Part V

Appendix

The description of the model used for this analysisis broken into three sections: methodology, datasources, and strengths and weaknesses. The appen-dix assumes that the reader has a rudimentaryknowledge of input-output analysis and mathemati-cal modeling. If additional background material isneeded, see one of the sources cited in box G.

METHODOLOGYThe model developed for this analysis consists of

a series of seven input-output tables that have beenmoditled from a strict dollar basis to a mixed dollarand quantity format in which energy use is measuredin British thermal units (Btu). This mixed format,which combines dollar values of a product withquantity values, is called a hybrid model and isdiscussed further in the next two sections.

Input-Output Analysis

Simplifying slightly, an input-output table con-sists of three parts: the Use table, the Make table, andfinal demand. The Use table is the heart of theanalysis. Each column of this matrix shows thedollar value of inputs used in a particular year togenerate that industry’s output. Each row of theMake matrix shows what commodities each industrymakes in a particular year (i.e., both primary andsecondary products). For example, the chemicalindustry makes chemicals as well as drugs, plastics,paints, and rubber.148

By normalizing the Make table by commodityoutput and multiplying it against the Use table whichhas been normalized by industry output, a matrix iscreated where each element in a column shows thevalue of the input commodity needed to make adollar’s worth of the commodity being produced(output). This matrix, A, is referred to as the directrequirements table. Basically, the matrix, A, repre-sents a series of linear equations that can be solvedsimultaneously. The solution, shown below, resultsin an inverse matrix, (l-A)-l, called the total require-ments table or the Leontief inverse. Each columnrepresents the production recipe for a particularproduct and each cell of a column in this matrixrepresents the direct and indirect inputs of a particu-lar commodity required to satisfy a dollar's worth offinal demand for a product. When the total require-ments matrix is multiplied by the final demand foreach product, the result is a vector consisting of the

ij

G

MuQu’MA

Yx

Hybrid Input-Output Energy Analysis

The construction of a hybrid input-output energymodel involves reorganizing the input-output com-modities and industries so that the first five rows andthe first five columns are energy commodities andenergy industries.149 The dollar flows of energyinputs in the Use table are replaced with the quantity(Btu) of energy required. Similarly, the energyportion of final demand (the first five rows) are alsoconverted to Btu. Instead of representing the dollaramount of an input needed to generate a dollarsworth of output, the hybrid direct requirements or“A” table represents four different relationships:

Quadrant 1: Btu of energy input needed per Btu ofenergy sector output.

Quadrant 2: Btu of energy input needed per dollar ofnonenergy sector output.

–53–

54 Energy Use and the U.S. Economy

Table 13-Primary Energy Conversion Ratios

1963 1967 1972 1977 1980 1982 1985

Coal mining . . . . . . . . . . . . . . . . . . . . . . . . 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 I.0000Crude oil & gas . . . . . . . . . . . . . . . . . . . . . 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000Refined petroleum . . . . . . . . . . . . . . . . . . 0.0536 0.0540 0.0628 0.0493 0.0469 0.0624 0.0635Primarv electricity . . . . . . . . . . . . . . . . . . . 0.5436 0.5748 0.5732 0.6854 0.7168 0.8211 0.8401Utility gas . . . . .’... . . . . . . . . . . . . . . . . . 0.0543 0.0644 0.0696 0.1072 0.1032 0.1059 0.1068SOURCE: S. Casler, “Energy Flows Through the U.S. Economy, 1980,1982, and 1985,” contractor report prepared for the Office of Technology Assessment,

December 1989.

Quadrant 3: dollars of nonenergy input needed perBtu of energy sector output. Energy sectors Nonenergy sectors

I I 1Quadrant 4: dollars of nonenergy inputs needed perdollar of nonenergy sector output.

Energy inputs

Quadrants 1 and 2 correspond to the energy portionof the production recipe while quadrants 3 and 4represent the nonenergy portion.

Nonenergy inputsBy multiplying the inverted hybrid energy input-

output table (Ae) by the hybrid final demand (Ye), a

Btu/Btu

1

$/Btu

3

Btu/$

2

$/$

4

Part V--Appendix ● 55

column of outputs for each commodity is generated.The output for the first five rows represents theenergy output by type required to satisfy the leveland mix of demand specified. In forming a measureof aggregate energy use, it is necessary to eliminatethe double counting of energy that would occur ifboth the coal used to make electricity and theelectricity that is generated from the coal werecounted. To eliminate this double counting, primaryconversion ratios (table 13) are applied to the outputof each energy type. As can be seen, by their naturecrude oil and coal are already in a primary state, thusthe conversion ratios are ones. Primary electricityhas a relatively high conversion ratio becausenuclear and hydroelectric power areme converted toBtu based on their fossil fossil fuel equivalent.150 Afterthese primary energy conversion ratios are applied,the sum of energy across energy types represents theenergy produced in the United States. To calculatethe consumption of energy, the sum of the absolutelevel of energy imports minus the primary energyassociated with energy exports are added to theproduction total.fi

Table 14 shows that the differences betweenenergy consumption estimates produced by the OTAmodel and those published by the Department ofEnergy are relatively small, except for 1963 and1967 where the differences exceed 3 percent. Thedifferences that do exist can probably be attributedto revisions made in the raw energy use numbers andthe primary energy conversion ratios that were notsubsequently made in the National Energy Accountsdata.

Decomposing the Change and theInteractive Factor

The calculation of the change in energy use due todifferent economic factors was achieved by using1985 as a base year and systematically varying onefactor over time while holding all other factorsconstant in their 1985 form. For example, tocalculate the change in energy use due to shifts inspending, the production recipe was held constant inits 1985 form, and final demand for each year (1963,1967, 1972, 1977, 1980, 1982, and 1985) was

applied. The change in energy use horn 1972 to 1985due to final demand (spending) was calculated bysubtracting the energy output associated with 1972demand (using the 1985 production recipe) horn the

Table 14-Comparison of OTA EnergyConsumption Estimates With Estimates Published

by the Department of Energy (DOE)(quadrillion Btu)

PercentOTA DOE difference

1963 . . . . . . 50.08 48.32 3.61967 . . . . . . 60.66 57.57 5.41972 . . . . . . 73.03 71.26 2.51977 . . . . . . 78.60 76.29 3.01980 . . . . . . 77.19 75.96 1.61982 . . . . . . 72.22 70.84 1.91985 . . . . . . 74.94 73.94 1.4SOURCE: Office of Technology Assessment Energy Model; and U.S.

Department of Energy, Energy Information Agency, AnnualEnergy Review, May 19SS, table 4.

output generated using 1985 demand (using the1985 production recipe). By doing this to everycomponent and subcomponent, the change in energyuse can be attributed to different factors. In somecases, this decomposition of the change was not dueto a single factor, but was instead due to two or morefactors changing simultaneously causing an interac-tion which affected energy use.

Unlike a residual in regression analysis, theinteractive factor is not unexplained variance; rather,it is accurately allocated to an identifiable, butdifficult to interpret factor that is the simultaneouschange of two (or more) variables. For example, aninteractive change may have occurred in the casewhere the substitution of plastics for steel in anautomobile to decrease weight caused both a changein the production recipe, and a change in the mixtureof spending as more fuel-efficient autos required lessgas and thus realigned the mix of products aconsumer bought.

Interactive factors are common to all types ofshift-share analyses, although many of those re-ported are of a smaller magnitude than the onecalculated in this study.151 The interaction term thatexists when the change in the product of two or morevariables is decomposed into individual effects ispresent because data are measured over discreteversus infinitesimal time changes. The use ofinput-output analysis precludes an annual timeseries, instead breaks between data points tend to be2 to 5 years in length. In particular, the 5-year spanin data from 1972 to 1977—a period of tremendousturmoil in terms of energy use because of the first oilshock—was the period that generated nearly two-

56 ● Energy Use and the U.S. Economy

thirds of the total interactive effect registeredbetween 1972 and 1985. Not surprisingly, over 85percent of the interaction between spending andproduction recipe was in crude oil& gas.

A in energy A in spending A in production interaction ofuse recipe production recipe

and spending

derived for each of the decompositions such asseparating the energy effect from the nonenergyeffect in changes in the production recipe:

where Pe is energy portion of the direct requirementstable and Pn is the nonenergy portion of the directrequirements table which are then combined and ashontief’s “A” and converted to a total require-ments matrix.The decomposition of the change in spending intoincreases in the level of spending and changes in the

where S1 is the level of spending and S~ is the mixof spending.

The size of the interaction effect is a function ofthe magnitude of the effect attributed to identifiedvariables. Since both spending and the productionrecipe were found to have a large impact on energyuse, it is not surprising that the interaction of thesetwo factors was also large. Because of the longertime period being analyzed, the wider range ofsectors being included, and the unavailability of a

1985 input-output table, the fact that the interactivefactor is larger than those reported in other studies isto be expected.152

In models of structural decomposition, treatmentof this effect varies and no consistent set of standardsseems to apply in dealing with it. For example, asseen in the recent literature, Wolff153 ignores theinteraction term, Feldman et al.154 and Boyd et al.155

allocate it equally among the other sources ofchange, while Casler and Hamon,156 Roop,157 andthe Department of Energy 158 treat it separately andreport its magnitude. Given that the interactive termis a unique factor that affects energy use, we decidedto keep it as a separate variable and report its value.

Calculation of Energy Intensities

The primary energy intensities presented in table9 were calculated using gross output or shipments,not value-added, in the denominator of the ratio.Neither measure of output is free of methodologicalproblems, but gross output is more appropriate giventhe analysis being undertaken.

Gross output data reflect the value of the wholeproduct, which consists of components made byother businesses (suppliers) and the value added tothose components by the producing business. Value--added is just the additional value supplied by thefirm in its conversion of raw inputs into a finaloutput. Businesses can boost their gross outputs i m p l y b y ‘ ‘out-sourcing’ ‘ more intermediateparts-in some cases the whole product can beout-sourced. 159 When aggregated across sectors orthe whole economy, shipments data reflect a lot ofdouble counting since both the supplying firm andthe buying firm count the same product as output.l60

The double counting makes calculating shares ofoutput by industry and a shifting mix of the economyhorn gross output data problematic.

Unlike gross output, constant dollar value-addedby industry161 is a residual of a “double-deflation”process where deflated intermediate inputs aresubtracted horn deflated gross output.

162 This proc--ess requires extensive intermediate input data anddeflators for each industry, including services wheresuch data is limited. It also necessitates an adjust-ment for imported intermediate inputs whose pricechanges might not be accurately reflected in defla-tors based on domestic products, such as theProducer’s Price Index (PPI. Depending on howthese adjustments are made, significant changes in

Part V--Appendix ● 57

Table 15-OTA Energy Model Data Sources and Coverage

Income and Product Accounts).SOURCE: Office of Technology Assessment, 1990.

industry- and sector-level constant dollar value--added can occur.lG3 In an effort to address some ofthese issues, the Bureau of Economic Analysis isrevising its constant dollar value-added by industryseries,l a making it currently unavailable.lG5

DATA SOURCESThe construction of this model required four key

data components for each year: input-output tables,final demand, energy use, and deflators.

Input-Output

As described in box G, input-output (I-O) tablesprovided a snapshot of the inputs required to makethe economy’s outputs in a particular year. Becauseenergy is primarily an intermediate rather than afinal product, I-O tables are particularly well-suitedfor this type of analysis. I-O tables are unique in thatthey incorporate data from nearly every Federalstatistical agency and numerous private sources,allowing a comprehensive, consistent treatment ofthe whole economy. Nevertheless, the data intensivenature of I-O tables means that “benchmark” I-Otibles, which incorporate quinqennial economiccensus data such as the Census of Manufactures areonly issued once every 5 years,ti and there is adelay of 6 to 8 years between the year the data wascollected and its release as input-output data.lfi Toalleviate this problem, the Department of Com-merce’s Bureau of Economic Analysis (BEA) issuesannual tables that are updates of the more rigorousbenchmark tables.lG7 In addition to updating theinput-output relationships between commoditiesand industries for changes in the prices that haveoccurred since the release of the benchmark table,the annual updating process also incorporates new

58 ● Energy Use and the U.S. Economy

1.4 percent above that reported by the Department ofEnergy.1 73

This technique of just updating the energy portionof the input-output tible has been shown by otherresearchers to be more accurate than not making themodification.

174 Accurate results are more likely ifthe updating occurs over a short period of time, suchas the 3-year span between 1982 and 1985,175

because changes in input-output coefficients occurgradually for many sectors of the economy.176

Final Demand

Final demand was available for every year exceptfor 1985 horn the corresponding input-output tables.Since a 1985 I-O table does not currently exist, 1985final demand by I-O commodities was estimated byconverting demand as reported in the NationalIncome and Product Accounts (NIPA)177 into de-mand by I-O commodity. The conversion of demandhorn NIPA categories into demand by I-O commod-ities is accomplished through use of ‘bridge’ tablesproduced by BEA. In the case of households(personal consumer expenditures or PCE)178 andbusiness investment in personal durable equipment(PDE),179 the bridges are published along with the1977 input-output table.180 For the remaining cate-gories of domestic demand—government181 andbusiness investment in structures182-unpublishedversions of these bridge tables were obtained fromBEA. xxxiv

Import and export data for 1985 were obtained inunpublished form from the Bureau of Labor Statis-tics’ Office of Economic Growth and EmploymentProjections in their 222 sectoring scheme and wereconverted to the BEA’s input-output classificationsusing a BLS sectoring plan.183

Adjusting for Changes in Prices

The analysis of change in the economy over timerequires that each year’s final demand and associ-ated input-output tables be based in the same set ofprices-allowing a consistent comparison overtime.This process of establishing a constant set of pricescorrects not only for the effects of inflation on aproduct’s price, but also for quality changes thathave occurred in the product over time, such as theaddition of a turbocharger to an engine. The

common name for this process is deflation becausethe current price of a product is deflated to someprice in the past, although the reverse also occurs.Currently, 1982 is the most up-to-date base year.This issue was discussed in box F and in theupcoming section of strengths and weaknesses.

The deflators used in this analysis are based in1982 and were obtained from the Bureau of LaborStatistics “Historical Input-Output Time SeriesData Base,”184 and were aggregated to the BEAsectoring scheme using current dollar weights ofoutput in accordance with the BLS sectoring planand unpublished worksheets from BLS.185 A defla-tor was derived for each nonenergy commodity.Since energy commodities are valued in quantities(Btu), no deflation was necessary.

Comparisons to Gross National Product(GNP)

Since the sum of all components of final demandis GNP, a preliminary check of the bridging processfrom NIPA to I-O commodities and the deflators isto compare the deflated total of GNP as derivedthrough this process with the constant dollar GNPfigures published by BEA in the Survey of CurrentBusiness. Table 16 shows that the difference be-tween the two series averages less than 1 percent.This difference can probably be attributed to revi-sions in the National Income and Product Accountsthat are not incorporated in the input-output tablesand the use of different deflators. Comparisons ofconstant dollar final demand at the commodity levelcan not be made because BEA does not produce aconstant dollar final demand series in commoditycategories.

Measures of Economic Activity

No one statistic can adequately reflect economicgrowth or changes in a country’s standard of living.This is especially true as an economy develops,incomes rise, and greater concern is directed towardsthe costs associated with economic activity such aspollution, depletion of natural resources, and trafficcongestion that are typically not accounted for ineconomic indicators like GNP.186 Nevertheless,GNP was never intended to be a proxy for economicdevelopment; rather, it is an estimate of “. . . the

Part V--Appendix ● 59

Table I6--Comparison of OTA GNP Estimates WithEstimates Published by the Bureau of Economic

Analysis (millions of constant 1982 dollars)

1963 . . . . . . 1883.9 1873.3 0.61967 . . . . . . 2286.9 2271.4 0.71972 . . . . . . 2638.9 2608.5 1.21977 . . . . . . 2946.3 2958.6 -0.41980 . . . . . . 3140.2 3187.1 -1.51982 . . . . . . 3190.7 3166.0 0.81985 . . . . . . 3622.6 3618.7 0.1

Average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.2

A component of GNP, Gross Domestic Product(GDP) is used throughout this report. The differencebetween GNP and GDP is the net return on capitallocated abroad but owned by U.S. residents minusthe income from capital owned by foreigners butlocated in the United States-a category called“Rest of World” (RoW).188 The category does notreflect actual output, but rather the returns (wages,profits, interest) associated with that output. Forexample, the dividends received by a U.S. investorin a European company and the interest paid toJapanese holders of U.S. Treasury bonds would bothbe counted in RoW.

Historically, the RoW category has been a smallaccounting adjustment made to the national ac-counts. Over the time period being analyzed (1963to 1985), the RoW category grew in size and becameerratic, hitting a high of 1.74 percent of GNP in 1980and a low of 1.00 percent in 1985. In 1980, thesepayments were equal to 86 percent of the contribu-tion made to GNP by the farming sector. Becausethese accounts do not represent tangible output of agood or service, they do not affect energy use; butbecause of their volatility, their inclusion does affect

60 ● Energy Use and the U.S. Economy

Table 17—Listing of Broad Sectors and Individual Industries/Commodities in the OTA Energy Model

Energy sector 451 Coal mining 462 Crude petroleum and natural gas 473 Petroleum refining and related industries 484 Electric utilities 495 Gas utilities 50

Natural resources sector 51- .6789

101112131415

Livestock and livestock productsOther agricultural productsForestry and fishery productsAgricultural, forestry, and fishery servicesIron and ferroalloy ores miningNonferrous metal ores mining, except copperStone and day mining and quarryingChemical and fertilizer mineral miningNew constructionMaintenance and repair construction

52535455565758

596162

Manufacturing 631617181920212223242526272829303132333435363738394041424344

7677787980818283848586

8788

Communications, except radio and televisionRadio and TV broadcastingWater and sanitary servicesWholesale and retail tradeFinance and insuranceReal estate and rentalHotels: personal and repair services (excluding auto)Business servicesAutomobile repair and servicesAmusementsHealth, education, social services, and nonprofit

organizationsFederal Government enterprisesState and local government enterprises

SOURCE: Office of Technology Assessment, 1990.

double counting of energy consumed would occur. STRENGTHS AND WEAKNESSESThe energy associated with both the sale and captiveuse of wood are not included. Lastly, purchases of OF THE OTA ENERGY MODELenergy products that are used as feedstocks, such as Economic models like the one used for thisthe petrochemical industry’s use of petroleum, are analysis are simulations of reality and thus sufferincluded in the NEA energy flows. from being unable to completely reflect all facets of

Part V--Appendi.x ● 61

a real economy. The power of models lies in the factthat “what if” questions can be asked that revealknowledge that would be difficult, dangerous, orimpossible to obtain from the real economy themodel emulates. In this sense, all models have theirstrengths and weaknesses and the results obtainedshould be interpreted with attention to these traits.The following section outlines some of the characterstrengths and flaws of the OTA energy model.

Strengths

There are two major strengths of the model: 1) itis based in input-output data and analysis, and 2) thehybrid nature of the model.

Input-output

Input-output tables reflect the state of the econ-omy at a particular time-a snapshot. The strengthof this analytical technique is that it is rooted in realdata that is the bedrock of the national accountingsystem used for estimating the performance of theeconomy. No economic activity that occurs in theformal marketplace escapes this accounting. Be-cause I-O plays this critical role in the U.S. statisticalsystem, the data are unusually complete and inter-nally consistent, and cover every sector of theeconomy.194 AS a result, the OTA energy modelencompasses the whole economy, not just individualor aggregated sectors such as manufacturing. As canbe seen from the analysis in part III, the servicesector is an important component of the U.S. energyequation. These features make I-O analysis aninvaluable tool in examining how the structure of theeconomy has evolved.195

In addition to its data intensiveness, anotherstrength of I-O analysis is the ability to capture theinterrelationships and linkages that exist betweensectors of the economy. By being able to trace thedirect and indirect links, input-output lets theresearcher calculate the complete energy required tomake a product from raw material all the way to theretail outlet. These interconnections allow not onlya tracing of the direct energy associated with someeconomic activity, but also the indirect energyembodied in specific goods and services. Seeminglylow energy-intensity products such as water &sewage treatment use a lot of energy when the directand indirect effects are included. As an increasingnumber of products are part of complex productionsystems that extend beyond U.S. borders, this ability

to calculate the energy embodied in a product isimportant and is unique to I-O models.

The construction of input-output tables allows aseparation of changes in energy use due to what isbeing purchased (spending) and how that productwas produced (production recipe), a feature that isdistinctive to input-output analysis. Because spend-ing (final demand) is an identifiable component ofinput-output tables, it allows a researcher the abilityto focus on different aspects of demand, analyzinghow different products or sources (households v.government) affect energy use. Similarly, experi-ments such as how much of the change in energy useassociated with the production recipe comes fromenergy inputs and how much horn nonenergy inputscan be run. This level of detail and the ability toseparate direct energy use horn indirect energy useis a valuable feature associated with input-outputanalysis.

Lastly, the input-output method of analyzingchange in energy use does not force the researcher toconstantly view energy use as a ratio where it isalways entangled with some other variable such asvalue-added or output.196 Thus, actual quantities ofenergy use are reported as opposed to quantitiescontingent on some economic variable.

Construction of a Hybrid I-O Table

Modifying an I-O table so that energy is expressedin quantities such as Btu instead of dollars (a hybridmodel) creates numerous methodological advan-tages. First, valuing a good in Btu rather than dollarseliminates the need to adjust for changing pricesover time, eliminating a possible source of error.Second, through the mathematics of input-output,energy intensities (Btu per dollar of output) are abyproduct of calculating energy consumption.Third, the hybrid method avoids the need to convertdollar-based energy output into energy quantities,such as Btu, using a simple conversion ratio (Btu perdollar of energy output or implicit price). Since theprice paid for different types of energy by differentindustries varies significantly, using an averageprice for all industries can introduce a significantdistortion (see figure 19).197 Thus, the OTA modelimplicitly uses a unique price for each fuel type forevery industry.

Lastly, only through using a hybrid I-Omodel canthe production recipe be divided into its energy andnonenergy portions. Use of conversion ratios like

62 ● Energy Use and the U.S. Economy

Figure 19-implicit Price Paid per Btu ofRefined Petroleum

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

‘ I n d u s t r y p r i c e ‘ A v e r a g e p r i c e

SOURCE: U.S. Department of Energy, Energy Information Agency,Annual/Energy Review, 1988 (DOE/ElA41364(66)), May1969, table 27, p. 65.

those described above would not capture the directand indirect changes associated with the inversion ofthe matrix.198

Limitations

Data

While I-O accounts have a number of advantages,they do suffer from data and methodological limitat-ions. Because the model is based on observed data,there tends to be a long lag time between thecollection of data and the availability of I-O tables.A‘ ‘benchmark’ table for 537 business categories ispublished following publication of the industrialcensuses, which are conducted every 5 years. Thebenchmark table for the 1977 I-O tables becameavailable only in 1984. A 1982 benchmark table hasnot yet been released. Asaresult,a‘‘revision’ of the1977 benchmark, updated to 1982 and aggregated to85 business categories, is the latest I-O table used inthis analysis. Although 85 industries provide suffi-cient detail for the a broad study of the economy,more detail is necessary for pinpointing changes andavoiding biases associated with aggregation. Forexample, what appears to be a change in the steelindustry’s (SIC 331) production recipe resulting inless energy use per unit of output might have little todo with technology and instead be attributable to ashift in production from pipes and tubes (SIC 3317)to wire and nails (SIC 3315).

The sporadic nature of benchmark I-O accountsmeans that a continuous time series is impractical toassemble. A weakness of this analysis is that it relies

on six I-O tables to explain changes in energy usethat occurred over a 22-year period-temporalpeculiarities can skew the findings. This limitationmeans that turning points, such as the first year thatthe energy intensity began to decline, 1971, are insome cases missed. The lack of a continuous seriesrestricts any connection between business cyclesand energy use to causal observances because annualtrends cannot be plotted.

Only four of these six I-O tables (1%3, 1967,1972, and 1977) were the more detailed and accuratebenchmark tables; the 1980 and 1982 tables wereannual updates of the 1977 benchmark. Given thesevere economic recession of 1982, its use as adatapoint, especially an endpoint, is questionable.This problem is reduced through the updating of theenergy and final demand components to 1985.Although no set of endpoints are typical, 1972 and1985 are at relatively the same point in the economiccycle.

Other than the input-output data, the other datasources employed, deflators and the NEA, also havetheir share of weaknesses. By and large, deflatorsused in this analysis are of good quality: a uniquedeflator is used for each industry and the same seriescan be used over the whole time period beingstudied. Nevertheless, the significance of deflatorsas a source of error and distortion is frequentlyoverlooked. The main weakness associated withdeflators is that it is very difficult to make qualityadjustments for service products where the output isinherently hard to measure and for products experi-encing rapid technological change such as comput-ers.lw In particular, the accuracy of the computerdeflator has been debated.2m Whether corrector not,its effect on economic analysis is substantial, andadditional work needs to be done to test thesensitivity of the findings presented in this report tochanges in the deflators used.

NEA data are the only source for consistentenergy use data by industry overtime. Nevertheless,the accounts suffer horn a lag of roughly 4 years: the1985 data were released in 1989. The lag associatedwith NEA and the I-O tables limits the analysis to1985, leaving a gap in trying to explain the morerecent, 1985 to 1988, increase in energy use. Alimiting assumption associated with the NEA is thatthe economic value of all types of energy areequal-a Btu is a Btu regardless of the type. Thisconversion of energy type into a common unit, Btu,

Part V--Appendix ● 63

conceals the fact that different forms of energy haveunique properties and are not equivalent replace-ments for each other.201 Some analysts argue thatwhen the quality of a particular energy type is takeninto account, the decline in the energy intensity inthe 1970s and early 1980s is much smaller.m

Methodology

The most important assumption made in I-Oanalysis is that of “linear,” or fixed, economies ofscale. Calculations that estimate the energy directlyand directly associated with a product assume thatthe same mix of inputs, the process employed, andthe relative prices of goods and services are the samefor making one product as they are for making10,000. Many of the calculations used in thisanalysis such as the energy associated with manu-factured v. service products, the primary energyintensities associated with a product, and the energyassociated with household v. government ex-penditures rely on this assumption.

Another methodological assumption made in thisanalysis was that all imported products could bemade in the United States and that the U.S.production recipe for making these imported prod-ucts is an exact proxy of the recipe used overseas.Some products like coffee or chrome, cannot bemade in this country. Other products, like cars, thatdo have a domestic counterpart are made muchdifferently overseas than in the United States. Thus,estimates of the energy embedded in importedproducts are rough approximations.

The production recipe only includes nondurableinputs, such as steel and rubber, that are completelyused up in the production of output. Inputs of a moredurable nature that depreciate over time, such asmachine tools or the actual physical plant (capitalgoods), are not included in the production recipe, butare instead thought of as business investment and areincluded in final demand.203 This assumption resultsin an underestimate of the indirect energy associatedwith a product if the complete demand vector,including all of business investment, is not part ofthe calculation. For example, the indirect energyassociated with making a car would not include theenergy required to make the stamping presses or theconveyor belt. (Nevertheless, the nondurable inputof electricity needed to drive this equipment wouldbe included.) This assumption would affect esti-mates of the energy embodied in manufacturedproducts, the individual energy intensities, and the

energy associated with household expenditures.This failure to include capital in the productionrecipe results in an underestimate of the energyembodied in products that ranges from 2 to 17percent depending on the product. The unweighedaverage underestimate is estimated to be 9 per-cent. 204

Lastly, the OTA model was constructed primarilyto address the question of how much of the changein energy use was due to efficiency gains and howmuch was due to a changing mix in the industrialcomposition of output. To make this comparison, itis important that the value of output be converted toa constant set of prices since a million dollars’ worthof output in 1963 had a much different value that amillion dollars’ worth of output in 1985. Thisrequires that price, an important factor in energy use,beheld constant by creating a constant-dollar model.In this sense, the model can isolate the change dueto efficiency but not why that efficiency changeoccurred. Examples of likely causes of the changeare frequently cited in the analysis, but theirinclusion is anecdotal, not conclusive.

ENDNOTES FOR PART V

64 ● Energy Use and the U.S. Economy

Part V--Appendix .65