueger, A.O., 1974, ‘The Political Economy of the Rent-Seeking Society’, American Economic Review (June), pp.291–303. Krugman, P.R., 1986, ‘New Thinking about Trade Policy’, in P.R. Krugman (ed.), Strategic Trade Policy and the New International Economics, Cambridge, MA: MIT Press, pp.1–22. Krugman, P.R., 1987, ‘Is Trade Policy Passé?’, Economic Perspectives, Vol.1, pp.131–44. Leibenstein, H.,1966, ‘Allocative Efficiency vs. X-Efficiency’, American Economic Review, Vol.56, pp.392– 415.

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Little, L.M., Mazumdar, D. and J.M. Page, 1987, Small Manufacturing Enterprises: A Comparative Analysis of India and Other Economies, London: Oxford University Press. Lucas, R.E., 1988, ‘On the Mechanics of Economic Development’, Journal of Monetary Economics, Vol.22, pp.30–42. Lundvall, K. and G.E. Battese, 2000, ‘Firm Size, Age and Efficiency: Evidence from Kenyan Manufacturing Firms’, The Journal of Development Studies, Vol.36, No.3, pp.146–63. 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, pp.109–22. Miller, S.M. and Mukti P. Upadhyay, 2000, ‘The Effects of Openness, Trade Orientation, and Human Capital on Total Factor Productivity’, Journal of Development Economics, Vol.63, pp.399–423. Nishimizu, M. and S. Robinson, ‘Trade Policy and Productivity Change in Semi-Industrialized Countries’, Journal of Development Economics, Vol.16, pp.177–206. 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 to Indian Survey Data’, Journal of Development Economics, Vol.16, Nos.1–2, pp.283–301. Pitt, M.M. and L.F. Lee, 1981, ‘The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry’, Journal of Development Economics, Vol.9, pp.43–64. Rahman, S.H., 1995, ‘Trade and Industrialisation in Bangladesh: An Assessment’, in G.K. Helleiner (ed.), Manufacturing for Exports in the Developing World: Problems and Possibilities, London and New York: Routledge. Rodrick, D., 1988, ‘Imperfect Competition, Scale Economies and Trade Policy in Developing Countries’, mimeo. Harvard University. Romer, P., 1990, ‘Endogenous Technical Change’, Journal of Political Economy, Vol.98, pp.71–102. Salim, R.A., 1999, Capacity Realization and Productivity Growth in a Developing Country: Has Economic Reform Had Impact?, Aldershot: Ashgate Publishing Ltd. Salim, R.A. and K.P. Kalirajan, 1999, ‘Sources of Output Growth in Bangladesh Food Processing Industries: A Decomposition Analysis’, The Developing Economies, Vol.XXXVII, No.3, pp.355–74. Seiford, L.M., 1996, ‘Data Envelopment Analysis: The Evolution of the State of the Art’, Journal of Productivity Analysis, Vol.7, pp.99–137. Schmidt, Planed and R.C. Sickles, 1984, ‘Production Frontiers and Panel Data’, Journal of Business and Economic Statistics, Vol.2, pp.367–74. van den Brooke, J., Koop, G., Osiewalski, J. and M. Steel, 1994, ‘Stochastic Frontier Models: A Baysian Perspective’, Journal of Econometrics, Vol.61, pp.273–303. 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. Young, A., 1991, ‘Learning by Doing and the Dynamics of International Trade’, Quarterly Journal of Economics, Vol.106, pp.369–405.

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APPENDIX TABL E A1 D E S C R I P T I O N O F T HE T HRE E - DI GI T L E VE L I NDUS TRIES A C C O R D IN G TO ISTC

Industry Code

Description

311 & 312 313 314 321 & 322 323 324 325 326 331 332 341 342 351 352 353 356 357 361 362 369 371 & 372 381 & 382 383 384 385

Food processing Beverages Tobacco manufacturing Textiles manufacturing Finished garments Leather & leather products Leather footwear Ginning, pressing & baling of fibres Wood & cork products Furniture & fixtures Paper & paper products Printing & publishing Drugs & pharmaceuticals Industrial chemicals Other chemicals Rubber & rubber products Plastic products Pottery & chinaware Glass & glass products Non-metallic mineral products Iron & steel basic industries Fabricated metal products Non-electrical machinery Electrical machinery Transport equipment

Source: Bangladesh Statistical Yearbook, 1997.

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TABL E A2 M A X I M U M L I K E L I HOOD E S T I MAT E S F OR PARAMETER S O F TR A N SLO G (TL) STOCHASTIC FRONTIER PRODUCTION FUNCTIONS FOR THE BANGLADESH MANUFACTURING SECTOR (BASED ON THE F ULL PA NEL ESTIMATIO N )

Variable description in natural logs Frontier function constant Kt: capital Lt: labour Tt: time Kt2 Lt2 Tt2 (Kt) x (Lt) (Kt) x (Tt) (Lt) x (Tt) Inefficiency model constant INPt KDt XORt NPLt INPt2 KDtv XORt2 NPLt2 (INPt) x (KDt) (INPt) x (XORt) (INPt) x (NPLt) (KDt) x (XORt) (KDt) x (NPLt) (XORt) x (NPLt) Dj’s(time dummies)

Parameter

Coefficient

β0 βK βL βT βKK βLL βTT βKL βKT βLT

5.27 1.39 -0.85 0.0087 0.031 0.2047 0.0039 -0.130 0.013 0.0049

0.99 0.29 0.32 0.0056 0.026 0.0402 0.0121 0.046 0.015 0.0141

5.31* 4.73* 2.68* 1.56 1.19 5.09* 0.33 2.81* 0.86 0.35

δ0 δ1 δ2 δ3 δ4 δ5 δ6 δ7 δ8 δ9 δ10 δ11 δ12 δ13 δ14 30 X

3.08 -0.68 0.091 -0.57 0.15 -0.044 -0.094 -0.050 -0.220 -0.150 0.035 0.010 -0.101 -0.31 0.274

0.99 0.26 0.364 0.31 0.45 0.023 0.055 0.043 0.076 0.056 0.033 0.064 0.064 0.11 0.10

3.09* 2.57* 0.25 1.81** 0.32 1.95** 1.71** 1.17 2.88* 2.65* 1.07 0.14 1.57 2.88* 2.66*

dt §

-1.11

S.E.

not available

Asymptotic t-statistic

not available

t¼15

Variance parameters δs2 = δ2 + δv2 γ = δv / (δ2 + δv2) Log-likelihood LR-test of the one-sided error*** Mean TE

0.25 0.20 -295.52 244.49* 0.548

0.03 0.10

8.74* 1.96**

Notes: S.E. = standard error. Standard errors are rounded to two-significant digits after the decimal point; *significant at 5 per cent level or less; **significant at 10 per cent level or less; *** tests the null hypothesis of no inefficiency effects ( =0); § the time dummies for the years 1983 and 1989 to 1994 are found significant at 10 per cent level or less.

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1978 1979 1980 1981 1982 1983 1984 1985

1986

1987

1988

1989

1990

1991 1992 1993 1994

311+312 313 314 321+322 323 324 325 326 331 332 341 342 351 352 353 356 357 361 362 369 371+372 381+382 383 384 385 TE1 S.D. TE2 Median

0.87 0.58 0.65 0.92 0.10 0.49 0.21 0.41 0.13 0.08 0.28 0.18 0.75 0.29 0.56 0.16 0.06 0.08 0.12 0.23 0.41 0.23 0.12 0.32 0.31 0.342 0.25 0.379 0.282

0.92 0.26 0.85 0.94 0.29 0.67 0.41 0.56 0.28 0.30 0.67 0.34 0.92 0.83 0.77 0.16 0.26 0.17 0.20 0.41 0.80 0.34 0.31 0.72 0.63 0.522 0.27 0.578 0.413

0.94 0.31 0.93 0.94 0.36 0.75 0.40 0.58 0.31 0.29 0.56 0.36 0.83 0.85 0.76 0.18 0.29 0.21 0.18 0.32 0.83 0.37 0.32 0.78 0.75 0.536 0.27 0.581 0.405

0.95 0.26 0.87 0.95 0.67 0.78 0.37 0.58 0.33 0.32 0.76 0.38 0.86 0.77 0.78 0.20 0.32 0.24 0.18 0.34 0.83 0.40 0.32 0.81 0.66 0.557 0.26 0.588 0.580

0.97 0.37 0.90 0.96 0.94 0.92 0.64 0.62 0.32 0.46 0.90 0.61 0.94 0.90 0.86 0.34 0.40 0.36 0.18 0.51 0.95 0.71 0.30 0.87 0.69 0.665 0.28 0.760 0.711

0.98 0.26 0.92 0.96 0.94 0.91 0.90 0.65 0.55 0.23 0.90 0.65 0.92 0.92 0.85 0.46 0.62 0.35 0.23 0.51 0.94 0.89 0.31 0.89 0.89 0.689 0.27 0.770 0.846

0.97 0.22 0.93 0.96 0.95 0.89 0.49 0.57 0.46 0.27 0.91 0.53 0.93 0.92 0.82 0.30 0.41 0.35 0.23 0.43 0.93 0.93 0.28 0.85 0.82 0.654 0.28 0.757 0.824

0.89 0.72 0.79 0.92 0.17 0.68 0.38 0.38 0.11 0.13 0.35 0.20 0.77 0.42 0.64 0.17 0.08 0.12 0.13 0.26 0.68 0.26 0.17 0.40 0.30 0.405 0.27 0.490 0.351

0.89 0.82 0.85 0.93 0.11 0.54 0.20 0.43 0.14 0.12 0.37 0.22 0.87 0.42 0.66 0.20 0.08 0.12 0.16 0.32 0.70 0.31 0.19 0.35 0.51 0.420 0.28 0.489 0.356

0.91 0.57 0.71 0.93 0.07 0.57 0.15 0.54 0.17 0.17 0.47 0.28 0.93 0.49 0.75 0.22 0.10 0.13 0.16 0.47 0.90 0.36 0.25 0.39 0.63 0.453 0.28 0.519 0.468

0.93 0.42 0.83 0.93 0.15 0.54 0.24 0.39 0.14 0.19 0.49 0.28 0.92 0.52 0.79 0.25 0.11 0.14 0.21 0.42 0.91 0.31 0.26 0.39 0.47 0.449 0.28 0.510 0.392

0.92 0.47 0.80 0.93 0.25 0.50 0.53 0.34 0.16 0.52 0.47 0.35 0.87 0.53 0.64 0.14 0.11 0.13 0.24 0.40 0.81 0.31 0.26 0.42 0.41 0.460 0.25 0.506 0.422

0.95 0.45 0.85 0.95 0.36 0.76 0.37 0.52 0.44 0.25 0.71 0.33 0.94 0.78 0.83 0.17 0.13 0.18 0.27 0.46 0.93 0.38 0.38 0.60 0.77 0.550 0.28 0.618 0.459

0.93 0.29 0.82 0.95 0.30 0.64 0.38 0.63 0.27 0.31 0.64 0.34 0.93 0.65 0.82 0.16 0.19 0.18 0.25 0.43 0.83 0.36 0.33 0.61 0.67 0.517 0.26 0.560 0.435

0.97 0.35 0.93 0.97 0.95 0.90 0.51 0.55 0.47 0.27 0.92 0.71 0.93 0.91 0.86 0.54 0.55 0.35 0.27 0.52 0.95 0.91 0.30 0.76 0.81 0.686 0.25 0.747 0.766

0.97 0.39 0.96 0.96 0.96 0.92 0.59 0.64 0.49 0.45 0.89 0.86 0.95 0.90 0.90 0.56 0.64 0.35 0.30 0.55 0.95 0.86 0.46 0.80 0.91 0.731 0.24 0.739 0.861

0.97 0.36 0.93 0.97 0.95 0.90 0.30 0.56 0.45 0.37 0.92 0.72 0.93 0.91 0.86 0.43 0.49 0.42 0.27 0.52 0.95 0.88 0.30 0.75 0.83 0.682 0.25 0.747 0.754

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).

THE J OURNAL OF D E V E L O P ME N T ST U D I E S

Industry Code/year

112

TABLE A 3 TECHNICAL E FFICIENCY E STIMATES OF VARIOUS THREE-DIGIT MANUFACTURING INDUSTRIES OF BANGLADESH, 1978–1994 (BASED ON THE FULL PANEL E STIMAT ION)

113

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LIBERALISATION AND EFFICIENCY I N BANGLADESH

TABL E A4 M A X I M U M L I K E L I HOOD E S T I MAT E S ( ML E ) F OR PARA METER S O F TR A N SLO G (T L ) S TO C H A S T I C F RONT I E R P RODUCT I ON F UNCT IO N S FO R TH E SU B -PA N ELS 1978–1982, 1983–1991 AND 1992–1994

Variable description in natural logs

Parameter

Sub-Panel t Sub-Panel 1978–82 statistic 1983–91

t Sub-Panel statistic 1992–94

t statistic

Frontier function constant Kt: capital Lt: labour Tt: time Kt2 Lt2 Tt2 (Kt) x (Lt) (Kt) x (Tt) (Lt) x (Tt)

β0 βK βL βT βKK βLL βTT βKL βKT βLT

2.01 3.72 -1.89 0.01 0.10 0.39 -0.01 -0.66 0.04 0.03

1.04 6.51* 3.82* 0.35 1.09 3.54* 0.23 2.68* 1.04 0.65

6.85 1.57 -1.39 0.02 -0.04 0.11 0.00 -0.06 -0.01 -0.01

4.74* 3.29* 3.06* 0.48 1.34 2.16* 0.31 1.79* 0.27 0.32

7.28 1.51 -1.58 0.02 0.07 0.23 -0.00 -0.29 0.01 -0.01

5.47* 4.22* 3.82* 1.61 3.25* 6.04* 0.49 5.70* 1.65 0.74

Inefficiency model constant INPt KDt XORt NPLt INPt2 KDt2 XORt2 NPLt2 (INPt) x (KDt) (INPt) x (XORt) (INPt) x (NPLt) (KDt) x (XORt) (KDt) x (NPLt) (XORt) x (NPLt)

δ0 δ1 δ2 δ3 δ4 δ5 δ6 δ7 δ8 δ9 δ10 δ11 δ12 δ13 δ14

-0.36 1.85 0.23 -0.06 1.35 -0.22 0.09 -0.13 -0.33 -0.24 0.02 -0.28 0.27 -0.59 0.39

0.16 2.99* 3.22* 1.38 1.39 5.08* 0.75 1.12 3.53* 3.74* 0.29 2.44* 1.81* 2.83* 1.59

2.37 0.14 -0.14 -0.09 0.25 -0.03 -0.22 -0.18 -0.26 0.02 -0.16 -0.04 0.07 -0.47 0.20

1.99** 0.40 0.25 1.62 0.41 1.70** 2.59* 1.90** 1.94** 0.28 1.84* 0.41 0.78 2.08* 1.56

6.16 -0.90 -0.83 -1.07 0.22 0.03 0.01 -0.06 -0.19 -0.07 -0.01 0.01 0.09 -0.26 0.31

5.96* 3.82* 1.08 3.44* 0.45 1.88** 0.29 1.80* 3.63* 1.27 4.13* 0.07 1.47 3.04* 2.98

0.21

not available

-0.48

not available

-1.07

not available

7.41* 2.02*

0.22 0.11 -55.39

11.21* 4.37*

Dj’s (time dummies)

T
1 X

dt

t¼15

Variance parameters σs2 = σ2 + σv2 γ = σ2 / (σ2 + σv2) Log-likelihood LR-test of the one-sided error*** Mean TE

0.19 0.54 -64.09 153.22* 0.392

6.20* 2.82*

0.23 0.14 -144.68 125.90* 0.531

125.24* 0.554

Note: *significant at 5 per cent level or less; **significant at 10 per cent level or less; ***tests the null hypothesis of no inefficiency effects (γ =0).

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1978 1979 1980 1981 1982 1983

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

311+312 313 314 321+322 323 324 325 326 331 332 341 342 351 352 353 356 357 361 362 369 371+372 381+382 383 384 385 TE1 S.D. TE2 Median

.95 .95 .83 .78 .76 .84 .95 .96 .10 .14 .69 .74 .20 .25 .29 .22 .10 .13 .11 .09 .46 .53 .07 .10 .57 .66 .52 .58 .29 .37 .09 .09 .06 .05 .06 .09 .09 .10 .29 .32 .87 .95 .09 .10 .15 .19 .41 .49 .28 .38 .368 .404 .320 .320 .519 .546 .280 . 250

.97 .41 .88 .96 .60 .60 .28 .43 .40 .42 .41 .30 .94 .61 .95 .14 .12 .19 .29 .36 .88 .27 .33 .75 .96 .538 .291 .591 .420

.92 .84 .78 .94 .12 .62 .28 .27 .14 .07 .65 .10 .75 .51 .38 .10 .04 .09 .13 .32 .93 .10 .27 .57 .36 .411 .320 .550 .280

.94 .61 .77 .95 .07 .49 .26 .26 .10 .06 .55 .12 .75 .50 .41 .09 .06 .10 .11 .31 .72 .11 .30 .61 .39 .386 .306 .631 .300

.95 .95 .41 .57 .82 .88 .96 .96 .15 .36 .43 .50 .37 .54 .22 .38 .10 .20 .17 .55 .37 .42 .14 .38 .81 .83 .40 .53 .35 .91 .11 .14 .05 .10 .08 .13 .12 .27 .28 .34 .74 .65 .25 .25 .36 .21 .60 .61 .52 .62 .390 .491 .207 .265 .610 .549 .360 .500

.96 .21 .86 .97 .42 .49 .28 .54 .18 .63 .42 .34 .93 .56 .95 .14 .16 .12 .26 .37 .61 .28 .31 .73 .94 .506 .292 .540 .420

.94 .20 .86 .96 .42 .48 .30 .49 .25 .49 .47 .42 .90 .77 .91 .14 .22 .13 .19 .35 .82 .26 .27 .85 .93 .521 .297 .581 .470

.95 .24 .92 .96 .51 .54 .29 .48 .25 .40 .38 .43 .75 .82 .89 .14 .24 .18 .13 .20 .81 .27 .24 .88 .94 .514 .302 .609 .430

.95 .17 .75 .96 .88 .50 .22 .40 .23 .45 .46 .40 .77 .56 .75 .14 .22 .20 .11 .19 .75 .26 .25 .85 .80 .489 .287 .512 .450

.98 .19 .91 .97 .97 .78 .43 .28 .66 .12 .66 .89 .92 .84 .74 .23 .24 .27 .13 .13 .94 .45 .19 .79 .47 .576 .3123 .703 .660

.98 .15 .95 .97 .97 .70 .37 .49 .61 .19 .72 .63 .89 .90 .75 .29 .32 .29 .14 .11 .92 .73 .25 .84 .88 .610 .300 .755 .700

.98 .11 .93 .97 .97 .57 .30 .33 .46 .29 .70 .47 .82 .89 .55 .20 .21 .27 .12 .11 .86 .57 .15 .71 .55 .532 .296 .697 .550

.97 .13 .93 .97 .96 .61 .33 .32 .49 .31 .73 .49 .79 .88 .65 .27 .31 .28 .13 .14 .88 .66 .19 .73 .64 .552 .293 .699 .610

.98 .16 .94 .96 .94 .56 .39 .37 .46 .33 .75 .54 .76 .84 .74 .29 .28 .31 .14 .12 .89 .62 .22 .78 .77 .566 .289 .651 .560

.96 .16 .91 .96 .91 .54 .34 .36 .52 .39 .69 .47 .72 .78 .76 .34 .25 .29 .12 .16 .81 .54 .22 .70 .72 .545 .272 .657 .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).

THE J OURNAL OF D E V E L O P ME N T ST U D I E S

Industry Code/year

114

TABLE A 5 TECHNICAL E FFICIENCY E STIMATES OF VARIOUS THREE-DIGIT MANUFACTURING INDUSTRIES OF BANGLADESH, 1978–1994 ( BAS E D ON TH E SU B-PA N ELS ESTIMATIO N )