TECHNICAL AND ENVIRONMENTAL EFFICIENCY OF KENYAN MANUFACTURING FIRMS: A STOCHASTIC FRONTIER APPROACH

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1 Technical Asian-African and Journal Environmental of Economics Efficiency and of Econometrics, Kenyan Manufacturing Vol. 16, No. Firms 2, 2016: TECHNICAL AND ENVIRONMENTAL EFFICIENCY OF KENYAN MANUFACTURING FIRMS: A STOCHASTIC FRONTIER APPROACH Kamande Mercyline 1, Lokina Razack 2 and Kahyarara Godius 3 Abstract: This paper uses a Stochastic Frontier approach to examine the impact of clean production technology on technical and environmental efficiency. The study aims at understanding the relationship between clean production technology and productivity as measured by technical efficiency. Any positive contribution to productivity would then be reflected in the overall economic growth of the country. Firms that adopt clean production technology are deemed to be more proactive in environmental issues. The focus is on four sub-sectors of the manufacturing sector in Kenya. The study find the food processing and wood and furniture subsectors to be more efficient than the textile and garments and metal and machinery sub-sectors. The overall technical efficiency for the manufacturing sector in Kenya is found to be 59% while environmental efficiency is only 3%. Technical efficiency and environmental efficiency are found to be positively related. Further, from the overall sample, it is observed that firms that have no environmental policy are least efficient. Medium sized firms are found to be most efficient as compared to the small scale and large scale firms. Only firm age and firm size are significant in explaining variations in environmental efficiency. Accounting for environmental inefficiencies significantly improves technical efficiency. Key Words: Stochastic Frontier Analysis, Environmental Efficiency, Technical Inefficiency JEL Classification: D2, D22 & Q55 INTRODUCTION The manufacturing sector is a significant driver of economic expansion. In Kenya, it contributes approximately 13% of the Gross Domestic Product (GDP) and this contribution has been on the upward trend. In 2005, the growth rate of GDP was 4.7% which rose to 6.3% in The demand for energy in Kenya has been growing at a rate of 6% which is largely driven by investments in manufacturing sector. Given such importance, there are issues that need to be understood including whether firms, by adopting clean production technology, could increase their productivity which would then be reflected in the overall economic growth. If this were the case, firms would then be proactive in environmental management issues, which is what clean production is all about. Furthermore, African countries including Kenya are faced with the dual challenge of meeting economic development needs without increasing dependence on fossil fuel or inefficient technologies while simultaneously mitigating the diverse and complex 1 Corresponding author. Senior Lecturer, Mount Kenya University, Rwanda 2 Senior Lecturer, University of Dares Salaam, Department of Economics 3 Senior Lecturer, University of Dares Salaam, Department of Economics

2 150 Kamande Mercyline, Lokina Razack and Kahyarara Godius impacts of climate change. In most countries, economic pressures to increase industrial output have contributed to rising levels of pollution and this trend is likely to continue if current development patterns persist. It is therefore paramount for the government to encourage firms to adopt cleaner production processes by ensuring energy efficient practices and documenting their carbon footprint as a starting point to improved environment management. A shift towards cleaner fuels as a source of energy will also aid the environmental agenda. This paper examines the impact of clean production technology on technical and environmental efficiency. The objectives are fourfold which include; to measure and compare the technical efficiency of four sub-sectors in a sample of manufacturing firms in Kenya as well as overall technical efficiency of the manufacturing sector. The sub-sectors include the food processing sector; wood and furniture sector; metal and machinery sector and textile and garment sector. Secondly, using the level of technical inefficiency, an environmental efficiency measure is computed in the presence of an environmentally detrimental input, energy. The third objective is to analyze the sources of environmental efficiency variation among these firms and come up with a revised technical efficiency measure which is also environmentally efficient known as conditional environmental efficiency measure. Finally, the paper seeks to establish the relationship between technical efficiency, environmental efficiency and conditional environmental efficiency for the firms in the sample. The data is obtained from Regional Program Enterprise Development (RPED) dataset for Kenya s manufacturing sector for the years 2000 to 2002 captured by the World Bank s RPED survey of year Additional environmental data were obtained from National Environment Management Authority (NEMA), Kenya National Clean Production Centre (KNCPC), Kenya Bureau of Standards (KEBS) and United Nations Environment Programme (UNEP) publications. The paper adopts a stochastic frontier approach to estimate the technical efficiency as well as to determine the factors responsible for variations in environmental efficiency. The use of environmental efficiency instead of technical efficiency as the dependent variable in the second stage analysis justifies the use of two-stage stochastic frontier analysis as opposed to the commonly used one stage analysis suggested by Battese and Coelli (1995). INDUSTRIALIZATION AND ITS IMPORTANCE IN ECONOMIC GROWTH Industrialization is often considered as the replacement of farming and resource extraction by manufacturing and service activity. It can be defined as the process by which a country builds its capacity to process raw materials for consumption or further production (Todaro 1985). Industrialization is therefore viewed by many economies as a means to move towards economic development. As the country continue to become more industrialized it is expected that there will be a shift out of traditional, low-productivity activities towards higher technological progress within the manufacturing subsector (Naude and Alcorta, 2010). In this regards industrialization became synonymous with wealth, economic development and technological leadership and the concept of development became closely associated with industrialization. Hence industrialization is rightly seen as the main engine of growth and development (Szirmai, 2008). In developing economies, there has been a shift from agriculture to manufacturing and more recently to the service sector. In the period between 1950 and

3 Technical and Environmental Efficiency of Kenyan Manufacturing Firms , the share of agricultural sector to GDP in developing countries declined from 41 per cent to 16 per cent while the share of manufacturing to GDP grew from 11 per cent to 18 per cent in the same period. The service industry contribution grew from 40 per cent in 1950 to 51 percent in 2005 (see Szirmai, 2009). In Kenya, there has been a dual recognition of both industry and agriculture as twin engines of development (Ronge and Nyangito, 2000) which has led to the development of both sectors to be pursued simultaneously on the understanding that a productive and vibrant agricultural sector is a sound foundation for industrialization. However, like most developing countries, the share of agriculture has been declining while that of manufacturing and service sector has been on the rise. Between 1972 and 1996, for instance, the share of agriculture to GDP declined from 36 per cent in 1972 to 27 per cent in 1996 with the share of manufacturing growing from 17 per cent to 18 per cent in The services sector contributed 47 per cent in 1972 as compared to 55 per cent in Figure 1 shows the trend of manufacturing share in GDP in Kenya, when compared with Africa and the developing countries on average for the years 1950 to Figure 1: Shares of Manufacturing in GDP for different categories, Source: Smirzai (2009) From figure 1, it is evident than growth in the share of manufacturing to GDP has been slow but remains on a positive trajectory. For Africa to be part of the global economy, it must increase its output and trade by focusing on industrialization as a critical engine of economic growth and development. Mankiw (2003) argues that the best way to help the manufacturing sector is to boost the growth of the overall economy. One way to do this is for the government to give businesses greater incentive to invest using initiatives that target to lower the cost of capital and measures that will help increase the demand for investment goods that are produced in the manufacturing sector. Such initiatives will help spur investment for the benefit of the entire economy.

4 152 Kamande Mercyline, Lokina Razack and Kahyarara Godius INDUSTRIALIZATION AND THE POLLUTION PROBLEM IN KENYA Industrial processes are associated with the exploitation of natural resources and pollution of the environment. This often depletes resource bases at rates beyond the ability of nature to replenish them, destroying habitats, generating wastes and discharging pollutants into the environment (UNDP, 2005). In Kenya, the use of natural resources in the process of development exerts tremendous pressure on the environment and this is expected to continue increasing for decades to come, as the nation strives to attain an industrialized country status by the year Industrialization involves increasing the quantity and quality of energy used in the transformation of raw materials into high value goods and increased use of agrochemicals to improve agricultural productivity. These development activities are accompanied by generation of various forms of wastes into the environment. The provision of energy services is essential for industrialization, social development and improved quality of life. Providing adequate and affordable energy is essential for eradicating poverty, improving human welfare, and raising living standards. Kenya has no known deposits of fossil fuels although petroleum based fuels are the primary movers of the domestic economy which can be shown by analyzing the various energy sources and the energy consumption for various sectors. The energy consumption considered here is that energy that is available for industries. For this reason, solid and liquid biomass as well as biogas are ignored since they are mainly used for domestic purposes. For the same reasons in analyzing energy consumption by sectors, the residential sector is again ignored. Figure 2 shows energy consumption by sector for the year 1999 in Kenya. Figure 2: Energy Consumption by Sector in Kenya for 1999(in 000 metric tons of Oil equivalent) Source: World Resources Institute, Earth Trends 2003

5 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 153 It is therefore clear that fossil fuel is the largest source of energy and the major consumers are the industries and the transport sector. Currently in Kenya, consumption of fossil fuels averages 2.3 million tonnes per annum. This contributes to air pollution owing to the large amounts of emissions they release into the environment when they are burnt. Apart from the gaseous emissions, the sector is also responsible for the release of other pollutants such as obnoxious smells, particulate matter, liquid effluents, solid wastes, heat and noise. PRODUCTION EFFICIENCY AND STOCHASTIC FRONTIER ANALYSIS Stochastic efficient frontiers emerged through the works of Aigner, Lovell and Schmidt (1977), Battese and Corra (1977), and Meeusen and van den Broeck (1977). Stochastic frontier technique can be formulated in two steps: firstly, an appropriate function such as a production, cost, revenue or profit function is estimated using an econometric method such as ordinary least squares, non-linear least squares or maximum likelihood; then secondly, the estimated regression error terms are separated into two components, usually a two-sided random error component and a one-sided inefficiency component. This produces an estimate of efficiency for every firm in the estimation sample. In the mathematical programming approach, the implementation that is used most frequently is data envelopment analysis (DEA), which was originated by Charnes et. al.(1978). The method can be used to estimate production, cost, revenue and profit frontiers and provides a particularly convenient way for decomposing efficiency into its components. A major benefit of the econometric approach is that it allows firms to be off the frontier due to random error as well as inefficiency and separates purely random error from inefficiency effects. Nevertheless, it requires the specification of a functional form such as Cobb-Douglas function or the trans-logarithmic function to estimate the frontier. One of the major weakness of the econometric approach to efficiency measure is the required distributional assumptions for the error terms in order to recover the efficiency estimates, the selection of which may be arbitrary (Coelli, 1995) and could lead to specification errors. The mathematical programming approach, DEA, is nonparametric which implies that it is not necessary to specify a functional form or distributional assumptions; hence it is not prone to specification errors. However, the fact that it is not stochastic renders it impossible to isolate technical efficiency from random noise (Lovell, 1993). Therefore, any departure from the frontier is measured as inefficiency. Another advantage of DEA is that it solves the optimization problem separately for each decision making unit (Charnes, et. al. 1994) unlike the econometric models which optimize over thesample as a whole, and the estimated function is assumed to apply to all units in the sample, with all of the differences among firms captured through the estimated residuals (Cummins and Weiss, 1998). The choice of the methodology is based on the advantages and disadvantages of each of these approaches and on the data sets in use. Cummins and Weiss (1998) suggest that in datasets that are known to be noisy, the econometric approach is more appropriate because it is capable of filtering out random errors from inefficiency. When the objective is to study the performance of specific units of firms, mathematical programming is more appropriate because the optimization is conducted separately for each unit. Given the objectives of the study, a stochastic frontier analysis is more appropriate since it is required to isolate the technical inefficiencies from random noise.

6 154 Kamande Mercyline, Lokina Razack and Kahyarara Godius Stochastic frontier analysis has been found useful in investigating the influence of environmental variables on technical efficiency. One of the first attempts to explicitly model environmental efficiency was Reinhard et. al., (1999) who estimated a stochastic production frontier relating the environmental performance of individual farms to the best practice of environment-friendly farming. By modeling the environmental effect as a conventional input rather than as an undesirable output, he provides separate estimates of technical efficiency and environmental efficiency. This approach is consistent with the objectives of this paper. Although a lot of empirical work has been done on technical efficiency and inefficiency, very little effort has been made to incorporate environmental issues into efficiency studies (See forexample, Kumbhakar and Heshmati 1995, Lokina, 2008) and Kumbhakar et. al. (1991)). The few environmental efficiency studies that have been done are in the developed world and touch mainly on the agricultural sector (See Reinhard 1999). Moreover, while studies on technical efficiency and many other aspects of the manufacturing sector have been done in Kenya and Africa in general (For example Lundvall and Battese (2000), Graner and Isaksson (2007) and Niringinye et. al. (2010),), evidence on environmental efficiency of the manufacturing sector remains scanty which is mainly as a result of lack of environmental data This is the gap that this paper seeks to fill by exploring both technical efficiency and environmental efficiency of Kenyan manufacturing firms in order to assess the level of technical efficiency in the presence of environmental detrimental input. MODEL SPECIFICATION Following Reinhard (1999), a stochastic frontier production function model is specified as Y it = exp (X it + Z it + it ) (1) where all firms are indexed with a subscript i and all years are indexed with a subscript t; Y it is the vector of output; X it is the vector of convectional inputs, Z it is the environmental detrimental input in this case energy; and are the vectors of parameters to be estimated; it is a composite error term specified as it = v it u i (2) Where u i is a non-negative random error term, independently and identically distributed as N(u, u2 ), which captures the firm specific technical inefficiency in production. v it is the convectional stochastic error term which is assumed to be an independently and identically distributed (iid) normal random variables with mean zero and constant variance, N(0, v2 ). Separating the two error term components, equation 1 can be written as which can also be expressed in logs as Y it = exp(x it + Z it ) exp(v it ) exp ( u i ) (3) ln Y it = X it + Z it + v it u j (4) The stochastic output oriented technical efficiency of production of the i th firm given the level of inputs, is specified as

7 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 155 Yit TEi exp( ui ) exp( X Z )exp( v ) it it it Where u i 0 and 0 exp ( u i ) 1 Battese and Coelli (1992) defines an estimator of technical efficiency (TE) as (5) exp( u i ) 1 [ * ( i / *)] 2 TEi E exp( i * / 2) (6) it 1 ( i / * ) Where (.) is the distribution function of a standard normal random variable, 2 2 v 2 2 and * (1 )[ v ]. The technical inefficiency score is computed as minus the natural log of the technical efficiency via u E it. it The absence of the technical inefficiency term in the denominator of equation 6 is what guarantees technical efficiency. as In modeling environmental efficiency, a translog stochastic production function is specified 1 ln Y 0 ln X ln Z ln X ln X 2 it j j itj z it j k jk itj itk 1 2 j zj ln Xitj ln Zit zz ln Zit vit u i (7) 2 where jk = kj. When u i = 0, the producer is deemed to be technically efficient using X it and Z it to produce Y it. is an environmentally detrimental input which makes the producer environmentally inefficient due to use of environmentally detrimental input in production. A producer who takes environmental factors into consideration will choose to minimize the environmental impact of the production process by using Z F, which is clean fuel, deemed it environmental friendly, as opposed to Z it, which is dirty fuel, hence environmental unfriendly. A producer who uses X it and Z F in production produces the level of output, it YF, which is the it environmentally efficient output. The translog stochastic production function for an environmentally efficient producer is therefore specified as F F 1 ln Y 0 ln X ln Z ln X ln X 2 it j j itj z it j k jk itj itk 1 2 F ln ln ln 2 j zj Xitj Zit zz Zit v it (8) 2

8 156 Kamande Mercyline, Lokina Razack and Kahyarara Godius This level of output is both technically efficient due to the absence of u i in the model and environmentally efficient due to the substitution of Z it with Z F it. The logarithm of the stochastic environmental efficiency measure can be expressed as a quadratic formula shown below and solved for the positive root. LnEE it z j zj ln Xitj zz ln Zit 2 z j zj ln Xitj zz ln Zit 2zzui Following Zhang and Bao-Di Xue (2005), the environmental efficiency measure, EE, is defined as.5 / zz (9) EE = Exp(LnEE) (10) In order to explore factors that cause variations in environmental efficiency, a stochastic environmental efficiency frontier is specified as EE exp( M * it ) (11) it where all firms are indexed with a subscript i and all years are indexed with a subscript t; EE it is the vector of environmental efficiency measures; M it is the vector of explanatory variables likely to influence the environmental efficiency of a firm; is a vector of parameters to be estimated; * is a composite error term specified as it it it ui * 2 * it i. i. d. and N(0, * ) while v ui i. i. d. and N ( u, 2 it* ). it * * * ; with the assumptions that From equation 8 and following Reinhard et. al. (2002), a conditional environmental efficiency measure (CEE) can be defined as CEE it EEit * exp( u ) * i exp( M )exp( v ) it it (12) Where CEE is the environmentally efficient level of technical efficiency. A description of variables used in the analysis is presented in Table 1. As observable from Table 2 the mean value of output and all inputs does not vary much across the years and have reasonably low standard deviations. In the case of environmental management, 23% of the firms show no efforts towards environmental sustainable practices, 68% have reactive environmental management policies motivated by the need to adhere to the recently enacted environmental management regulations (see EMCA, 1999 for a detailed description of the regulations). However, only 9% show evidence of proactive environmental management policies. Figure 3 shows the distribution of firms based on their environmental management practices as reflected by the environmental management policies they have put in place. Examining the mean output for these categories, firms with no EMP have a mean output of 18.4 as compared

9 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 157 Variable Table 1 Description of the variables used in the analysis Description Output - Dependent variable proxied by value of total sales Capital - Value of capital goods including machinery and other equipment employed in the production process Labour - Poxied by number of workers Intermediate inputs - Value of purchased inputs excluding all sources of energy Energy - Environmental detrimental variable measured as value of total energy used in the firm irrespective of source Dirty fuel - Value of fuel oil used in the manufacturing process Clean fuel - Value of other sources of energy which includes electricity used in the manufacturing process EMP - Dummy variables to captures the nature of environmental management policy (EMP). Reactive EMP - A dummy variable to capture a firm that only adheres to NEMA s environmental audit requirement Proactive EMP - A dummy variable to capture firms with EMP that does more than just complying to environmental regulations by embracing clean production practices EE - Environmental efficiency measure computed from the results of the first stage analysis and is the dependent variable in second stage analysis Table 2 Summary Statistics in Years Variable Overall Log of output (2.26) (1.93) (1.99) (2.79) Log of Capital (2.49) (2.35) (2.71) (2.36) Log of labour (1.25) (1.22) (1.18) (1.33) Log of energy (2.35) (2.29) (2.53) (2.22) Log of intermediate inputs (2.29) (2.02) (2.16) (2.65) Log of dirtyfuel (2.28) (2.33) (2.27) (2.28) Log of cleanfuel (2.50) (2.34) (2.73) (2.39) No EMP (0.42) (0.42) (0.44) (0.40) Reactive EMP (0.47) (0.47) (0.48) (0.45) Proactive EMP (0.29) (0.30) (0.28) (0.28) Source: Computed from RPED Data set; The Figure in parentheses is the standard deviation

10 158 Kamande Mercyline, Lokina Razack and Kahyarara Godius Figure 2: Distribution of Firms according to nature of EMP to 19.3 and 19.5 for firms with proactive EMP and reactive EMP respectively. This suggests that while a environmental management policy may have a positive impact on a firm s performance, the motive behind the environmental management policy may not matter. ESTIMATION OF TECHNICAL EFFICIENCY Pooled OLS An average production function is estimated using pooled OLS in order to show how different variables relate to output. Energy is differentiated into dirty and clean fuel in order to estimate the effect of each separately. The dummy variables for reactive and proactive EMP provide information on the impact of the environmental management policies on the output of firms. The location dummies capture the impact of geographical location of the firms. The results are reported in Table 3. Examining the results, capital, labour and inputs have a positive impact on the output of firms and they are all significant. However, their elasticity differ with the elasticity of output with respect to inputs being highest followed by that of labour and then that of capital. Both dirty fuel and clean fuel are not significant explanatory factors of the firm s output. The location dummies are all negative and significant. In the fully controlled model, the dummy variable for reactive EMP is significant and positive while the dummy for proactive EMP is insignificant. This implies that taking environmental factors into consideration matters for the firm s output but the motive behind the EMP does not matter. However, in the presence of technical inefficiency, pooled OLS would be a wrong model specification that could possibly influence the outcome. It is therefore appropriate to explore the assumption of technical inefficiency further The test for skewness of OLS residuals is used to check the presence of inefficiency. (Nikaido, 2004). According to Reinhard et. al. (2002), given the underlying assumption that

11 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 159 Table 3 Results of Pooled OLS Variable Basic model With EMS With EMS and (no dummies) dummies Loc dummies Constant 2.291*** 2.397*** 3.150*** (0.583) (0.608) (0.668) Log of Capital 0.066** 0.069** 0.071** (0.027) (0.027) (0.027) Log of Labour 0.124** 0.117** 0.116** (0.053) (0.054) (0.054) Log of intermediate inputs 0.849*** 0.842*** 0.839*** (0.031) (0.031) (0.031) Log of dirty fuel (0.041) (0.043) (0.043) Log of clean fuel (0.038) (0.040) (0.040) Reactive EMS Dummy * (0.165) (0.166) Proactive EMS Dummy (0.299) (0.299) Loc Nairobi Dummy ** (0.275) Loc Mombasa Dummy *** (0.306) Loc Nakuru Dummy ** (0.342) Loc Eldoret Dummy ** (0.333) Adjusted R squared Skewness of residuals N = 224 Source: The Figures in parenthesis are the Standard errors, ***,**, and * denote Significance levels at 1%, 5% and 10% respectively based on t-statistics u i > 0, a negatively skewed residual, i = v i u i, implies the presence of technical inefficiency in the data A positive skewness of the residual is therefore considered problematic because it cannot be reconciled with a one-sided distribution of inefficiencies that is positively skewed. Waldman (1982) suggest that when an industry shows positive skewness of the residuals, it is assumed that there are little if any inefficiencies. Carree (2002) shows that a positive skewness of the residual suggests a one-sided distribution that has low probabilities for small inefficiencies and high probabilities of large inefficiencies. The results in Table 3 report a negative skewness of OLS residual. This implies that the firms in the sample are characterized by inefficiencies. Reinhard et. al. (2002) suggested computing the skewness of the OLS residuals as a way of testing the appropriateness of the frontier specification. Since the skewness of residuals has been established to be negative, a stochastic frontier function is appropriate for this analysis.

12 160 Kamande Mercyline, Lokina Razack and Kahyarara Godius STOCHASTIC FRONTIER ANALYSIS OF TECHNICAL EFFICIENCY As discussed earlier, stochastic frontier analysis has been widely used in estimating technical efficiency due to its ability to decompose the composite error term into a technical inefficiency term and a stochastic error term. Before embarking on the stochastic frontier estimation, there is need to establish statistically the presence of technical inefficiencies. This is implemented using the generalized likelihood ratio statistic which tests the null hypothesis that inefficiency effects are absent from the model. This hypothesis is specified as H 0 : = 1 = = 5 = 0. Another aspect of the stochastic frontier analysis is that it can assume either a trans-logarithmic or a Cobb-Douglas functional form. In order to determine the more appropriate of the two functional forms, the generalized likelihood ratio test is used to test the null hypothesis that Cobb Douglas production function is an adequate representation for the data given the specifications of the translog function. It is specified as H 0 : ij = 0 for all i,j = 1.4. To implement the generalized likelihood ratio test, the likelihood ratio statistic is given by LR 2[ Lˆ ˆ R LNR ] where Lˆ R and Lˆ NR are the estimated log-likelihood of the restricted model and of the non-restricted model respectively. In this case L ˆ R is the Cobb-Douglas function and L ˆ NR is the translog function. The LR statistic has a 2 (DF) type distribution, where DF shows the difference in the degrees of freedom among the various models. If the calculated value is greater than the critical value, then the null hypothesis is rejected, otherwise it is accepted and the translog specification is adopted. Table 4 reports the generalized likelihood ratios for overall sample and different sectors. Table 4 Generalized Likelihood Ratio tests for Technical Efficiency Overall Food Wood and Textile and Metal and Critical Sample Processing Furniture Garments Machinery Value H 0 : = 0 for all i,j = 1.4(Cobb Douglas function) ij 26.70* * 25.32* 48.66* H 0 : = =.= = 0 (No inefficiency effects) * * 33.59* Neutral vs- Non-neutral model 17.86* Source: Own computation from Frontier 4.1 estimation. When the calculated value exceeds the critical value, the null hypothesis is rejected.. * denotes cases where the null hypothesis is rejected. Critical values are obtained from Kodde and Palm, 1986, The critical values are at 5% significance level The results in Table 4 show that the Cobb-Douglas function is rejected for the overall sample and for all other sectors except the food processing given the assumption of the translog stochastic frontier model. Therefore, the translog specification is adopted for the overall sample and three sectors but Cobb Douglas specification is adopted for the food

13 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 161 processing sector. The null hypothesis of no technical inefficiency effects is rejected for the overall sample and for two sectors namely textile and garments and metal and machinery. For the food processing and wood and furniture sectors, the null hypothesis of no technical inefficiency effects cannot be rejected. The neutral model is rejected in favour of non- neutral model for the overall sample. Stochastic frontier regression is conducted using Frontier 4.1 software. Table 5 reports the estimates of the translog for full sample and for different sectors. Table 5 Translog Stochastic Frontier Estimation of Technical Efficiency Variable Full Food Wood and Textile and Metal and Sample Processing Furniture garments Machinery Constant * *** *** 40.13*** (4.587) (1.225) (1.922) 0.997) (1.006) Log of Capital *** 2.104*** *** (0.298) (0.030) (0.754) (0.548) (0.808) Log of Labour *** 1.408** 2.065*** 12.54*** (0.540) (0.069) (0.837) (0.709) (2.291) Log of Intermediate 1.352*** 0.912*** 9.111*** 3.153*** *** Inputs (0.329) (0.032) (0.406) (0.378) (0.742) Log of Energy *** 1.711*** *** (0.323) (0.049) (0.387) (0.324) (0.718) (½)Log (K x K) *** 0.070*** 0.233*** (0.014) (0.086) (0.033) (0.097) (½)Log (L x L) *** (0.068) (0.165) (0.108) (0.519) (½)Log (I x I) *** (0.013) (0.072) (0.045) (0.132) (½)Log (E x E) (0.016) (0.023) (0.011) (0.058) Log (K x L) 0.093*** 0.118* *** (0.027) (0.080) (0.048) (0.199) Log (K x I) *** *** (0.014) (0.070) (0.033) (0.055) Log (K x E) *** *** (0.014) (0.026) (0.013) (0.084) Log (L x I) *** *** (0.035) (0.083) (0.058) (0.279) Log (L x E) 0.009*** (0.026) (0.060) (0.028) (0.109) Log (I x E) 0.023* 0.165*** *** 0.117*** (0.016) (0.030) (0.020) (0.058) Sigma-squared 1.110*** 0.454**** 0.011*** Gamma 0.609*** Mean TE N Source: From Stochastic Frontier Analysis using Frontier 4.1.The Figures in parenthesis are the Standard errors, ***,**, and * denote Significance levels at 1%, 5% and 10% respectively based on t-statistics

14 162 Kamande Mercyline, Lokina Razack and Kahyarara Godius Examining the results in Table 5, it is observed that in the overall sample and in the textiles and garments sector, the coefficients of all inputs are positive as expected. This is consistent with Biggs et. al. (1995). The other three sectors report different signs for different coefficients with capital being negative for the food processing, wood and furniture and metal and machinery sector while energy and intermediate inputs coefficients have a negative sign in the metal and machinery sector. These results are comparable with the results of Lundvall and Battese (2000) who find a negative coefficient in the wood, textile and metal sectors. For labour, they find a negative relationship in the food sector but a positive relationship in wood and textile sector. In the overall sample, the level of technical efficiency is 59%. The food processing and wood and furniture sub-sectors were found to be highly efficient with 99% technical efficiency, followed by textile and garments which has a technical efficiency of 74% and lastly metal and machinery with a technical efficiency of 54% Given that the variance parameter,, is only significant for the overall sample, the technical inefficiency term is only significant in the overall sample but not in any of the sub-sectors. A high value of the variance parameter, implies that of the total variation captured by sigma squared, most of it is as a result of the technical inefficiency in production processes. Conversely, a low value of the variance parameter, implies that the stochastic error term captures noise but not technical inefficiency. For this reason, only the technical inefficiency in the overall sample is worth investigating further. It is important to examine further if other firm specific characteristics contribute to the technical inefficiency. These factors include firm size and environmental orientation. Table 6 gives the mean technical efficiency across different environmental orientations defined by EMP scores and for different firm sizes defined by number of employees. The results show that firms with no EMP are the least efficient with a technical efficiency level of 57% followed by the reactive group and proactive group which score 59% and 60% respectively. Variable Table 6 Mean Technical Efficiency by Sector and Environmental Orientation Overall Sample No EMP Reactive EMP Proactive EMP Small Medium Large Overall TE Source: From Stochastic Frontier Analysis using Frontier 4.1 No EMP are the firms that have no established EMP i.e. EMP = 0 Reactive EMP are the firms that have regulatory based EMP i.e. EMP = 1 Proactive EMP are the firms that have CPI and ISO based EMP i.e. EMP = 2 and 3 Small represents firms with less than 50 employees Medium represents firms with 50 to 100 employees Large represents firms with over 100 employees

15 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 163 As pertains to firm size, medium sized firms are found to be most technically efficient while large firms are found to be least efficient. Although most studies have found a positive relationship between technical efficiency and firm size (see Lundvall and Batesse, 2000 and Niringiye et. al., 2010), the observed pattern agrees with Biggs, et. al. (1995) who found an inverted U-shaped association between firm size and efficiency where the size-efficiency relationship is negative for large firms and positive for small firms and medium-sized firms found to be the most efficient. STOCHASTIC FRONTIER ANALYSIS OF ENVIRONMENTAL EFFICIENCY After establishing the level of technical efficiency of the firms in the sample, the second task of this paper is to characterize the level of environmental efficiency of the firms and explore sources of variations in the level of environmental efficiency. Following the methodology proposed by Reinhard et. al. (2002), a measure of environmental efficiency is computed using the technical inefficiency term in the first stage and the parameters of the technical efficiency model. Once the level of environmental efficiency is established, factors that are likely to cause variations in environmental efficiency are investigated. Environmental inefficiency may arise from the use of environmentally detrimental inputs. In this paper, energy is the detrimental input. To capture the environmental impact of energy, total energy is differentiated by source isolating dirty fuel from clean fuel and the impact of each is observed separately. The use of intermediate inputs is also deemed to have an environmental impact. If the intermediate inputs are not used efficiently, they generate a lot of solid waste. The actual amount of intermediate inputs may be rising but the resource productivity may be going down and waste going up. In the second stage of analysis, the computed environmental efficiency measure (EE) is the variable which is used as a dependent variable. Unlike the technical inefficiency term that is assumed to be independently identically distributed, no such assumption is attached to the environmental efficiency measure (Reinhard et. al. 1999) hence it is appropriate to be used as a dependent variable. In this second stage of analysis, the aim is to establish if controlling for the environmental impacts associated with intermediate inputs and dirty fuel has an impact on technical efficiency. This involves estimating a stochastic frontier model with environmental efficiency as the dependent variable with the variables that are deemed to have an environmental impact being the explanatory variables following Reinhard et. al. (1999). Given that the technical inefficiency term was only significant for the overall sample, the second stage estimation of environmental efficiency is also done for the overall sample. A translog stochastic frontier model is chosen as opposed to the Cobb-Douglas form given the results of the generalized likelihood ratio test where the computed likelihood ratio is which is greater than the critical value of at 5% significant level for the degrees of freedom equal to 15 (Kodde and Palm,1986). Thus, the null hypothesis that the Cobb-Douglas frontier is an adequate representation of the data, given the specifications of the translog function is rejected and a translog function adopted. The results of the translog stochastic frontier are presented in Table 7.

16 164 Kamande Mercyline, Lokina Razack and Kahyarara Godius From the results the variables that are significant in explaining the variations in environmental efficiency include firm age and squared firm size. Firm size has a negative impact which agrees with previous findings by Bhandari and Maiti (2007) while squared firm age parameter is also negative. Table 7 Translog Stochastic Frontier Estimation of Environmental Efficiency Variable Coefficient Variable Coefficient Constant Log (II x DF) (0.364) (0.002) Log of Intermediate Inputs Log (II x CF) (0.023) (0.002) Log of Dirty fuel Log (II x FS) (0.042) (0.003) Log of Clean Fuel Log (II x FA) 0.011*** (0.043) (0.004) Log of Firm size Log (DF x CF) (0.052) (0.002) Log of Firm age *** Log (DF x FS) 0.006* (0.088) (0.004) (½)Log (II x II) Log (DF x FA) (0.001) (0.006) (½)Log (DF x DF) Log (CF x FS) (0.003) (0.003) (½)Log (CF x CF) Log (CF x FA) (0.002) (0.005) (½)Log (FS x FS) *** Log (FS x FA) (0.007) (0.007) (½)Log (FA x FA) Sigma-squared 0.006*** Gamma Mean EE Mean CEE Source: From Stochastic Frontier Analysis using Frontier 4.1.The Figures in parenthesis are the Standard errors, ***,**, and * denote Significance levels at 1%, 5% and 10% respectively based on t-statistics While the environmental efficiency score is quite low, once we have accounted for variations in environmental efficiency the conditional environmental efficiency improves drastically to 99%. The magnitude of the sigma squared shows that only less than only 1% of the variations are unexplained which is an indicator that the larger proportion of technical inefficiency captured in stage one of this analysis may be associated with environmental inefficiency. Of the total unexplained variation still existing after accounting for variations that result from the firm choices, none is attributable to technical inefficiencies as shown by the almost zero parameter. However, given that most of the environmental variables are insignificant in the second stage analysis casts doubts as to whether the environmental inefficiency is as a result of fuel and input choices. Firm age and firm size are the only variables that seem to contribute to the

17 Technical and Environmental Efficiency of Kenyan Manufacturing Firms 165 environmental inefficiency. This is consistent with the findings of Lundvall et. al., (1999) who found a strong relationship between firm size and technical efficiency. GRAPHICAL ANALYSIS OF EFFICIENCY LEVELS Technical efficiency is postulated to be both necessary and sufficient for environmental efficiency (Reinhard et. al. 1999). Looking at the mean values of technical efficiency and environmental efficiency presented in previous sections, it is revealed that on average, firms are generally environmentally inefficient with a mean environmental efficiency score of and the highest score being Given that the mean technical efficiency term is 0.587, it is evident that technical efficiency does not imply environmental efficiency. However, the two parameters may move together in the sense that firms that are more technically efficient also score a relatively high environmental efficiency score. This is evident in Figure 4 which shows the relationship between technical efficiency and environmental efficiency. Figure 4: Trend of Technical and Environmental Efficiency Although technical efficiency is higher than environmental efficiency in all cases, firms with a high technical efficiency also report a high environmental efficiency while in most cases, where technical efficiency dips, the environmental efficiency follows suit. This implies that technical efficiency and environmental efficiency are positively related which is consistent with the findings of Reinhard et. al. (2002). It is postulated that the technical inefficiency stipulated in this study is due to environmentally detrimental factors. Once the effects of these factors are controlled in the second stage analysis, a new measure of technical efficiency which Reinhard et. al. (2002) calls Conditional Environmental Efficiency (CEE) is obtained which is very high. This level of efficiency (CEE) is both technically and environmentally efficient. Figure 5 shows the relationship between TE and CEE.

18 166 Kamande Mercyline, Lokina Razack and Kahyarara Godius Figure 5: Trend of Technical and Conditional Environmental Efficiency It is evident that technical efficiency is higher after the variations in environmental efficiency are accounted for. It can therefore be deduced that environmental efficiency improves technical efficiency. Looking at the correlation coefficients between technical efficiency, environmental efficiency and conditional environmental efficiency, TE and TI are highly correlated with a coefficient of but the correlation between TI and CEE is quite low standing at 0.12 showing that the new level of technical efficiency is not associated with the inefficiency due to environmental factors. The correlation between TE and EE is positive but quite low with a coefficient of 0.26 while EE and CEE have a positive correlation of Again the high level of technical efficiency indicated by CEE is related to the improvement in environmental efficiency. CONCLUSION AND RECOMMENDATIONS The food processing and wood and furniture sub-sectors were found to be more efficient than the textile and garments and metal and machinery sub-sectors. The overall technical efficiency for the manufacturing sector in Kenya was found to be 59% while environmental efficiency was only 3%. The high technical inefficiency reported by firms was associated with low level of environmental efficiency and the two were found to be inversely related. Further, from the overall sample, it was observed that firms that had no environmental policy were least efficient. Medium sized firms were found to be most efficient as compared to the small scale and large scale firms. On the factors that were thought to have an impact on environmental efficiency, only the marginal effects of squared firm size and firm age were significant. However, the drastic improvement of the technical efficiency score once the variation in environmental efficiency

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