Bulletn of Energy Economcs http://www.tesdo.org/journaldetal.aspx?id=4 Energy Intensty and Technology Sourcng: A Study of Manufacturng Frms n Inda Santosh Kumar Sahu a,, K. Narayanan b a Madras School of Economcs, Gangh Mandapam Road, Kottur, Chenna, Taml Nadu, Inda, 600025 b Department of Humantes and Socal Scences, Indan Insttute of Technology Bombay, Powa, Mumba, Inda, 400076 Abstract Ths study focuses on the role of dfferent sources of technology n determnng the energy ntensty of the frms. Usng data from the Center for Montorng Indan Economy (CMIE), we model energy ntensty and dfferent sources of technologes usage. The major fndng of the study s; frms those use dsemboded technology mports are lessenergy ntensve. Addtonally, frms wth R and D actvtes are less-energy ntensve as compared to others. We also found an nverted U shaped relatonshp between energy ntensty and age of the frm, mplyng both older and younger frms tend to be more energy effcent. Keyword: Energy, Technology, Inda JEL Classfcaton: N7 I. Introducton Inda s a developng country wth more than a bllon populaton. There has been a rapd rse n the use of energy resources and consequently ncrease n emsson of greenhouse gas (GHG) due to structural changes n the Indan economy durng the past ffty years. The energy mx n Inda has shfted towards coal, due to hgher endowment, relatve to ol and gas, whch has led to a rapdly rsng trend of energy emssons ntenstes [1]. Energy ntensty s an ndcator that shows how effcently energy s used n the economy. The energy ntensty of Inda s over twce that of the matured economes, whch are represented by the OECD 1 member countres [2]. However, snce 1999, Inda s energy ntensty has been decreasng and s expected to contnue to decrease. These changes could be attrbuted to several factors, some of them beng demographc shfts from rural to urban areas, structural economc changes towards lesser energy ndustry, mpressve growth of servces, mprovement n effcency of energy use, and nter-fuel substtuton. Energy ntensty n Indan ndustres s among the hghest n the world. The manufacturng sector s the largest consumer of commercal energy compared to the other ndustral sectors n Inda. In producng about a ffth of Inda's GDP, ths sector consumes about half the commercal energy when the total commercal energy for ndustral use n Inda s taken n consderaton. Energy consumpton per unt of producton n the manufacturng of steel, alumnum, cement, paper, textle, etc. s much hgher n Inda, even n comparson wth other developng countres [1]. Several studes have been reported n estmatng Total Factor Productvty (TFP) and Techncal Effcency n Indan Manufacturng [3]. Studes have also estmated the TFP of energy ntensve ndustres n Indan manufacturng [4]. Many other studes have been carred out to study the varaton n R and D ntensty n Indan Manufacturng sector at the aggregate and dsaggregate levels [5] and determnants of R and D n Indan ndustres [6, 7, 8]. Demand for energy n Indan manufacturng Correspondng Author s Emal: santoshkusahu@mse.ac.n 1 Organzaton of Economc Co-operaton and Development - 66 -
ndustres at the aggregate level as well as for specfc ndustres, have also been of much nterest to the energy researchers n Inda [9]. Over the last two decades, there has been sgnfcant growth n technology sourcng, such as computer-aded desgn and manufacturng and nformaton networks n manufacturng plants. The adopton of such nnovatons by manufacturng facltes can be vewed essentally as emboded techncal change. Techncal change has two prncpal effects on plant-level producton (a) shftng the producton functon, and (b) changng the nput mx. Recently [10] examned the role of technologcal efforts and frm sze n determnng the export behavor of frms belongng to the basc chemcal ndustry n Inda. They used three dfferent econometrc models, namely, the Tobt, the two-part (Probt and Truncaton) and the Heckman sample selecton model and compared the results. The results of the econometrc exercse confrm that the technologcal efforts, frm sze and other frm-specfc characterstcs are mportant n explanng the export behavor of the frms. However less emphass s gven n examnng the role of technology n determnng the energy ntensty for any ndustry or frm. Ths paper reports on how technology sourcng nfluence the factor for example, nput choce, and, n partcular, the effect they have on plant-level energy ntensty. Most economc studes of energy and technology use aggregate or ndustry-level data and model technology as a tme trend [11]. Frms n developng countres mostly follow mported adapt technologcal strategy. They source these technologes from mports as well as through n-house R and D efforts. Import of technology could be n the form of emboded and/or dsemboded technologes. Some frms could also allow foregn equty collaboratons for sourng ther technologes. Wth a presence of adaptng energy savng measures, frm n Indan manufacturng could use any of these sources for technologes. Ths study focuses on emboded as well as dsemboded techncal change and ther relatonshp wth the energy ntensty of the frms. Further ths study examnes the queston; are plants that employ emboded technology sourcng are more energy effcent as compared to the dsemboded technology sourcng? I.I Energy Consumpton and Technology Sourcng Two basc approaches have been taken to examne the relatonshp between energy and technology sourcng. One approach s to look at case studes of energy-savng technology nnovatons or technology sourcng [12, 13, 14, 15]. Ths nvolves the analyss of ndvdual producton processes from an engneerng perspectve. A second approach models producton functons and examnes the role of technology n shftng producton functons [16]. In the sprt of the producton studes, [17] estmated the determnants of energy ntensty for Indan manufacturng ndustres. The objectve of ther work was to look at the varables affectng the changes n the energy ntensty of manufacturng frms n Inda. They found captal ntensty, sze of the frm, age of the frm and labor ntensty are one of the major determnants of energy ntensty of Indan manufacturng ndustres. We econometrcally estmate energy-factor demand equatons usng the data for the Indan manufacturng ndustres. The man focus of ths paper s to explore the relatonshp between technology sourcng and energy ntenstes n Indan manufacturng frms. The effect of ncreased automaton through the adopton of manufacturng technologes s hypotheszed to mpact the overall energy consumpton at manufacturng frms n several ways. Although these advanced technologes are not prmarly desgned to reduce the energy requred to produce a good, advanced manufacturng technologes may ndrectly reduce overall energy ntensty by havng energy effcency spllovers. Besdes the effect of advanced manufacturng technologcal advancement n producton on energy usage, ths paper examnes whether older frms are more energy ntensve than ther younger counterparts. One mght expect plant age to nfluence energy effcency and energy mx through several dfferent mechansms. The frst s a technology effect. Older frms dd not have ntal access to recent energy-savng technologes as dd younger plants. Therefore, f older frms are locked nto old technologes then they may be more energy ntensve then newer plants. The second mpact age has on energy ntensty s presented n models by [18] and [19] whch demonstrate the mportance of expected relatve prces n the choce of technology. The models above state that f expected energy prces are hgh frms wll choose less energy-ntensve methods of producton. Thus, one mght hypothesze that plants - 67 -
constructed durng the perod of hgh expected energy prces may have chosen less energy-ntensve producton facltes. Fnally, f plant survval s an ndcator of plant effcency, then old plants (plants that survve for a long tme) may n fact be generally more effcent than young plants. The thrd objectve of the paper s to observe those plants whch budget for the R and D expenses. R and D expenses s too a crucal factor that can be lnked wth the energy ntensty of the frm. More the R and D expenses, t can be hypotheszed that the ndustres/frms mprove ther technologes those produce the output and hence they nnovate less energy ntensve technology to mnmze the cost on the energy consumpton and hence mnmzng the cost of producton too. Ths n turn can be stated that the R and D expenses can have a drect relaton wth the energy ntensty of the ndustres/frms. II. An Emprcal Model of Energy Intensty To ncorporate these factors of technology nto an emprcal model of energy consumpton we follow [20] based on energy factor demand equaton from a cost mnmzaton model. Suppose each plant s short-run varable cost functon has the followng form: ~ ~ ~ ~ VC ( p, y, K, z, t) y. AVC ( p, K, z, t), (1) Where VC s a varable cost functon, p s a vector of nput prces, y s output, z s a vector of plant characterstcs expected to affect costs, K represents the fxed factor captal, t ncorporates measures of plant s technology, and AVC s an average varable cost functon. The above expresson equates total varable cost to output tmes average varable cost and mples a constant returns to scale technology. Dfferentatng (1) wth respect to the th nput prce p and applyng Sheppard s lemma yelds: C p AVC y p K z t x (2) ~ ~ *. (,,, ). p Where x s the cost-mnmzng quantty of the th factor. Ths s the standard expresson for the th factor demand equaton n a unt varable cost framework. In ths paper, we follow the approach as followed by [20] and focus on the factor demand equaton for energy and re-express t n ntensty form as: * x ~ ~ E AVC p k z t y (,,, ). (3) p E Equaton-3 express that energy consumpton per unt of output s a functon of nput prces, the captal stock, plant characterstcs, and technology. To estmate (equaton-3) we must frst specfy a functonal form for the AVC functon. We wll utlze a double-logarthmc form for the emprcal analyss, as follows: * j k k m x E p k k z t y 1 1 1 1 (4) ln ln ln The dependent varable s measured as total energy consumed for the frm n INR, dvded by net sales as a proxy for the ndustral output. The frst set of ndependent varables represent factor prces at the plant. Ths vector ncludes the plant-level labor rate. The next set of the ndependent varable presents a plant specfc measure of the fxed factor captal. One of the reason for the specfcaton s t controls the varaton n energy ntensty dfferent plant sze. The vector z contans a set of producton process varables. - 68 -
The technology term wll be modeled wth two sets of varables. Frst, the plant age varables wll be ncluded to model the overall perod of the plant and relatonshp to energy ntensty of the frm. In an mprovement to the research by [20], we have added the sze of the frm. Here the sze of the frm s measured n terms of total energy consumpton. Here sze s consdered to capture the nter ndustry dfference of frms whch consumes hgher energy and those whch consumes lesser energy, whch allow us to fnd the nter-ndustry dfferences. Increases n energy effcency may take place when ether energy nputs are reduced for a gven level of servce or there are ncreased or enhanced servces for a gven amount of energy nputs. In developng countres lke Inda, mport of technology s one of the most mportant sources of knowledge acquston by enterprse. The technology mports are lkely to affect the energy ntensty. Whether these nnovaton actvtes lead to product or process nnovaton, they may have measurable effect on energy ntensty. In case of the Indan manufacturng ndustres, at secondary level t s dffcult to get the vectors of the technology mplemented. Hence we have classfed the technology sourcng as two components. The frst component takes care of the emboded technology nterventon and the second s the dsemboded technology used n the frms. The reason for consderng age of the frm s the frms havng long span of years n producton would lkely ncur relatvely more expendture on R and D compared to younger frms and hence age of the frm may affect the energy ntensty of the frm. We have used the OLS regresson model to analyze equaton-4. The study uses the followng defntons of varables (gven n Table-1). The data used n ths analyss were collected from the Center for Montorng Indan Economy (CMIE) database from 2000 to 2008. The general form of regresson equaton takes the followng functonal form: 1 2 3 4 5 6 7 8 ln EI lnci WI AGE SIZE RI ETI DTI MNE u (5) Table-1. Defnton of the Varables and ther Expected Sgns No Varable Symbol Defnton Expected Sgn 1 Energy Intensty EI Rato of the power and fuel expenses to sales Dependent Varable 2 Captal Intensty CI Rato of the total captal employed to the +ve total value of the output 3 Labour Intensty WI Rato of the labours and salares to the +ve sales 4 Age AGE As a measure of age, we subtract the year +ve of ncorporaton from the year of the study. 5 Sze SIZE Sze of the frm s measured by the energy -ve consumed n volume. By takng the natural log of the energy consumed 6 Research RI Rato of RandD expenses to the sales. +ve / -ve Intensty 7 Emboded Technology Intensty ETI Expendture on mport of captal goods / Sales turnover of the frm -ve 8 Dsemboded Technology Intensty DTI Royalty, and techncal fees payments / Sales turnover of the frm 9 Industry Dummy MNE Ths dummy takes the value one for the foregn owned frms and zero for the rest -ve -ve - 69 -
III. Emprcal Results The followng secton deals wth the emprcal analyss of technology sourcng and energy ntensty n Indan manufacturng ndustres. As stated earler, data for the study are collected from the CMIE from 200-2008. Two dfferent specfcatons n the data set are prepared. One set of the data s the full sample. However, the second sample carres only frms wth technology sourcng. Fgure-1 gves the hstogram of the energy ntensty of the full sample. From the fgure we can observe that the densty of the energy ntensty of the frms, are rangng from -9 to 1 (gven that the energy ntensty s n terms of ts log terms). The nterestng factor here s that most of the frms fall n the range of -5 to 0. The descrptve statstcs of the sample s gven n Table-3. Fgure-1: Densty of the Energy Intensty Densty 0.1.2.3-10 -5 0 5 Energy Intensty Table-2 gves a bref outlne of the sample characterstcs. The mean energy consumed n the sample s calculated to be -3.330 (n the emprcal model we have followed a double log model). The standard devaton s calculated to be 1.81 wth the mnmum energy ntensty of -10.833 and a maxmum of 2.639. The mean captal ntensty s calculated at 3.31, wth a devaton of 1.762. The mnmum captal ntensty of the sample s -4.605 and the maxmum s at 11.754. The mean labor ntensty or the labor ntensty of the sample s found at 0.733. The standard devaton n the wage ntensty was calculated to at 1.802. In the sample t can be observed that the youngest frm s of one year and the oldest frm s of 184 years. Hence the sample represents a dversfed age structure. Therefore, t s assumed that there mght be a dfference n the energy ntensty of the frms wth an older frm and those whch are younger. A smlar case can be observed from the sze of the frm. The mean sze of the frm s calculated 1.642. The largest frm s consdered at 5.439 and the smallest frm s at -2.000. Here we can observe that n case of the sze of the frm there s a wde dfference, and ths dfference may also has an mpact on the varaton of energy ntensty of the manufacturng ndustres as well. Table-3 gves the correlaton analyss of the sample. We can observe that the labor ntensty and captal ntensty are postvely correlated. Further we can fnd out that the sze of the frm and the captal ntensty are postvely correlated. From the correlaton analyss t can further notced that energy ntensty and captal ntensty are postvely related. - 70 -
Table-2. Descrptve Statstcs Varable Mean Std. Dev. Mn Max Energy Intensty -3.330 1.317-10.833 2.639 Captal Intensty 3.311 1.762-4.605 11.754 Labour Intensty 0.733 1.802-4.605 8.250 Age of the Frm 30 19 1 184 Sze of the Frm 1.642 0.810-2.000 5.439 Emboded Technology Intensty 2.166 26.452 0.000 2553.870 Dsemboded Technology Intensty 0.059 1.822 0.000 223.880 MNE Afflaton of the Frm 0.982 0.133 0.000 1.000 Number of Observatons 33496 Table-3. Correlaton Analyss Varable EI CI WI AGE SIZE RD ETI DTI MNE EI 1.000 0.111-0.060 0.076-0.219-0.030 0.004-0.034 0.001 CI 0.111 1.000 0.805 0.199 0.809 0.125 0.203 0.077-0.164 WI -0.060 0.805 1.000 0.374 0.827 0.133 0.185 0.075-0.203 AGE 0.076 0.199 0.374 1.000 0.193 0.039 0.076 0.027-0.084 SIZE -0.219 0.809 0.827 0.193 1.000 0.124 0.188 0.081-0.146 RD -0.030 0.125 0.133 0.039 0.124 1.000 0.113 0.082-0.071 ETI 0.004 0.203 0.185 0.076 0.188 0.113 1.000 0.239-0.173 DTI -0.034 0.077 0.075 0.027 0.081 0.082 0.239 1.000-0.107 MNE 0.001-0.164-0.203-0.084-0.146-0.071-0.173-0.107 1.000 Therefore a rse n captal ntensty can lead to a postvely rse n energy ntensty. Labor ntensty s negatvely related to the energy ntensty. Age of the frm s possblty related to the energy ntensty of Indan Manufacturng. Sze of the frm s negatvely related to the energy ntensty. Hence larger frms mght be hgher energy effcent. R and D expendture and dsemboded technology sourcng are negatvely related to energy ntensty of the frm. Plant-level energy ntensty varatons depend on many factors, ncludng the producton process that s beng employed by the frm, factor prces and the dfferent technology sourcng as per the frm. In the theoretcal model we have dscussed the constructon of the model and further the econometrc specfcaton. Ths secton dscusses the emprcal results based on the regresson analyss. We estmate the mpact of the frm specfc varables on energy ntensty. Table-4 gves the output of two dfferent regresson specfcatons. Each model s modfed from the earler specfcaton. In the frst model we have ncluded the square of the Age varable to capture the non-lner relatonshp between energy ntensty of the frm. Frst model gves the regresson result of the full sample. However, the second model gves the result of those frms whch have reported technology sourcng ether n terms of emboded or dsemboded technology sourcng. Both the regressons nclude the plant age, sze of the frm, labor, gross book value, and a set of producton varables and a dummy of MNE afflaton. Examnng the results of the frst model, captal ntensty has a postve relaton wth the energy ntensty of the frm, and s hghly sgnfcant at 1%. Whch s turn means that, the frms wth the larger captal are more energy ntensve compared to those wth smaller captal. Further t can be observed that, a 1% ncrease n the captal ntensty ncreases energy ntensty by 0.63%. Labor ntensty s negatvely related to the energy ntensty and sgnfcant at 1%. Ths ndcates that as employment rses, energy per unt of output falls. The varable age and square of the age are - 71 -
sgnfcant and presents an nverted U-Shaped relaton wth the energy ntensty of the frm. Whch n turn ndcates that older the frms, lesser the energy ntensty. Further, when we observe the sze of the frm, t ndcates a negatve relaton wth the energy ntensty of the frm. Ths means, the bgger frms are more energy effcent as compared to the smaller frms. R and D varable s sgnfcant at 5% wth the energy ntensty and carres a negatve relaton. Ths ndcates that frms nvestng more n R and D are hgher energy effcent as compared to the frms nvestng less n research and development. Table-4. Results of Regresson Analyss Varables Coeffcent RSE T-value Coeffcent RSE T-value Model 1 Model 2 Captal Intensty 0.630 0.008 80.830*** 0.880 0.045 19.590*** Labour Intensty -0.038 0.009-4.300*** -0.012 0.057-0.210 Age of the Frm 0.017 0.001 16.480*** 0.003 0.001 2.400** Square of Age of the Frm -0.008 0.000-11.210*** NA NA NA Sze of the Frm -1.427 0.019-76.590*** -1.126 0.218-5.170*** Square of the Sze of the Frm -0.207 0.044-4.600*** RandD Intensty of the Frm -0.001 0.001-2.450** 0.077 0.000-0.720 Emboded Technology Intensty 0.004 0.000 3.250*** 0.004 0.001 2.860*** Dsemboded Technology Intensty -0.020 0.004-5.010*** -0.014 0.003-4.010*** MNE Afflaton of the Frm 0.098 0.056 1.730* 0.177 0.161 1.100 Constant -3.531 0.062-57.110-4.289 0.262-16.380 N 33496 N 748 F( 9, 33486) 1037.53 F( 9, 738) 62.1 Prob > F 0.000 Prob > F 0.000 R 2 0.30 0.43 Note: ***: Sgnfcant at 1%, **: Sgnfcant at 5%, *: Sgnfcant at 10%, RSE- Robust Standard Error The emboded technology ntensty varable s postvely sgnfcant to the energy ntensty, whch means frms mportng emboded technology are hgher energy ntensve. However the dsemboded technology ntensty has a negatve relaton to the energy ntensty of the frm and sgnfcant at 1%. Ths ndcates that frms mportng dsemboded technology are less energy ntensve ones. Ths means, the royalty and techncal fees payments of the frms play an mportant role n reducng the energy ntensty of the frms. Further we can observe that 1% ncrease n the dsemboded technology ntensty of the frm decreases 0.019% of energy ntensty of the frm. The MNE afflaton of the frm s not hghly sgnfcant (sgnfcant at 10%), and carres a postve sgn wth per unt of energy consumed to the output. Ths mples that the frms wth the MNE afflaton are less energy effcent as compared to the domestc frms for ths sample. The second model, gves the result of only those frms whch have reported the technology mports. We have tred many specfcatons; however we have reported the best result n Table-4. Unlke the frst model the results for the captal varable has also turned out to be sgnfcant and postve. However here a 1% change n captal ntensty leads to a hgher percent change n energy ntensty of about 0.87%. Labor varable comparng to the earler - 72 -
model has turned out to be not sgnfcant but carres the same relaton wth the energy ntensty. Age varable has also turned out to be postve as n the earler model. A postve relaton between Age of the frm and energy ntensty s found out. That mples that older frms are hgher energy ntensve. Here we have tred to look at the non-lnear relatonshp between sze of the frm and the energy ntensty. The result of the non-lnear relatonshp between the energy ntensty and the frm sze suggests hgher the frm sze of the frm lesser the energy ntensty. A smlar result s found for the emboded as well as the dsemboded technology ntensty of the frm. IV. Concludng Remarks Ths paper utlzes a frm-level data set from 2000-2008, from the CMIE to assess the role that technology sourcng and other plant-level characterstcs play n the consumpton of energy n the Indan manufacturng plants. The major result of the study ndcates that dsemboded technology use of the frms help them n reducng energy ntensty for both of the models. However, for both the models emboded technology mport has a postve relaton wth the energy ntensty of frms. Therefore emboded technology makes frms hgher energy ntensve as mport of captal goods could be hgher energy consumng. In general, the results ndcate that dfferences n plant-level energy demand are systematcally related to dentfable plant characterstcs. Even after controllng for technology mport varable, plant-level dfferences n producton process, age, technology affect, sze of the frm follows the same drecton. [20], found a smlar results for those varables. [17], found smlar results for captal ntensty, labor ntensty, age of the frm, sze of the frm. The ncreasng concern on clmate change, green house gases, and energy for future and emssons are matter of concern not only for developed countres but also for the developng as well as the underdeveloped countres. Inda beng the largest and rapdly growng developng country the ssue of energy ntensty needs specal focus. However, the dscusson on the energy ntensty should not be at the aggregate level/ at natonal level. Specfc nterest must be gven for the sub sectors as well. Ths work s an attempt n understandng the technology sourcng and energy ntensty n Indan manufacturng ndustres usng a panel data from 2000-2008. In addton, energy ntensty n Indan manufacturng frms s a matter of concern gven the hgh mport burden of crude petroleum. Concerns have been renvgorated by the global and local envronmental problems caused by the ever-ncreasng use of fossl fuels, and so t s clearly an enormous challenge to fuel economc growth n an envronmentally sustanable way. References [1]. Internatonal Energy Agency (IEA). (2007). World Energy Outlook 2007 Hghlghts. [2]. Internatonal Energy Agency (IEA). (2007). Key World Energy Statstcs. [3]. Mtra, A. Varoudaks, A. and Veganzones, M. (1998). State nfrastructure and productve performance n Indan manufacturng. Techncal Paper, OECD Development Centre, Pars. [4]. Puran, M and Jayant, M. (1998). Productvty trends n Inda's energy ntensve ndustres: A growth accountng analyss. Ernest Orlando Lawrence Berkeley Natonal Laboratory, LBNL- 41838. [5]. Kumar, N. (1987). Technology mports and local R and D n Indan manufacturng. Developng Economcs, 25(3), 220-233. [6]. Narayanan, K. Banerjee, S. (2006). R and D and productvty n select Indan ndustres. ICFAI Journal of Industral Economcs, 3(2), 9-17. [7]. Kumar, N. and Saqb, M. (1996). Frm sze, opportuntes for adaptaton, and n-house R and D actvty n developng countres: The Case of Indan Manufacturng. Research Polcy, 25(2), 712-722. [8]. Sddharthan, N. S. and Agarwal R. N. (1992). Determnants of R and D decsons: A crosssecton study of Indan prvate corporate frms. Economcs of Innovaton and New Technology, 2(2), 103-110. - 73 -
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