X4 o ST- THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. 1 49 -74 FILE COP o l4 The Scope for Fuel Substitution in Manufacturing Industries: A Case Study of Chile and Colombia Diana L. Moss and James R. Tybout This article analyzes plant-level panel data from Chile and Colombia to assess how manufacturers might respond to carbon taxes and other policies that induce substitu- tion between clean and dirtyfuels. Is producerflexibility linked with sector of activity, capital vintage, or rates of new capital formation? When adjustments in energy use occur, are they accomplished through changes in factor proportions for individual producers, changes in the output shares of producers within an industry, or changes in the relative production levels of different manufacturing industries? Patterns of energy use within and between industries show thatfiscal policies can significantly influence the level and mixture of energy use among manufacturers. To anyone who has visited Mexico City, Santiago, Beijing, or Bangkok, the problem of industrial pollution is obvious. But despite increasing concern, effec- tive strategies for emissions control have yet to be designed and implemented. Eskeland and Jimenez (1990) cite the following barriers to efficient abatement policies: public sector budget constraints (which inhibit public spending on monitoring, enforcement, and cleanup); uncertainty regarding abatement costs or benefits (which complicates the evaluation of net social returns to alternative policies); distributive concerns (which make it difficult to impose significant adjustment burdens); and the influence of private interest groups (which can lead to serious loopholes and windfalls for politically powerful agents). In view of these barriers, Eskeland and Jimenez propose that "fiscal policies not directly linked to pollution damage or emissions-such as commodity taxes, subsidies, or public sector prices-can be efficient complements to direct instruments" (p. 6). Not only can such policies potentially generate revenue, but they are also more difficult to manipulate for political reasons and are relatively easy to administer. Because energy intensity and fuel choice are important determinants of air pollution, selective fuel taxes would be candidates for such-rather blunt-indirect instruments. Diana L. Moss is with National Economic Research Associates, and James R. Tybout is with the Department of Economics at Georgetown University. This article was funded by the World Bank research project "Pollution and the Choice of Policy Instrument in Developing Countries" (RPo 676-48). The authors thank Gunnar S. Eskeland, Luis Gutierrez, Emmanuel Jimenez, Mark Kosmo, David Wheeler, and three anonymous referees for comments; and Jane Lay for computing assistance. © 1994 The International Bank for Reconstruction and Development / THE WORLD BANK 49 SO THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. 1 'Althouggh appealing, the Eskeland-Jimenez strategy must be weighed against the possibility that technologies permit little fuel substitution within most indus- tries, especially in the short run. If producers' flexibility is limited, changes in the relative prices of different types of energy are mainly redistributive and might put many firms out of business or at a serious competitive disadvantage in world markets. This article is a first step toward evaluating the Eskeland-Jimenez perspective. Using plant-level panel data from manufacturing industries in Chile and Colom- bia, we address two fundamental issues. * What do recent experiences in these countries suggest about the latitude for energy conservation and fuel substitution?' Is producer flexibility linked with the sector of activity, capital vintage, or rates of new capital formation? More broadly, what can be said about the relative importance of technology and the economic environment in determining patterns of energy use? * When adjustments in energy use take place, are they accomplished through changes in factor proportions for individual producers, changes in the out- put shares of producers within an industry, or changes in the relative pro- duction levels of different manufacturing industries?2 What do these adjust- ment patterns suggest about the effect of energy policy on the structure of production? To a lesser degree, we also examine the relation between energy intensity and energy prices and the question of whether there is likely to be a tradeoff between various policy objectives (such as a sensible trade policy) and pollution abatement. The novelty of this study is that it is based on plant-level panel data from Chile and Colombia. These data provide detailed information on the quantities and values of various fuels consumed by all establishments with at least 10 workers during 1979-85 for Chile and 1977-89 for Colombia.3 Hence, it is possible to construct much better quantity and price measures than have been used in analyses based on aggregated data. Moreover, given that plants can be tracked through time, the data allow us to decompose the behavior of sectoral aggregates into changes in individual plant behavior and changes in the mix of plants. Section I describes the three sources of changes in industrial energy use. Sec- tion II examines descriptive statistics on industrial energy use in Chile and Colombia: first, we examine indexes of energy use for the manufacturing sector and for specific industries and document the effect of product mix on aggregate 1. We use "energy" to mean a (value-weighted) sum of energy sources such as fuels and electricity. 2. We use "producer" equivalently with "plant,' and we use "industry" to mean specific subsectors of the manufacturing sector. Generally these are identified by a three-digit isic code. 3. In connection with an earlier research project on industrial performance (RPo 674-46), the data were provided to the World Bank by the Chilean National Statistics Institute (INE) and the Colombian National Department for Statistics Administration (DANE). Moss and Tybout 51 energy use. Then, we decompose intra-industry adjustments into changes in the output shares of plants (interplant adjustment) and changes in the energy inten- sity of individual plants (intraplant adjustment). Section III assesses temporal trends in energy prices to determine whether relative price changes have been associated with any of the dimensions of adjustment described above. Two earlier studies have already estimated various price elasticities of energy demand using our data (Eskeland, Jimenez, and Liu 1991; Guo and Tybout 1993). We review their findings and relate them to observed adjustment patterns. Section IV develops a simple error components model to evaluate sources of variation in patterns of fuel use across plants within each industry. We introduce industry-specific variables such as location, plant entry, and size, and attempt to account for any unexplained variation in expenditure shares and energy prices across plants. Section V concludes with general observations. I. DIMENSIONS OF ADJuSTMENT Changes in industrial energy use can be thought of as coming from one of three sources: changes in the interindustry mix of goods produced, changes in the intra-industry output shares of the producers, or changes in the intrafirm energy intensity of individual producers. Although little work has been done on intra-industry output shares, there is evidence that the other two dimensions of adjustment have been important sources of change in industrial countries. For example, with respect to the interindustry mix of goods, changes in the sectoral composition of manufacturing in the United States have significantly reduced the use of fossil fuels. This reduction has occurred as the "smokestack" industries-chemicals, coal, iron and steel, paper products, and petroleum refining-have given way to nonelectric machinery, electronics, and instruments (Doblin 1988). The net effect has been to reduce energy needs per unit of industrial production and to increase the importance of electricity as an energy source in relation to fossil fuels. Intraplant adjustments in energy intensity have also been extensively docu- mented in the industrial countries.4 These adjustments can take several forms. First, for a given capital stock, plants make short-run substitutions among labor, capital, materials, and energy in response to shocks in the factor and output markets. Second, over time, equipment is renewed. New technologies become embodied in firms' capital stocks, either through the wholesale replacement of equipment ("process innovation") or through the retrofitting of old capital. Process innovation is a major undertaking, typically accomplished in the long run, whereas retrofitting allows for substitution between capital and energy or between fuels, or both, in the shorter term. Examples of process innovation 4. For example, Doblin (1988), Meyer (1974), and Gowdy and Miller (1987) identify a decrease in the energy intensiveness of the U.S. manufacturing sector. Ostblom (1982) draws a similar conclusion for the Swedish economy, as do Cattell (1982) and Thomas and MacKerron (1982) for the United Kingdom. Sterner (1985), however, identifies an increase in energy intensity in the Mexican manufacturing sector. 52 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. 1 include the displacement of blast furnaces by arc furnaces in the steel industry and of wet kilns by dry kilns in the cement industry (Sterner 1989). Examples of retrofitting include the installation of more efficient motors, the addition of heat exchangers to boilers, and the addition of combustion controls to existing equip- ment. Efficiency can also be enhanced in the short run with housekeeping mea- sures such as cleaning or replacing flues, smokestacks, furnaces, and plant insulation. To determine policy, it is important to know which of the three dimensions of adjustment have been significant and how much latitude each provides for response to changes in the incentive structure. The balance of this article is devoted to shedding light on these issues. II. AN OVERVIEW OF PATTERNS OF ENERGY INTENSITY Most analyses of industrial energy use in the semi-industrial countries have been based on data aggregated over plants and industries.5 Thus, there is no set of "stylized facts" about the patterns of energy use in plant-level panel data, and the resulting implications regarding the questions raised above. To help fill this void, we begin with an overview of the Chilean and Colombian data. All quan- tity and share measures are Laspeyres indexes constructed with plant-specific prices held constant at 1980 values, and all price measures are Laspeyres indexes constructed with plant-specific quantities held constant at 1980 values. (The appendix provides details.) We use these indexes, rather than Divisia indexes, to isolate pure quantity effects from effects that are a result of price variation. Of course, the cost of this approach is that 1980 prices become increasingly inap- propriate weights over time. Manufacturingwide Aggregates To gain a general sense of the levels of, and variability in, fuel intensities, we start, in table 1, with time trajectories of the share of energy spending in total variable costs and in gross output. Real fuel use as a percentage of real variable costs varies from a high of 5.73 to a low of 4.46 in Chile and a high of 3.83 to a low of 3.30 in Colombia. One immediate implication of these relatively small shares is that, overall, manufacturing subsectors could absorb substantial fluctu- ations in fuel prices without large changes in operating profits. However, as will be seen shortly, a handful of subsectors are much more dependent on energy than the average numbers suggest. Given that prices are held constant for our energy use indexes, the fluctuations reported in table 1 do not reflect revaluation effects. Rather, they reflect changes in the output shares of plants, entry, exit, or adjustments in energy intensity among incumbent surviving plants. Interestingly, energy intensity appears to be procyclic in Chile, where the correlation between fuel intensity and manufactur- 5. Notable exceptions include Sterner (1989) and Lee and Pitt (1987). Moss and Tybout 53 ing employment levels is, 0.52 for the sample period 1979-85. However, in Colombia this correlation is -0.11. The contrast between these countries could reflect the fact that Chile underwent a major recession in the early 1980s that eliminated nearly one-third of its plants. Two other contrasts between Chile and Colombia are noteworthy. First, Chile managed to conserve some energy during 1979-85, whereas Colombia did not. If electricity generated is produced with purchased fuels (as opposed to feed- stocks), our measure of total energy use overstates actual use. This bias proba- bly grows with time in Chile because self-generated electricity grows, so the actual reduction in energy use is probably more dramatic than is indicated by table 1. Second, even after this conservation effort, manufacturing remained more energy intensive in Chile than in Colombia. This partly reflects the impor- tance of copper smelting, which is an energy-intensive subsector, in Chilean manufacturing. But comparisons by subsector reveal that, in many industries, Chile also uses more energy per unit of output than Colombia does. One reason may be that Chile has a much cooler climate. Differences in the policy regime and in the relative prices of energy may also be important. We cannot pursue the relative-price explanation without actual output prices for standardized units of products. Unfortunately, only price indexes are available. Next we consider shares of specific energy sources in manufacturers' total spending for energy. (Details of the construction of these shares are in the appendix.) Results for Chile appear in table 2, and for Colombia, at a more aggregated level, in table 3. In both countries, spending for electricity-the most important source of energy-increases fairly steadily as a percentage of total spending for energy. Holding prices constant, electricity's share of total spending for energy in Chile went from 37 percent in 1979 to 45 percent in 1985; in Colombia, a milder increase, from 49 percent in 1977 to 52 percent in 1988, was registered. Interestingly, the movement toward electricity use in Chile was accomplished mainly through self-generation by manufacturing plants; the share for self-generated electricity rose from 6.5 to 11.1 percent of total spending for energy during the sample period. Colombian manufacturers, whose use of elec- tricity increased much less, were already generating electricity accounting for about 11 percent of total energy spending in 1977, and the share remained around this figure for the entire sample period. Self-generation may allow feed- stocks to be used as inputs in electricity production and by-product heat and steam from electricity production to be used as inputs in output production. If self-generation promotes energy efficiency (for example, by allowing the plant more flexibility in fuel purchases), it could account for the overall patterns in energy intensity found in table 1. In the Chilean case, it was possible to disaggregate nonelectric energy sources by type of fuel. This exercise (table 2) reveals that the use of fuel oil dropped significantly during the sample period, by 1985 reaching about 80 percent of its 1979 share of total fuel use. However, diesel's share more than doubled between 1979 and 1982 before dropping back to its 1979 level by 1985. Also, among the Table 1. Manufacturingwide Energy Intensity in Chile and Colombia, 1977-88 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 Chile Share of energy spending in total variable costs, SVC (percent) - - 5.29 5.73 4.89 5.02 5.09 4.69 4.46 - - - Ratio of total energy use to total output, SQ - - 3.88 3.98 3.41 3.43 3.73 3.70 3.61 - - - '4 Colombia Share of energy spending in total variable costs, SVC (percent) 3.50 3.30 3.30 3.56 3.81 3.83 3.82 3.81 3.58 3.68 3.48 3.45 Ratio of total energy use to total output, SQ 2.64 2.39 2.37 2.66 2.86 2.75 2.68 2.68 2.49 2.55 2.51 2.46 - Not available. Note: The construction of SQ and SVC is discussed in the appendix. Moss and Tybout 55 Table 2. Manufacturing Energy Mix in Chile, 1979 and 1985 (percent) Share of manufacturers' total spendingfor energy, SEk Source of energy 1979 1985 Electricity 37.20 45.25 Generated 6.52 11.14 Purchased 31.22 34.66 Sold 0.55 0.55 Fuel oil 31.75 25.85 Diesel 8.52 8.29 Stone coals 4.55 8.03 Coke 0.38 0.75 Fuelwood 1.67 3.68 Coal 2.39 2.04 Gasoline 3.30 1.90 Liquid gas 1.59 1.69 Paraffin 3.75 1.27 Other fuel 4.06 0.86 Piped gas 0.84 0.40 Total 100.00 100.00 Note: Construction of SEk is discussed in the appendix. less important fuels, the use of two relatively dirty energy sources (stone coals and fuelwood) increased during the sample period, whereas the use of two relatively clean sources (gasoline and paraffin) declined. Combined, the former went from 6.2 percent of total fuel use to 11.7 percent; the latter went from 7.1 to 3.2 percent. The association between these patterns of change in fuel intensity and changes in relative prices will be examined below. Overall, then, there is significant movement toward using electricity and away from using other fuels in both countries. Given that electricity is generated mainly by hydro power in both countries, this is presumably a desirable trend toward cleaner production. We also see considerable flexibility in the use of other fuels, especially stone coals and fuel oil. However, on the basis of tables 2 and 3 alone, we cannot tell whether these adjustments were accomplished at the cost of contraction or failure among plants using dirty fuels relatively heavily. Table 3. Manufacturing Energy Mix in Colombia, 1977 and 1988 (percent) Share of manufacturers' total spendingfor energy, SEk Source of energy 1977 1988 Electricity 48.88 52.23 Generated 10.71 10.58 Purchased 40.36 42.74 Sold 2.19 1.10 Other fuels 51.12 47.77 Total 100.00 100.00 Note: Construction of SEk is discussed in the appendix. 56 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. 1 Accordingly, we now turn our attention to the industry- and plant-specific en- ergy usage patterns that lie behind the aggregates in tables 1, 2, and 3. Industry-Specific Energy Intensity Table 4 presents energy intensities by subsector through time. There is dra- matic cross-industry variation in the fuel intensity of production. Seven indus- tries stand out as considerably more fuel intensive than the rest in both coun- tries: paper, industrial chemicals, ceramics, glass, cement, iron and steel, and nonferrous metals. Moreover, it is not unusual to find that fuel intensities vary dramatically within industries over time. For example, as a percentage of real gross output, energy use in the industrial chemicals subsector drops from 9.2 to 6.2 in Chile (1979-85) and from 7.3 to 3.7 in Colombia (1977-88). Likewise, for glass, energy use drops from 13.6 to 8.0 in Chile, and from 15.3 to 7.9 in Colombia. Ceramics, however, becomes more energy intensive in Chile (rising from 7.3 to 11.4) and less energy intensive in Colombia (decreasing from 15.6 to 11.4). Overall, 16 of the 28 Chilean industries and 16 of the 26 Colombian indus- tries showed at least some reduction (from the beginning to end of the period) in energy use per unit of output.6 These changes are partly caused by capacity utilization effects, but it appears likely that longer-term shifts in the fuel mix have taken place during the sample period. Moreover, the fact that adjustment patterns are very country specific for some industries (paper, rubber, and ce- ramics) suggests that local economic conditions can play a potentially large role in determining energy efficiency. Changes in the composition of energy sources are also substantial within some industries. Although the changes are too extensive to report here in their en- tirety, industry-specific series on shares of energy sources in total energy spend- ing by manufacturers show that the Chilean apparel subsector goes from 35 percent electric to 60 percent electric during 1979-85, while the Colombian wood products industry goes from 35 to 61 percent electric over 1977-88. Also, nonmetallic mineral production goes from 21 percent coal and 25 percent stone coals to 1 percent coal and 46 percent stone coals. (Industry-by-industry figures are available from the authors.) Product Mix and Intra-industry Adjustments To determine the relative importance of changes in product mix and in intra- industry energy use in shaping the aggregate energy intensity of manufacturing, we begin with a simple decomposition. Aggregate energy use. Let SQmt be total energy use expressed as a percentage of total output for subsector m in year t, and define acmt = Ymt,/ jYjt as subsec- 6. The petroleum refining industry was omitted from the analysis for Colombia because of suspicious data. Moss and Tybout 57 Table 4. Energy Expenditure as a Percentage of Gross Output by Subsector in Chile and Colombia, Selected Years Chile Colombia Industry 1979 1985 1977 1988 Food 3.48 3.05 1.35 1.11 Beverages 1.80 1.48 2.01 1.89 Tobacco 0.18 0.26 0.29 0.93 Textiles 4.28 3.49 2.41 2.79 Apparel 1.29 0.71 0.67 0.52 Leather 2.33 2.21 1.82 1.55 Footwear 0.60 0.57 0.77 0.86 Wood 3.06 1.97 4.51 2.14 Furniture 0.78 1.01 1.22 0.94 Paper 7.67 8.07 4.91 4.81 Printing 0.98 1.35 0.59 1.59 Industrial chemicals 9.17 6.23 7.29 3.67 Other chemicals 1.15 1.03 0.83 0.73 Petroleumrefining 1.19 0.28 n.a. n.a. Petroleum products 4.79 5.13 n.a. n.a. Rubber 3.86 2.36 1.98 1.96 Plastics 1.94 2.16 1.68 2.23 Ceramics 7.27 11.43 15.59 11.44 Glass 13.61 8.00 15.32 7.85 Cement 13.00 12.37 12.20 11.20 Iron and steel 9.26 9.51 4.92 5.47 Nonferrous metals 6.03 6.14 2.83 3.15 Metal products 2.39 2.03 1.50 1.72 Nonelectric machinery 2.40 3.75 1.92 1.29 Electricmachinery 2.06 1.70 1.12 0.93 Transport equipment 1.20 1.70 0.70 1.36 Professional equipment 3.13 2.54 1.35 0.84 Miscellaneous industries 1.44 2.25 1.64 1.28 n.a. Not applicable. Note: Construction of energy expenditure as a percentage of gross output, SQ, is discussed in the appendix. tor m's share in total manufacturing output during year t, where Yj, is total output of subsector j.7 Then the aggregates in table 1 are related to their industry-specific counterparts by the identity SQ., = E..SQ,tmce,n, and the change in manufacturingwide energy intensity between period t - 1 and period t may be written as (1) A(SQ.t) = Em ASQmt (>m + Em Aamt SQm. where SQm. = 1/2(SQmt + SQmt,-) and a,,. = 1/2(amr + amt,-). The first term on the right side of equation 1 is a share-weighted sum of the changes in energy intensities of each industry, and the second term is an intensity-weighted 7. All variables are measured in constant prices. 58 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. I sum of the changes in output shares of each industry.8 (Changes in intensity for entering and exiting plants are all set equal to zero.) The individual elements of each sum are too numerous to report; here we focus on several key findings about the changes between the initial and final sample years. For Chile, recall from table 1 that the change in manufacturing- wide fuel use over the sample period was A(SQ.,) = 3.61 - 3.88 = -0.27 percentage points, or about an 8 percent decline. Using the decomposition (the first term on the right-hand side of equation 1), we find that this decline reflected within-industry changes in energy intensity (holding output composition con- stant) of EmASQmtam. = -0.47 and that this decline in energy use was offset partly by shifts in production toward more energy-intensive products: SmAactmtSQm- = 0.20 (see table 5, row 1). So the Chilean manufacturing sector could have reduced its energy intensity almost twice as much if it had maintained its 1979 output mix. (Contrast this with the United States, where changes in output mix have tended to reduce energy intensity.) Nonetheless, effects within industries were dominant and in the direction of energy conservation. The main subsector responsible for the drag on energy saving during 1979-85 was nonferrous metals, which in Chile is mostly copper. This industry, which rapidly expanded its share of output, is one of the most energy intensive, and it became more so during the sample period. Partly offsetting the drag on energy saving were petroleum refining (which produced a smaller share of industrial output and became more energy efficient) and ferrous metals (which produced a smaller share of output but did not become more energy efficient). At the aggregate level, Colombian manufacturing exhibits a qualitatively simi- lar pattern: the total change in energy intensity was -0.18, but it would have been -0.39 if output shares had not been reallocated toward relatively energy- intensive producers (see table 5, row 5). The most important energy savings came from the food subsector (which accounts for about 20 percent of total output and mildly improved efficiency) and the industrial chemical subsector (which accounts for about 6 percent of output and dramatically improved efficiency). In summary, energy savings have been accomplished in both countries without scaling back the energy-intensive industries. To the contrary, these industries have apparently gained market share while there has been a secular trend toward increasing energy efficiency within industries (recall that important energy price shocks occurred before the sample periods). Energy-intensive subsectors have saved the most, possibly because they reap a relatively high return from doing so. And their growth may have partly reflected the relocation to Latin America 8. Because changes in energy intensity (SQ) are weighted by averaged shares (et_.) rather than base shares (%_,), the magnitude of this component depends partly on changes in shares, and similar com- ments apply to the component describing share effects. Nonetheless, because it is a product of changes, this effect is in the second order of smallness. To see this, write equation 1 as SE,ASQ.,[1_ _1 + 1/2A(x_,] + E_Ace_jSQm, t- + 1/2ASQ_,]. Also the difference in magnitude between our share effect and our intensity effect is the same as it would have been if base year weights had been used. Moss and Tybout 59 Table S. Energy-Specific Decomposition of Changes in Energy Intensity in Chile, 1979-85, and in Colombia, 1977-88 (expressed as change in percentage of output) Total change Within-industry Output mix in energy effect, effect, intensity, Type of energy BmASQ,,ta,, rmActSQm A(SQ.t) Chile, 1979-85 Total -0.47 0.20 -0.27 Diesel -0.05 0.02 -0.03 Electricity 0.15 0.05 0.19 Fuel oil -0.42 0.12 -0.30 Colombia, 1977-88 Total -0.39 0.21 -0.18 Electricity -0.11 0.10 -0.01 Otherfuels -0.28 0.11 -0.18 of energy-intensive producers from the North. However, given that energy costs do not account for a large proportion of total costs in most industries, it is likely that other forces not related to energy have also been at work. Decomposing particular energy sources. Thus far we have used our decom- position (equation 1) to study changes in aggregate energy intensity. We now apply the same methodology to study changes in the intensity with which each of the major fuels is used. This exercise will allow us to determine the relative importance of output mix effects and within-industry effects in accounting for the shift away from fossil fuels toward electricity that was documented in tables 2 and 3. Results for diesel, electricity, and fuel oil are presented for Chile in table 5, as is a decomposition of electricity use for Colombia. Several patterns merit note. First, for fuels, both countries follow the pattern for aggregate energy use discussed above. In Chile, the use of diesel and fuel oil per unit of output decline by 0.03 and 0.30, respectively, and general fuel use per unit of output declines in Colombia by about 0.18. These are substantial drops, given that these ratios begin at 0.33, 1.23, and 1.35, respectively. Sec- ond, however, patterns of electricity usage evolved very differently in the two countries. Chilean industries tended to become more electricity intensive at the same time that the output mix shifted toward electricity-intensive products. (Most of this was caused by increased electricity intensity in paper and pulp and in nonferrous metals-16 of the 28 three-digit industries showed reductions in electricity intensity.) But Colombian producers were attempting to economize on electricity usage while an offsetting product shift was taking place. In the next section we return to the issue of whether contrasting electricity prices in Chile and Colombia might account for this pattern. Finally, as we saw with aggregate energy use, adjustments within industries dominate changes among industries in output shares, or, in other words, overall changes in energy use are qualitatively similar to changes within industries. 60 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. I Intra plant, Compared with Interplant, Adjustment in Energy-Intensive Industries Thus far, we have seen that energy-intensive subsectors need not contract in order to save energy overall. But the adjustment burden may still be high if these savings are accomplished by forcing out the most energy-intensive plants within each subsector. To see if this type of adjustment explains the trend toward energy conservation documented at the industry level in tables 4 and 5, we now reapply our decomposition (equation 1) industry by industry. For this exercise we rein- terpret SQmt to be the energy intensity of the mth plant, and ,tmt to describe the mth plant's share in industrywide output. Tables 6 and 7 report this decomposi- tion for the relatively energy-intensive subsectors. Many of the figures in table 6 are large in relation to the figures in table 5. This is presumably because industries that use energy most intensively have the most latitude for adjustment. Furthermore, in most instances adjustment within plants was a more important source of change than adjustments in the output shares of different plants. (The exceptions are iron and steel in both countries, nonferrous metals in Chile, and cement in Colombia.) The importance of adjust- ments within plants suggests that substantial changes in energy intensity may be feasible without forcing plants to shut down. In both countries, producers of glass and industrial chemicals provide dramatic examples of this type of adjust- ment. In Colombia, ceramics shows dramatic improvements in energy efficiency within plants; but in Chile, equally dramatic adjustments within plants go in the Table 6. Industry-Specific Decomposition of Changes in Energy Intensity in Chile, 1979-85, and Colombia, 1977-88 (expressed as change in percentage of output) Total change Within-plant Output in energy effect, share effect, intensity, Industry 1m_Ar m SQm A(SQ.d) Chile, 1979-85 Paper 0.25 0.15 0.40 Industrial chemicals -3.05 0.11 -2.94 Ceramics 4.33 -0.18 4.16 Glass -5.48 -0.14 -5.62 Cement -0.83 0.20 -0.63 Iron and steel -3.12 3.37 0.25 Nonferrous metals -0.44 0.55 0.11 All industries -0.47 0.20 -0.27 Colombia, 1977-88 Paper 0.25 -0.35 -0.10 Industrial chemicals -2.26 -1.36 -3.62 Ceramics -4.03 -0.11 -4.15 Glass -7.05 -0.42 -7.47 Cement -0.13 -0.86 -0.99 Iron and steel -0.02 0.57 0.56 Nonferrous metals 0.60 -0.23 0.31 All industries -0.39 0.21 -0.18 Moss and Tybout 61 "wrong" direction. Such contrast suggests that the economic environment can play an important role in determining adjustment patterns. To complete our analysis of adjustment within industries, we disaggregate changes in the energy mix variables, industry by industry. (This exercise is done for Chile only; data do not permit analogous disaggregation for Colombia.) Results based on a variant of equation 1 are reported in table 7 for the three most important energy sources and the seven most energy-intensive sectors. Not surprisingly, we still find that most of the important adjustments are coming within plants, rather than through changes in market shares. This pattern is especially clear in the paper and industrial chemicals subsectors, both of which show dramatic shifts toward the use of electricity and away from the use of fuel oil. Iron and steel producers also shift away from using fuel oil. What might explain intraplant shifts in the mix of energy inputs? Industrial energy is used mainly to generate heat (with boilers and furnaces) and to drive machinery. Energy applications of the latter type are most likely to account for short- and medium-run shifts toward electricity use: motors and compressors that run on different fuels can be swapped without wholesale replacement of equipment. This may explain why the heavily mechanical paper and pulp indus- try showed a substantial increase in the use of electricity during the sample period. However, in Chile much of the increased demand for electricity was met by self-generation, suggesting that additional forces were at work. Self- generation was concentrated in the paper and pulp, industrial chemical, and petroleum refining industries-the same industries that have shifted toward self- generation in the United States. As in the United States, technological change may have moved these subsectors toward cogeneration, which produces electri- cal power and useful thermal output, both of which have uses in these indus- tries. Also, and perhaps more important, the increase in self-generation may have reflected better use of feedstocks-wood waste, blast furnace gases, and by-products of petroleum refining-as inputs for self-generation. The decline in fossil fuel consumption by the paper and pulp, industrial chemicals, and petro- leum industries as self-generation increases supports this latter explanation. In principle, high-temperature furnaces-used for melting, firing kilns, refin- ing, reheating, and smelting-might also have accounted for some of the move- ment out of fossil fuels. These furnaces can be fueled with coal, coke, or electric- ity. Moreover, furnace technology has changed significantly in the past several decades, widening the menu of available equipment. (The advent of the electric arc furnace, used to melt scrap steel in minimills, is a notable example.) Substi- tution between electricity and fossil fuels is not possible, however, without major adjustments in capital stocks. This may explain why the use of coal and coke shows no clear tendency to decline during the sample period (table 2). There is, however, evidence of significant substitution within several industries during this period: the nonmetallic minerals industry shows a clear tendency to substitute stone coals, and (less dramatically) electricity, for coal, and the iron and steel industry shifts out of using petroleum products and into using coal. Table 7. Industry-Specific Decomposition of Energy Mix in Chile, 1979-85 (expressed as change in percentage of total energy) Electricity Fuel oil Diesel Within- Between- Within- Between- Within- Between- plant plants Total plant plants Total plant plants Total Industry effect effect effect effect effect effect effect effect effect Paper 22.09 1.68 23.77 -27.54 8.89 -18.66 0.84 0.33 1.18 ON Industrial chemicals 15.25 4.01 19.26 -23.64 -4.67 -28.31 0.06 1.72 1.79 Glass 3.39 -1.04 2.35 0.72 2.73 3.45 1.03 0.96 1.98 Ceramics -9.01 -0.24 -9.25 -1.24 0.44 -0.80 2.52 0.72 3.24 Cement 2.68 1.98 4.67 -0.64 -4.37 -5.01 4.24 -0.81 3.43 Iron and steel 1.56 -2.82 -1.26 -13.18 -0.72 -13.91 -0.X1 2.15 1.34 Nonferrous metals 0.78 6.09 6.17 7.81 -6.01 1.80 -0.65 0.49 -0.16 Moss and Tybout 63 These changes may well reflect the installation of new furnace equipment at major plants. Boiler applications were probably the least important in providing latitude for substitution between electricity and fossil fuels, but they may have accounted for much of the substitution among fossil fuels. Boilers are typically fired with coal, natural gas, or oil (technological factors make electricity very inefficient). In Chile, natural gas is not widely available, so most heating is presumably done with coal and oil. Even among fossil fuels, boilers are typically designed to use only one type of fuel, although dual-fuel boilers can be built. Hence, unless Chilean manufacturers anticipated significant changes in the relative costs of coal and oil, it is unlikely that the short-run changes we observe in the sample years in coal and oil intensities reflect boiler adjustments. However, some manu- facturers may have switched boiler types or retrofitted for alternative fuels over the longer term. III. TEMPORAL TRENDS IN PRICES We have mentioned several ways in which energy intensity and fuel mixtures can be adjusted, and we have found that, particularly within certain three-digit industries, considerable adjustment has taken place at the plant level. We now wish to determine whether strong relative price changes have been associated with these adjustments. Tables 8 and 9 present our manufacturingwide Laspeyres price indexes for each fuel category. Several general observations are in order. First, in Chile the cost of energy rose almost 30 percent more than output prices for manufacturers during 1979-85. Similarly, in Colombia the cost of energy rose 36 percent in relation to the wholesale price index during 1977-88. These relative price changes may largely explain the observed overall reductions in energy intensity within plants (see table 6, column 1). Second, although the cost of electricity rose less than the aggregate energy price index in Chile, the cost of electricity rose substantially more than the aggregate en- ergy price index in Colombia. (This contrast was at least partly the result of pric- ing policies that favored residential over industrial users in Colombia.) Corre- spondingly, manufacturers shifted toward reliance on electricity in Chile and away from reliance on electricity in Colombia (see table 5, column 1). The shift toward electricity was sufficiently strong in Chile that electricity per unit of output rose for manufacturing overall, even as energy per unit of output fell. Finally, further evidence of a negative association between prices and fuel intensity is apparent in the Chilean data, where we can isolate utilization pat- terns for the various fossil fuels. For example, the ascendance of stone coals from 4.55 percent of total fuel use in 1979 to 8.03 percent in 1985 (table 2) is matched by a relatively modest rate of growth in stone coal prices, especially through 1984. Likewise, the growth of coke's share in total fuel use through 1984 (from 0.38 percent in 1979 to 0.93 percent in 1984) mirrors a low rate of 64 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. I Table 8. Energy Price Indexes in Chile, 1979 and 1985 (1980 = 100) Type of energy 1979 1985 Electricity 95.7 327.3 Coal 57.4 298.6 Stone coals 73.1 303.0 Coke 88.1 467.3 Fuel oil 73.8 418.1 Diesel 76.0 355.6 Gasoline 77.2 358.3 Paraffin 65.4 375.2 Liquid gas 64.0 365.2 Piped gas 62.0 327.4 Fuelwood 108.5 364.7 All types of energy 69.2 351.6 Manufacturing wPI 74.7 296.7 Note: All indexes except the manufacturing wholesale price index (WPI) are constructed from the annual industrial survey data as described in the appendix. The manufacturing wPi is taken from Banco Central de Chile (1986). Table 9. Energy Price Indexes in Colombia, 1977 and 1988 (1980 = 100) Type of energy 1977 1988 Electricity 45.6 830.0 Fossil fuels 44.4 492.3 All types of energy 46.5 646.7 Manufacturing WPI 53.5 548.2 Note: The indexes are constructed from the annual industrial survey data as described in the appendix. price increase through that year, and the precipitous drop in coke's share in 1985 (to 0.75 percent) is associated with extremely rapid inflation in coke prices. Similar remarks apply to diesel, which showed mild price increases and rapid growth in usage through 1982, then reversed on both counts in 1984 and 1985. (Diesel fuel is mostly imported in Chile; the dramatic price swings represent both a reduction in import barriers and a maxi devaluation.) Can we conclude that producers are responsive to prices? Elsewhere, Eske- land, Jimenez, and Liu (1991) use the Chilean data, aggregated up to the sector level for each of 13 regions, to econometrically estimate partial and total price elasticities of energy demand in several sectors.9 They find considerable respon- siveness to prices, and, in particular, that the partial own-price elasticity of demand for electricity is -0.982. When the effects of electricity prices on total energy use are factored in, this elasticity becomes -1.203. Other energy 9. By "partial" we mean that these elasticities describe substitution among energy sources when total spending on energy is held constant. "Total" elasticities allow for the fact that changes in energy prices will affect aggregate spending on energy. Moss and Tybout 65 sources-especially diesel and gas-show even higher elasticities; only the de- mand for coal is unresponsive to price changes. (See Westley [1992] for a fairly comprehensive survey on the price elasticity of demand for electricity among Latin American manufacturers.) Further evidence is provided by Guo and Tybout (1993), who estimate partial price elasticities using cross-plant variation in the Chilean data. They base their estimates on a variant of the maximum likelihood estimator developed by Lee and Pitt (1987). This estimator deals with the technical problem that any par- ticular plant will use only one or several fuels out of the available set. In sectors where the Guo-Tybout model performs well, the results confirm that the partial price elasticities of demand for electricity are substantial: -1.36 in bakeries, -0.498 in meatpacking, and -0.401 in metal products. Furthermore, in some sectors, substantially larger (negative) price elasticities are found for many fuels. For example, the price elasticity of demand for fuelwood is - 1.80, and the price elasticity of demand for "other fuels" (the residual third category) is -2.44 in bakeries. For meatpacking the same two elasticities are -3.75 and -1.24, respectively. Finally, elasticities are found to depend very strongly on plant size because production technologies are nonhomothetic. For example, among bak- eries the partial price elasticity of demand for electricity ranges from about - 1.4 for the smallest plants to about -2.5 for the largest. All of these results are consistent with our general thesis that price variation can influence energy use patterns among manufacturers, even in the relatively short term. However, price-quantity associations need not imply causation, and many alternative readings of the data are possible. For example, most manufac- turing equipment is imported in these countries, and, to the extent that techno- logical innovations are embodied in all new equipment, manufacturers might have been passively pulled toward energy conservation even without fuel price incentives. IV. ERROR COMPONENTS ANALYSIS OF SOURCES OF VARIATION By looking only at sectoral and industry-level summary statistics, we obscure the tremendous heterogeneity in patterns of fuel use across plants within each industry. This heterogeneity is potentially produced by cross-plant price varia- tion, capital vintage effects, product differentiation, regional market effects, market power, and variation in productive efficiency. Having already considered price variation, this section looks at the remaining factors. For our analysis of plant-specific variation in energy expenditure shares, we employ the following error components model: (2) Zit =E amDTmt + E Xj DRij + E T/k DIk + 01 INCUMBi + 02 ENTRANTi + (1 SIZEi + 02 INVESTi + 033 T*INVEST, + bi + Ej,. The symbols in equation 2 are defined in table 10. We assume that bi and ei, are orthogonal to all explanatory variables with a plant i subscript. This allows us 66 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. 1 Table 10. The Error Components Model Symbol Definition Zit One of the plant-level energy share measures of energy intensity (the share of a spe- cific energy source in total energy spending, SEK, or the share of energy spending in total variable costs, SVC). D T_t Time dummy for period m that takes a value of 1 when m = t, and 0 otherwise. DRij Location dummy that takes on value of 1 when plant i is in regionj, and 0 other- wise. DIik Industry dummy that takes a value of 1 if plant i is a member of three-digit industry k, and 0 otherwise. INCUMB, A dummy that takes a value of 1 if plant i is present in all sample years, and 0 other- wise., ENTRANTi A dummy that takes a value of 1 if the ith plant enters the data base during the sam- ple years (and stays in)., SIZE- A measure of the size of the ijh plant (the logarithm of its mean real output level). INVEST, A measure of the rate of capital stock replacement (the log of the plant's mean in- vestment level minus SIZE). T A time trend. bj An unobservable (time-invariant) plant effect. e~it Remaining unexplained variation. a. The omitted class is plants that exit the data base. to use standard error components estimators to fit equation 2 and to isolate sources of variation in each plant-level index of fuel use.10 Unlike the sectorwide and industry-specific statistics discussed already, the error components results do not weight plants in proportion to their size. Hence, given that small plants are much more common than large plants, our findings in this section are driven mainly by the abundance of smaller plants. Unexplained Variation and the Role of Prices Table 11 presents our results explaining share in real expenditure for various fuel types. As discussed earlier, the dependent variables are calculated using plant-specific base year prices, so cross-plant variation reflects variation in both price and quantity. The most remarkable result in this table is that the variations in fuel shares across plants are mainly caused by unexplained plant effects and unexplained random noise. " For example, in Chile, total sample variation in energy expenditure as a percentage of variable cost is 0.0082. Of this figure, 44 percent is attributable to variation in bi, and another 49 percent is attributable to variation in eftj Only the remaining 7 percent is explained by industry dummies, time dummies (not reported), plant size, location, incumbency, and rate of investment. Similar comments apply to Colombia, although 29 percent of the variation in energy expenditure as a percentage of variable cost is explained 10. In principle, cross-equation constraints should be imposed that ensure that predicted fuel shares sum to one. We forego this bit of rigor to save on computing costs. 11. When interpreting this table, it must be borne in mind that each plant's fuel mix is evaluated at its own (plant-specific) base year price vector. Hence some cross-plant variation in the dependent variable is due to variation in the relative prices of various energy sources. Moss and Tybout 67 Table 11. Error Components Model of Real Expenditure Shares in Chile and Colombia Chile Colombia Share of Share of Share of Share of energy electricity energy electricity spending in in total spending in in total total energy total energy Explanatory variable spending, variable spending, variable costs, SVC SE1 costs, SVC SE1 Size 0.001 0.006 -0.006 -0.016 (2.25) (2.37) (-6.52) (-2.89) City 1 -0.018 0.084 -0.049 0.152 (-7.70) (9.02) (-11.74) (6.01) City 2 -0.017 0.052 -0.056 0.259 (-4.41) (3.53) (-12.04) (9.17) Incumbent -0.010 -0.010 0.011 -0.120 (-3.30) (-8.32) (2.99) (-5.46) Entrant -0.005 0.013 -0.011 0.041 (-1.34) (0.98) (-2.68) (1.59) Investment/GDP, I/Q 0.001 -0.015 0.003 0.014 (2.63) (-11.02) (1.63) (1.46) Dependence of trend -0.000 -0.000 0.000 -0.002 on investment/GDP (-0.66) (-0.17) (2.67) (-3.34) ratio, T* (I/Q) Variance of Unobservable 0.004 0.063 0.001 0.043 plant effect, 6 Unexplained 0.004 0.028 0.000 0.027 variation, c Energy share 0.008 0.121 0.002 0.083 measure, Z Mean Z value 0.039 0.607 0.033 0.649 Note: t ratios are in parentheses. For Chile, all equations are estimated with 29,231 observations. For Colombia, a random sample of 6,151 plants was used to save on computing costs. Time dummies and three-digit industry dummies are included in each regression but not reported. The city dummies represent Santiago and Valparaiso/Vina in Chile; Bogota and Medellin in Colombia. Dummies for six additional cities are included but not reported for Colombia. there. Moreover, even if we limit our analysis to electricity, the explained por- tion of variation in real expenditure shares is only 25 percent in Chile and 16 percent in Colombia.12 These findings mean that there is tremendous within-industry heterogeneity in technologies (because of capital vintage effects and product heterogeneity) or that the economic incentives that influence fuel mixtures vary dramatically across plants within an industry (for example, because of regional fuel price variation or price discrimination) or that unexplained variation in base year 12. More detailed results on other energy sources for Chile are available from the authors. These results follow the same pattern. The reader may recall that dramatic cross-industry variation in energy intensities and fuel mixtures were reported in section I and may wonder why industry dummies do so little to explain variation in our error components model. The answer probably lies in the fact that the results in section I are dominated by the largest producers in each subsector, whereas the results in table 5 are dominated by the small (relatively common) plants. 68 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. 1 (1980) prices across plants generates corresponding variation in expenditure shares, because these are measured in plant-specific base year prices. We have no direct data on technologies, but price variation appears to be part of the story. A fourth possible explanation is measurement error problems. However, we view this as unlikely, given that the fuels are relatively homoge- neous and the units of measurement are clearly defined in the survey forms. Further regressions (not reported) confirm that some of the cross-plant price variation is correlated with variables on the right-hand side of our error compo- nent model. So the coefficients in table 11 really represent an amalgam of technology, price, and other effects. Explained Variation What about explained variation in table 11? First, there is surprisingly little covariation of energy shares with plant size: t-statistics are small (given sample sizes) and the signs of the coefficients vary across countries. Bear in mind, however, that the reported regressions describe only the typical pattern of devia- tion from industry norms, because industry dummies control for industrywide size effects. Large plants in Chile generally rely more heavily on coal, diesel, fuel oil, and stone coals, whereas small plants rely much more heavily on fuelwood. Hence the incidence of fuel-specific taxes across the plant size distribution is likely to be far from uniform. (The strong nonhomotheticities in production technology that Guo and Tybout [1993] find with these data also imply unequal incidence.) Also location matters. Plants in the major cities (Santiago and Valparaiso/ Vina for Chile, Bogota and Medellin for Colombia) tend to be less energy intensive overall. In particular, these establishments rely relatively less on coal, coke, diesel, fuel oil, and stone coals and relatively more on electricity. So it appears that fuel mixtures are cleaner in the major cities, and less energy is used per unit of output. Several explanations for city effects are possible. First, al- though our industry dummies crudely control for product mix effects, goods manufactured in industrial centers probably differ from others within each of the three-digit industries. Second, fuel prices differ across regions, affecting both producer choices and the plant-specific price weights in our indexes. Finally, there may be complementarities between fuels and other factors (such as unskilled labor) that exhibit regional price and quality variation. To the extent that the latter two explanations hold true, the results in table 11 are another manifestation of the price sensitivity of fuel demands. It is not possible to observe the age of plants in our data base, but we can distinguish plants that enter the data base during the sample period from those already in it. Given that the data cover all plants with at least 10 workers, these are either new plants or plants that have crossed the 10-worker threshold. As a first pass on the issue of vintage effects, we compare these to incumbents and to plants that exit the data base. (The latter are the omitted category.) Our results indicate that the three groups are not very different in terms of overall energy Moss and Tybout 69 intensity, although incumbents seem systematically less reliant on electricity than are entrants and exiting plants, both of which tend to be relatively young. On the one hand, this suggests an embodiment effect. On the other hand, aside from the association between rapid investment and low reliance on electricity in Chile, there is little evidence that plants that are replacing their capital stocks differ systematically from others in energy usage. (This observation holds also for the nonelectric energy sources.) So new capital equipment may not be neces- sary for adjustments in energy usage patterns. V. CONCLUSIONS Although we have not provided definitive answers to the questions we set out at the beginning of this article, we have reported several results that shed light on them. At the aggregate level, with respect to the latitude for changes in energy use, we found that Chilean manufacturers managed to mildly reduce the energy intensity of their production during 1979-85. Colombian manufacturing was less energy intensive on the whole, and its energy intensity decreased as well, albeit by a smaller amount than in Chile, during 1977-88. More dramatically, Chilean manufacturers shifted away from fossil fuels and toward electricity. Both of these adjustments were primarily within plants rather than caused by changes in the mix of manufacturing goods produced. We see a different pattern in Colombia, where individual plants tended to become less electricity intensive during 1977-88, but where changes in the manufacturing output mix offset this tendency to economize on electricity. Generally, intra- industry and intraplant reductions in the use of fossil fuels and energy overall tended to dominate changes in the manufacturing output mix. Accordingly, it appears that substantial energy savings are possible without forcing widespread shutdowns among energy-intensive producers. Combined with complementary studies on demand elasticities, our results suggest that the intra-industry adjustment away from electricity in Colombian manufacturing was probably caused in part by a rapid increase in the price of electricity during the period and that the adjustment toward electricity in Chile was probably partly the result of a fall in the price of electricity in relation to fossil fuels. Relative price changes for other fuels also matched changes in fuel intensities with surprisingly little lag. In Chile, there is evidence that new capital equipment has embodied technolo- gies that use electricity. First, much of the new demand for electricity was met with a secular trend toward self-generation; self-generation is not easily accom- plished without new equipment. Second, new plants differed systematically in their energy usage from others of comparable size in the same industries. In both Chile and Colombia, new firms tended to rely more on electricity and much less on petroleum, than incumbents did. This suggests that new technologies were becoming embodied in the manufacturing capital stock through entry. As for the incidence of energy taxes and subsidies, we found that energy use in Chile was concentrated in a handful of industries: food (because it is a big 70 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. I subsector); copper (because it is big and energy intensive); and cement, indus- trial chemicals, iron and steel, paper, and petroleum (all of which are moderate in size and very energy intensive). The most important adjustments during the sample period came from petroleum refining (which became more energy effi- cient), iron and steel (which shrank), and copper (which grew while becoming less energy efficient). In Colombia, industrial chemicals, ceramics, glass, paper, cement, and iron and steel consumed the largest amounts of energy. The first three of these industries displayed the most significant adjustments, and the direction of these adjustments was similar to that in Chile. Given these patterns of energy intensity, it is difficult to generalize about whether macro and trade policies are energy saving or energy using. Some products consistent with Chile's comparative advantage use considerable energy per unit of output. Copper and paper and pulp are prominent among these. However, it is not hard to find energy-intensive products among the import- competing subsectors (for example, industrial chemicals and iron and steel) or among nontradables (for example, cement). Patterns of fuel use across industries do suggest that taxes and subsidies designed to discourage reliance on "dirty" energy sources will have uneven incidence. For example, the brunt of adjustment to a tax on coal usage is likely to be concentrated in a few subsectors, such as iron and steel, and nonmetallic minerals. In general, there is tremendous heterogeneity with respect to overall energy intensity and to fuel mixtures within each industry. Our error components model explained relatively little of the variation in fuel mixtures, but it did reveal that large firms rely relatively heavily on diesel, electricity, and fuel oil. Also, firms in the major cities (Santiago and Valparaiso/Vina in Chile; Bogota and Medellin in Colombia) are less fuel intensive overall and tend to rely relatively heavily on electricity; firms in other locations favor diesel and fuel oil. To the extent that these patterns reflect price variation across plant sizes and time, they provide further support for the conjecture that fiscal policies can significantly influence the level and mixture of energy usage among manufacturers. APPENDIX. DATA PREPARATION The variables described below are the building blocks for our analysis. They are used to define and calculate the various quantity and price indexes and expenditure shares. (Details of missing data and outlier treatment are available from the authors.) Chile The panel of Chilean plants covers virtually all manufacturing establishments with at least 10 workers during 1979-85. For each plant and year, it includes data on electricity generated (volume), purchased (volume and value), and sold (volume and value), as well as on purchases of coal (volume and value), coke (volume and value), diesel (volume and value), fuel oil (volume and value), Moss and Tybout 71 fuelwood (volume and value), gasoline (volume and value), liquid gas (volume and value), paraffin (volume and value), piped gas (volume and value), stone coals (volume and value), and other fuels (value). Colombia The Colombian data span 1977-88, and, like the Chilean data, provide nearly comprehensive coverage of plants with more than 10 workers. However, these data do not disaggregate nonelectric energy use by category. The variables we observe at the plant level are electricity bought (volume and value), generated (volume), and sold (volume and value) as well as fuels and lubricants purchased (value). Electricity is treated exactly as in the Chilean case: fuels and lubricants are deflated using fuel price deflators specific to each three-digit industry. The in- dexes were constructed by obtaining series from Colombia at the national level on coal, coke, diesel, and petroleum prices, then using the subsector-specific (three-digit industry) shares of these fuels from Chile as weights to construct Laspeyres price indexes, industry by industry. The Variables of Interest The symbol Qit denotes the physical volume of energy sourcej consumed by plant i in year t, Vij, denotes the value expenditure by plant i on energy sourcej in year t, and Pij, = Vij,/ Qjt denotes the unit price for energy source j at plant i in year t. Quantity indexes. Real expenditure series were constructed using the unit price series. In turn, the real expenditure series were used to form Laspeyres quantity indexes for total fuel consumption at the plant, industry, or manufac- turing level. For example, given J fuel types, our plant-level quantity index of total fuel use is E PIJ,80 Qiit (A-1) LQit j E Pij,80 Qij,so j=1 Similarly, at the subsector level, our quantity index is n ] (A-2)~~~~~~~~ E E Pij,80 Qij, (A-2) LQ.t n *iZ ~ E E Pi;,8o Qi;,8o i=1 j=1 72 THE WORLD BANK ECONOMIC REVIEW, VOL. 8, NO. I Price indexes. Aggregating across fuels, we constructed plant-level Laspeyres energy price indexes: IPiit Qq,s80 (A-3) LPi, = E P,y,80 Qij,80 j=1 Similarly, aggregating across fuels and plants, we arrived at subsectorwide en- ergy price indexes: n J (A-4) LP i= j=1 E >3 Pij,80 Qij,80 i=1 j=1 Analogously, for the kth fuel alone, we constructed subsector-level Laspeyres price indexes: n E- Pikt Qik,80 n= (A-S) LMt -= n- E Pik,80 Qik,80 Expenditure shares. Finally, to document changes in fuel intensities, expendi- tures on each fuel were expressed as a fraction of several alternative aggregates: total fuel expenditures, total variable cost, and the gross value of output. Specif- ically, at the plant level, the share of energy source k in total energy use was constructed as (A-6) SEKi= Pik,80 Qik,t jz Pij,80 Qij,t When aggregated to the subsector level, this measure of fuel intensity became n Z Pik,80 Qik,t (A-7) SEK - n J E E3 Pij,80 Qij,t i=1 j=1 When energy use was expressed in relation to real variable costs (VCi,) or real gross output (Yi,), we obtained the plant-level measures of energy intensity SVCiZ, Pik,80 Qik,t/ VCi, and SQit = Pik,80 Qik,t/ Yj, respectively, where both Moss and Tybout 73 VC and Y are expressed in 1980 prices. At the industry level, the numerators and denominators of these measures were summed over plants to yield n n E Pik,80 Qik,t E rik,80 Qik,t (A-8) SVC! = and SQk = E vcit E Yit i=1 i=1 When k superscripts are missing, the numerators of these measures have been summed across all J energy sources. For both countries, output was deflated using official three-digit gross output deflators. 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