Appendix C

Quality of the Data

SYˆ 
 M

n

i
1

y

2

i (Wi

)(Wi	1) ,

Energy Information Administration, Manufacturing Energy Consumption Survey: Methodological Report 1985. Although this report describes 44

data quality in the 1985 MECS, much of the discussion still holds for the 1994 MECS.

Energy Information Administration/Manufacturing Consumption of Energy 1994 435

(1)

Appendix C

Quality of the Data

Introduction

All data collection activities and the population estimates produced from them are subject to a variety of errors. These errors

may be broadly classified under two general types, sampling and nonsampling errors. 

Sampling errors are defined as the variability in a survey estimator that arises because data used to estimate population values

are collected from a sample of units rather than the complete enumeration of the entire population. Each possible sample

produces different estimates of population values, depending on the set of respondents that are selected. Consider, for

example, a sample of two units from a population comprised of three units. In this example, there exist three possible sample

sets of respondents, each of which produces a different estimate of the population total. The difference between the estimate

calculated from one of the samples and the population total is referred to as the sampling error.

Nonsampling errors, on the other hand, occur in any data collection activity, whether a sample survey or a complete

enumeration of the population. Nonsampling errors may be associated with any part of a survey process except sampling

and can include both random and systematic (biasing) errors. Commonly recognized sources of nonsampling error include

undercoverage, random and systematic response errors, unit and item nonresponse, data processing errors, and tabulation

errors. This appendix describes the effect of both sampling and nonsampling errors on data from the MECS. In addition,

the measure for sampling errors of the population estimates are given. More details are presented in the methodological

report for the MECS. 44

Sampling Errors

The estimated values appearing in this report were developed from a sample of the universe of manufacturing establishments

and, as a result, will differ from true population values that would be obtained from a complete enumeration of the

manufacturing universe. This difference occurs because the MECS sample is only one of a very large number of samples

that could have been selected under the same sampling specifications. Each possible sample would yield its own unique

estimates of the true population values, with the differences attributable to the particular set of establishments selected into

each sample. 

 

One measure of variability due to sampling is the square root of the average of the squared differences between the estimates

that would be produced by all possible samples and the mean value of those estimates. This type of measure is commonly

known as sampling error. Estimates of the magnitude of these sampling errors based on data from a single sample are

provided by a statistic known as the standard error of an estimate. There are two different types of statistics presented in

this MECS: consumption estimates and population estimates. Each of these types has a different method of computing the

standard error of the estimate. 

Standard Errors of Consumption Statistics

Standard errors for MECS consumption estimates are directly computed from the reported data by using the formula: 

where Î = 
(yW) is the MECS survey estimator of weighted values reported by the i MECS sample establishment, y is i i i

th

the reported value of characteristic Y for the i MECS sample establishment, W is the final adjusted weight used to inflate th

i
SYˆ 
 M

n

i
1

wi(wi	1)(ui 	 p)2 ,

p 


M

n

i
1

uiwi

M

n

i
1

wi

.

RSEˆ i,j 
 Ri& Cj ,

Steven K. Thompson, Sampling, ( New York: John Wiley & Sons, Inc. 1992), pp. 69-70. 45

436 Energy Information Administration/Manufacturing Consumption of Energy 1994

(2)

(3)

(4)

the sample data to population estimates, and n is the number of MECS respondents. Justification for this formula is found

in the MECS methodological report. 

Standard Errors of Population Statistics

Standard errors for MECS population estimates are computed directly from the reported data by using the formula : 45

where Î = 
(yW ) is the MECS survey estimator, w is the adjusted sample weight for establishment I, n is the number of ii i

respondents in the sample, u is equal to 1 if it is in the domain of interest or 0 otherwise for the i MECS sample i

th

establishment, and p is the proportion of the population that has the characteristic of interest

 

Estimates of standard errors have been computed from the MECS sample data for the estimated aggregate values and

selected ratios appearing in this report. In the 1985 and 1988 MECS reports, measures of precision were presented

separately in the form of relative standard errors (RSE), that is, the standard error divided by the estimated value to which

it refers. In this report, as was the method used in the 1991 report, computed RSEs for Tables A1 through A44 are

approximated in a two-factor model and are embedded into each table as "row and column factors."

Sampling Error from Generalized Variances

The RSEs computed by using standard errors from equations (1) and (2) may be efficiently modeled by a generalized

variance procedure, which has been successfully used in several complex sample surveys conducted by EIA. This procedure

provides a comprehensive means of reporting generalized relative standard errors, which minimizes the publishing space

required to present standard errors and eases the reader's use of precision measures. Actual RSEs (by equations 1 and 2)

are used for statistical tests and confidence intervals presented in the text and for determining if a population estimate is too

imprecise to publish (RSE greater than 50 percent). 

The estimator used to approximate RSEs is based on a two-factor model. This model-based estimator is given as 

where R is the row factor for the i row and C is the column factor for the j column used to compute the generalized RSE i j

th th

of the sample estimate at the intersection of the i row and j column. Since RSEs calculated by this generalized variance th th

technique are approximate, confidence intervals and statistical tests of significance must also be regarded as approximate.

See Table C1 for a specific example of computing an approximate RSE. The three boxes that follow give examples of

calculating the approximate standard error, calculating the confidence range of the estimate, and measuring statistical

significance between any two estimates.
Energy Information Administration/Manufacturing Consumption of Energy 1994 437

 Calculating the Approximate Standard Error

RSE Column Factor Natural Gas)

= 0.6

RSE Row Factor (Chemicals and Allied Products)

= 6.4

Approximate RSE (Chemicals and Allied Products, Natural Gas)

= 6.4 # 0.6

= 3.8 percent

Approximate Standard Error (Chemicals and Allied Products, Natural Gas)

= (0.038) # (2,495 billion cubic feet) = 95 billion cubic feet.

Table C1. Calculation of Generalized Relative Standard Error (RSE)

Estimates Taken from Table A1: Total First Use of Energy for All Purposes, 1994: Part 2

(Estimates in Btu or Physical Units)

SIC Industry Group (trillion (million (1000 (1000 (billion (1000 short short (trillion (trillion

Code and Industry Btu) kWh) bbl) bbl) cu ft) bbl) tons) tons) Btu) Btu)

Total Electricity Fuel Oil Fuel Oil Gas LPG (1000 (1000 Other ments

Net Residual Distillate Natural Coal Breeze ShipCoke

and

g

RSE

Row

Factors

RSE Column Factors: 0.5 0.5 1.2 1.1 0.6 1.1 1.2 1.5 1.1 1.8

Total United States

 20 Food and Kindred Products . . . . . . . . . . . . . . 1,193 58,004 4,785 3,181 613 W 7,500 W 141 0 5.7

 2011 Meat Packing Plants . . . . . . . . . . . . . . . . . . 53 3,924 138 101 35 W W 0 2 0 16.0

 2033 Canned Fruits and Vegetables . . . . . . . . . . . 51 1,432 241 119 42 W W 0 * 0 15.7

 2037 Frozen Fruits and Vegetables . . . . . . . . . . . 42 2,901 204 62 27 76 0 0 2 0 18.0

 2046 Wet Corn Milling . . . . . . . . . . . . . . . . . . . . . . 173 5,662 169 10 67 9 3,556 6 5 0 20.4

 2051 Bread, Cake, and Related Products . . . . . . . 37 2,436 Q 131 26 89 0 0 * 0 21.8

 2061 Cane Sugar, Except Refining ............ 111 W W 220 2 W W 0 105 0 19.1

 2061 Cane Sugar Refining .................. 23 125 313 97 16 4 0 0 4 0 29.6

 2063 Beet Sugar . . . . . . . . . . . . . . . . . . . . . . . . . . 64 425 270 W 18 W 1,790 W * 0 3.5

 2075 Soybean Oil Mills . . . . . . . . . . . . . . . . . . . . . 57 1,845 147 W 30 W 682 0 3 0 1.4

 2082 Malt Beverages . . . . . . . . . . . . . . . . . . . . . . 51 2,311 W 21 21 W 789 0 * 0 8.1

 21 Tobacco Products . . . . . . . . . . . . . . . . . . . . . W 842 133 W W W W 0 W 0 25.5

 22 Textile Mill Products . . . . . . . . . . . . . . . . . . . . 310 32,614 2,680 1,274 113 999 1,821 0 14 0 13.1

 23 Apparel and Other Textile Products . . . . . . . . W 7,735 W 106 25 W W 0 W 0 20.9

 24 Lumber and Wood Products . . . . . . . . . . . . . 491 19,836 389 4,314 47 W W 0 341 0 9.3

 2421 Sawmills and Planing Mills, General ...... 201 6,556 W 1,206 11 W 0 0 160 0 14.3

 2436 Softwood Veneer and Plywood .......... 74 2,517 Q 251 3 168 0 0 61 0 15.3

 2493 Reconstituted Wood Products ........... 98 4,453 198 128 17 W W 0 60 0 14.2

 25 Furniture and Fixtures . . . . . . . . . . . . . . . . . . 69 6,590 60 154 23 211 115 0 18 0 13.4

 2511 Wood Furniture, Except Upholstered ...... 24 2,146 47 62 2 59 56 0 13 0 15.6

 26 Paper and Allied Products . . . . . . . . . . . . . . . 2,665 65,479 27,444 1,564 558 1,334 13,812 0 1,373 0 3.6

 2611 Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . W 2,190 3,583 178 21 W 328 0 W 0 13.5

 2621 Paper Mills . . . . . . . . . . . . . . . . . . . . . . . . . . 1,297 34,419 14,942 746 264 476 8,783 0 612 0 5.5

 2631 Paperboard Mills . . . . . . . . . . . . . . . . . . . . . 954 13,512 7,914 314 194 119 4,552 0 556 0 2.9

 27 Printing and Publishing . . . . . . . . . . . . . . . . . 112 17,409 W 264 46 W 0 0 2 0 10.6

 28 Chemicals and Allied Products . . . . . . . . . . . 5,328 152,482 17,457 2,324 2,495 435,926 13,239 449 442 166 6.4

 2812 Alkalies and Chlorine . . . . . . . . . . . . . . . . . . 135 13,424 W 53 52 W W 0 16 0 9.1

 2813 Industrial Gases . . . . . . . . . . . . . . . . . . . . . . 104 23,525 0 W 23 W 15 29 1 W 25.9

 2816 Inorganic Pigments .................... 51 2,393 159 W 21 W W W 11 0 10.1

 2819 Industrial Inorganic Chemicals, nec . . . . . . . 377 42,239 W 264 145 W W 352 28 * 14.6

 2821 Plastics Materials and Resins . . . . . . . . . . . 642 16,408 542 153 234 89,084 875 0 26 21 9.1

 2822 Synthetic Rubber . . . . . . . . . . . . . . . . . . . . . 102 2,276 W W 53 6,899 190 0 9 0 26.2

 2823 Cellulosic Manmade Fibers . . . . . . . . . . . . . 28 419 0 23 W W W 0 W 0 1.2

 2824 Organic Fibers, Noncellulosic . . . . . . . . . . . 116 7,093 1,435 88 39 W W 0 W W 8.3

 2861 Gum and Wood Chemicals ............. 28 211 * W 4 W 164 W 19 0 7.4

 2865 Cyclic Crudes and Intermediates . . . . . . . . . 206 4,789 W 204 98 W 152 0 35 0 19.5

 2869 Industrial Organic Chemicals, nec . . . . . . . . 2,369 18,786 795 319 1,037 282,371 W W 250 142 9.8

 2873 Nitrogenous Fertilizers . . . . . . . . . . . . . . . . . 622 3,817 0 30 589 4 0 0 2 0 14.0

 2874 Phosphatic Fertilizers . . . . . . . . . . . . . . . . . . 46 1,131 W W 13 3 W 0 25 0 6.4

 2895 Carbon Black ........................ 80 W 9,344 W 19 W 0 0 * 0 13.3

 Source: Energy Information Administration, 1994 Manufacturing Energy Consumption Survey.
log( RSEi,j) 
 m  ri  cj  ei,j ,

William G. Cochran and Gertrude M. Cox, Experimental Design (2nd ed.),( New York: John Wiley & Sons, Inc. 1957). 46

438 Energy Information Administration/Manufacturing Consumption of Energy 1994

Calculating the Confidence Range with Generalized RSEs

Steps to calculate the 95-percent confidence range (that range which

includes the true value of the estimate with 95 percent confidence): 

1. Multiply the standard error by 1.96;

2. Subtract the result of Step 1 from the given estimate to determine the

lower bound of the range;

3. Add the result of Step 1 to the given estimate to determine the upper

bound of the range.

Measuring Statistical Significance with Generalized RSEs

Steps to determine if the difference between any two estimates in this report are

statistically significant:

1. Calculate the standard error of each estimate;

2. Square the standard error of each estimate;

3. Add the two values from Step 2;

4. Take the square root of the value in Step 3;

5. Multiply the value in Step 4 by 1.96;

6. If the value in Step 5 is less than the difference between the estimates, the

difference between the estimates is statistically significant according to the

generalized RSEs.

(5)

Derivation of Row and Column Factors

Row and column factors are derived by an analysis of variance procedure with the table of RSEs. Although analysis of

variance is used to derive row and column effects from which row and column factors are computed, this generalized

variance procedure can not be considered an analysis of variance because the primary concern here is to determine model

fit rather than to analyze the effects of row and column variables on the RSEs. The two-way model is fit separately for each

log transformed RSE table and is consistent for every table in this report. Because of this consistency over all tables, the

model can be written in general format as

where m is the grand mean of log(RSE ) of a "balanced" table composed of I non-zero rows and J non-zero columns, r is i,j i

the effect of the i row, c is the effect of the j row, and e is the error term. Model parameters are fit by the standard th th

 j i,j

formulas for Ordinary Least Squares given by Cochran and Cox. For a given table of log(RSE) estimates, point estimators 46

of model parameters are given as
mˆ 





I

i
1




J

j
1

log( RSEi,j)

I & J 
 log( RSE&,&)

ˆr

i 





J

j
1

log(RSEi,j)

J 	 mˆ 
 log(RSEi,&) 	 mˆ

cˆj 





I

i
1

log(RSEi,j)

I 	 mˆ 
 log( RSE&,j) 	 m . ˆ

Ri 
 log	1 ( ˆm  ˆr

i

) 
 log	1 log(RSEi,& )

Cj 
 log	1 (ˆcj

) 
 log	1 log(RSE&,j) 	 log( RSE&,&) .

Ri 
 N

J

j
1

RSEi,j

1

J .

Pˆ 
 Yˆ

Xˆ ,

Ibid. 47

P.M. Gargiullo and M.L Goldberg, "A Modified Table Producing Language (TPL) for Producing Tables of Survey Statistics with Variances," 48

Proceedings of the Bureau of the Census Fifth Annual Research Conference (1989).

Energy Information Administration/Manufacturing Consumption of Energy 1994 439

(6)

(7)

(8)

(9)

The row and column factors are then computed by back-transforming the estimated model parameters; that is, by taking the

log of the model effects. This transformation yields -1

For ease of presentation, the row factor includes the grand mean, m. Because of this factoring, the row factor for the i row th

can alternately be expressed as the geometric mean of i row: th

And, column factors, C , for a given table have a geometric mean equal to 1.0. j

Since the MECS report presents a variety of energy-related estimates that are unique to certain industries, measures of the

precision of population estimates are sometimes equal to zero or are withheld from publication. When an RSE table contains

a zero or withheld RSE, the table of RSEs is considered for generalization purposes to be "unbalanced." When the condition

of an "unbalanced" table arises, substitute RSE estimates are inserted for these missing elements of the RSE table.

Substitution of missing RSEs elements is based on an iterative procedure developed by Cochran and Cox. A detailed 47

description of the automated procedure used to produce the row and column factors appearing in this report can be found

in Gargiullo and Goldberg. 48

Sampling Error of Proportions

The estimates in this report can be used to produce proportion statistics based on the ratio of various estimates reported in

the tables. Proportions are not given in the "Detailed Statistics Tables" but can be used to clarify the analysis. A proportion

is the statistic of the form 
RSE( ˆ P)ˆ  [RSEˆ 2

(Y) ˆ & (1	2&P)] ˆ  RSEˆ 2(X) . ˆ

Rˆ 
 Yˆ

Xˆ ,

RSE(R) ˆ 3 [ RSE 2 (Y) ˆ  RSE 2 (X) ˆ 	 2RELCOV(Y, ˆ X) ] , ˆ

Xˆ

YGX

Xˆ

M. Hansen, W. Hurwitz, and W. Madlow, “Sample and Survey Methods and Theory, Volume I” (New York: John Wiley & Sons, Inc., 1953), 49

p. 166.

440 Energy Information Administration/Manufacturing Consumption of Energy 1994

(10)

(11)

(12)

where Î and are survey-based estimates of aggregate parameters Y and X, respectively, and characteristic X

"encompasses" characteristic Y ( ). That is, each population element (and, thus, each sample case) that contributes

to Y also contributes to X, and the value of X for each element is greater than or equal to the value of Y. 

From standard errors given by equation (1) that are then generalized by equation (4), the approximate RSEs of aggregate

statistics can be used to produce an upper bound on the approximate errors for proportions. The straightforward additive

error formula shown in equation (1) gives rise to a similarly straightforward upper-bound approximation to the error of an

estimated proportion. The approximation can be expressed in terms of the generalized RSEs of the aggregate statistics

entering into the proportion as 

Justification for this formula is found in the MECS methodological report. 

Sampling Error of Average Values

Estimates in this report can be used to produce ratio statistics when the sample case characteristic (y) is not found in every

sample case (x) (i.e. Y is not a subset of X). The ratio is the statistic of the form

where Î and are survey-based estimates of average parameters Y and X, respectively, and characteristic X does not

"encompass" characteristic Y. That is, each population element (and, thus, each sample case) that contributes to Y may not

contribute to X.

From standard errors given by equation (1) that are then generalized by equation (4), the approximate RSEs of average

statistics can be used to produce an upper bound on the approximate errors for proportions. An approximation of the RSE

can be expressed by entering the average statistics in the ratio as49

where RELCOV(y, x) is assumed to be zero. Hence, the RSE(r) is overestimated. 

Nonsampling Errors and Bias

Nonsampling errors that affect MECS sample units can be divided into four major categories: 

 Operational errors, including editing, coding, and tabulation errors 

 Errors of measurement, including a lack of precision by the respondent, failure of the respondent to understand

instructions, etc.

 Errors of estimation, including the assumptions underlying the derived values

 Errors of nonobservation, including unit and item nonresponse and noncoverage. 

These errors are collectively referred to as nonsampling errors because they are not related to the sampling process and, thus,

would be equally likely to occur in a complete census or a sample survey. 
Energy Information Administration/Manufacturing Consumption of Energy 1994 441

Operational Errors

It is felt that operational errors are not a major concern for the estimates included in this report. The quality control

procedures that were employed for check-in, editing, coding, and keying the returned questionnaires (Appendix B) are

standard procedures that are in place at the Bureau of the Census and have withstood the test of time. Data tabulations were

independently verified by comparing marginal totals in tables generated from files supplied to EIA with corresponding totals

generated directly from microdata files maintained at the Census Bureau. 

Errors of Measurement

Errors of measurement are a concern in any data collection activity. The survey results for the MECS were subjected to

extensive computer editing procedures which were specifically designed to detect errors of measurement. Establishments

that failed these tests for response reasonableness and consistency were contacted again by analysts familiar with

manufacturing processes and energy use. Major errors, including omissions and misreporting by orders of magnitude, were

corrected. No editing procedure is capable of identifying all measurement errors, however, and some small errors will

remain. To the extent that these errors are due to random, rather than systematic, misjudgments, they are compensated in

the aggregate totals presented in this report, and it is believed that there are few large systematic biases that result from them.

Errors of Estimation

Errors of estimation of energy consumption could have resulted from the assumptions that underlie the derived values (see

Appendix B), and the estimates of the consumption of onsite- and offsite-produced fuels and raw material inputs could be

biased as a result of such errors. For example, the derivation logic makes the assumption that energy produced onsite at a

manufacturing establishment is considered first as a shipped product, second as a feedstock, and lastly as a fuel. If that logic

does not hold, derived estimate values will be misapportioned. However, considering the mechanisms required to produce

energy onsite, it is highly probable that this logic accurately represents manufacturers. These nonsampling errors, if present,

are relevant only for tables in this report that are based on derived values. Estimates based upon reported values would not

be subject to this potential source of bias. 

Errors of Nonobservation 

Finally, several potential sources of nonsampling error and bias result from errors of nonobservation. As described in

Appendix B, the 1994 MECS represents, in terms of sampling coverage, the mail frame of the 1994 ASM or 98 percent of

the manufacturing universe, which is consistent with the 1991 MECS.

Unit Nonresponse

Even though the MECS is a legislatively mandated survey and sampled establishments are given sufficient opportunity and

time to respond, nonresponse occurs in the MECS and is accounted for in a nonresponse adjustment of sampling weights

presented in Appendix B. Clearly, had these adjustments not been performed, the estimates produced from only the

responding establishments would not have been representative of the target universe for the MECS. Such estimates would

have been biased. Adjusting the sampling weights to reflect the target universe is an attempt to mitigate the potential effects

of such a bias.

Adjustment factors are calculated for each of the 72 published strata to account for the variation of nonresponse between

strata. Each stratum represents a relatively homogeneous subgrouping of establishments with respect to primary product

output and level of fuel consumption. 

Implicit in that procedure is the assumption that primary product output and level of fuel consumption are highly correlated

with energy expenditure patterns, so that the establishments within a stratum would also be homogeneous with respect to

the quantities, types, and shares of energy consumed as fuels and for nonfuel purposes. Also, the weight adjustment method

assumes that the relationship between survey variables of interest and the control variable used for constructing the adjusted
VAimp
(

SW94

SW92

) . (VA92) ,

442 Energy Information Administration/Manufacturing Consumption of Energy 1994

(13)

sample weight is the same for the population covered by MECS respondents within an adjustment stratum as it is for the rest

of the population within that stratum.

To the extent that the nonresponding establishments within the adjustment stratum share the energy expenditure patterns of

the responding establishments within the strata, the resulting adjustments to the MECS estimates will tend to be minimally

biased. If, on the other hand, the energy expenditure patterns of the responding and nonresponding establishments differ

substantially, the resulting adjustments are potentially biased, and the overall estimates may not accurately represent the

originally targeted MECS universe. 

Item Nonresponse

Item nonresponse is the type of nonresponse that occurs when an item (or several items) is missing in an otherwise

completed questionnaire. In 1994, MECS expanded its collection of establishments’ characteristics. MECS now collects

economic information, such as floorspace, motor purchase evaluations, reasons for fuel switching, energy management

activities, and energy efficient technologies. Although a Response Analysis Survey of 1991 respondents indicated that a

record-keeping system tracked these items, some establishments did not have a record-keeping system which would have

enabled them to respond to these types of questions. To the extent that information systems excluded economic items, the

MECS incurred item nonresponse. 

Some surveys impute values for item nonresponse. The MECS did not impute for most of these missing items. The

complexities and inherent heterogeneity of manufacturing establishments prevent the use of imputation techniques for most

of these missing items. However, budget restrictions and timeliness issues halted respondent recontacts before item

nonresponse could be eliminated. Hence, tables in this report have nonresponse columns or rows to reveal the extent of item

nonresponse; for example, the floorspace data in Table A7. 

MECS has a full reporting of energy consumption from all responding establishments. However, economic variables (such

as value of shipments and value added) have historically been obtained from the Annual Survey of Manufactures (ASM).

The change in sampling frames from the ASM to the CM had an indirect result on item nonresponse; that is, some

establishments in the MECS sample did not have economic data (value of shipments and value added) because they were

not included in the 1994 ASM sample. One approach considered by EIA was to query these establishments on their

shipments. Unfortunately, duplication issues with the ASM precluded the MECS from querying establishments on their

shipments and value added. Hence, these establishments did not have the ability to report their economic data. 

Unlike the establishment characteristics, like floorspace, value of shipments and value-added estimates were imputed for

all MECS establishments that were not in the sample. Of the 19,292 MECS sample respondents, 13,011 cases have ASMreported value of shipments data and ASM-reported value-added data. Of the remaining cases, we only have the total

number of employees and payroll from the Standard Statistical Establishment List (SSEL). Using aggregate payroll data,

by industry (at the four-digit SIC level), value of shipments and value added were imputed by forward indexing. Using value

added as an example, this imputation technique is expressed as

where SW is payroll from the 1994 SSEL and SW and VA are payroll and value added from the 1992 Census of 94 92 92

Manufactures (CM). Value of shipments was imputed analogously.

The imputation model was evaluated by testing how well it predicted known 1994 ASM value-added data. Matching was

done for SIC industries 2000 through 3999. If an establishment was indicated as out-of-business, the observation was

deleted. If the ratio SW /SW was less than 1/8 or more than 8, the data were considered either as outliers or as recording 94 92

errors. Such observations (696 out of 52,227) were removed prior to analysis. 

A paired comparison t-test was done for the percent difference between VA and actual VA to determine if the mean imp 94

percent difference was statistically significant from zero. At a 95-percent confidence level, the null hypotheses (the mean
Energy Information Administration/Manufacturing Consumption of Energy 1994 443

percent difference between VA and actual VA is zero) could not be rejected. Hence, there was no difference statistically imp 94

between Va and VA . A t-test using value of shipments yielded the same conclusion. imp 94

Table C2 provides the counts, percents, and weighted percents for the imputed estimates of value of shipments and value

added by industry. 

More detailed information on sources of nonsampling error in the MECS can be found in the methodological report.
444 Energy Information Administration/Manufacturing Consumption of Energy 1994

Table C2. Number of Establishments and Weighted and Unweighted Percentages of the Imputed Value of

Shipments and Value Added

SIC Industry Groups Number of Weighted Number of Weighted

Code and Industry Establishments Percent Percent Establishments Percent Percent

Value of Shipments Value Added

 2011 Meat Packing Plants . . . . . . . . . . . . . . . . . . . 10 1.2 4.2 10 1.1 4.6

 2033 Canned Fruits and Vegetables . . . . . . . . . . . 47 10.1 19.2 47 9.5 17.7

 2037 Frozen Fruits and Vegetables . . . . . . . . . . . . 18 5.2 21.3 18 3.0 17.7

 2046 Wet Corn Milling . . . . . . . . . . . . . . . . . . . . . . 12 3.6 3.6 12 3.2 3.2

 2051 Bread, Cake, and Related Products . . . . . . . 24 2.3 5.7 24 2.0 5.3

 2061 Cane Sugar, Except Refining . . . . . . . . . . . . 26 36.7 36.7 26 35.1 35.1

 2062 Cane Sugar Refining . . . . . . . . . . . . . . . . . . 4 6.8 6.8 4 7.0 7.0

 2063 Beet Sugar .......................... 4 W W 4 W W

 2075 Soybean Oil Mills . . . . . . . . . . . . . . . . . . . . . 10 2.4 2.4 10 3.0 3.0

 20 Balance of Food and Kindred Products . . . . 322 6.1 17.4 322 3.9 13.2

 21 Tobacco Products . . . . . . . . . . . . . . . . . . . . 7 1.3 1.5 7 1.2 1.4

 22 Textile Mill Products . . . . . . . . . . . . . . . . . . . 139 6.3 14.5 139 6.7 18.0

 23 Apparel and Other Textile Products . . . . . . . 247 7.9 17.2 247 8.7 21.9

 2421 Sawmills and Planing Mills, General . . . . . . . 177 19.0 38.3 177 20.9 48.1

 2436 Softwood Veneer and Plywood . . . . . . . . . . . 5 2.8 3.9 5 2.8 4.0

 2493 Reconstituted Wood Products . . . . . . . . . . . 13 5.9 13.4 13 4.5 11.0

 24 Balance of Lumber and Wood Products . . . . 313 14.8 42.5 313 17.2 49.7

 2511 Wood Furniture, Except Upholstered . . . . . . 38 10.3 32.7 38 8.9 27.1

 25 Furniture and Fixtures . . . . . . . . . . . . . . . . . . 131 9.3 35.2 131 9.0 37.7

 2611 Pulp Mills . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 6.5 6.5 9 5.6 5.6

 2621 Paper Mills . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2 4.2 61 4.2 4.2

 2631 Paperboard Mills . . . . . . . . . . . . . . . . . . . . . . 64 6.0 6.0 64 7.3 7.3

 26 Balance of Paper and Allied Products . . . . . 240 10.5 24.6 240 10.0 23.9

 27 Printing and Publishing . . . . . . . . . . . . . . . . . 612 9.9 35.2 612 8.7 32.9

 2812 Alkalies and Chlorine . . . . . . . . . . . . . . . . . . 18 24.8 24.8 18 33.2 33.2

 2813 Industrial Gases . . . . . . . . . . . . . . . . . . . . . . 24 9.4 11.8 24 8.8 10.8

 2816 Inorganic Pigments . . . . . . . . . . . . . . . . . . . . 33 12.0 12.0 33 11.3 11.3

 2819 Industrial Inorganic Chemicals, nec. . . . . . . . 42 6.6 13.6 42 3.6 9.7

 2821 Plastics Materials and Resins . . . . . . . . . . . . 22 4.8 5.2 22 3.8 4.4

 2822 Synthetic Rubber . . . . . . . . . . . . . . . . . . . . . 15 6.6 6.6 15 6.1 6.1

 2823 Cellulosic Manmade Fibers .............. 2 W W 2 W W

 2824 Organic Fibers, Noncellulosic . . . . . . . . . . . . 16 3.3 3.3 16 2.8 2.8

 2861 Gum and Wood Chemicals . . . . . . . . . . . . . 19 12.9 12.9 19 12.8 12.8

 2865 Cyclic Crudes and Intermediates . . . . . . . . . 17 7.9 10.2 17 9.2 10.8

 2869 Industrial Organic Chemicals, nec. . . . . . . . . 46 6.1 8.7 46 6.9 9.9

 2873 Nitrogenous Fertilizers . . . . . . . . . . . . . . . . . 54 19.9 19.9 54 17.2 17.2

 2874 Phosphatic Fertilizers . . . . . . . . . . . . . . . . . . 27 17.5 17.5 27 3.5 3.5

 287 Balance of Agricultural Chemicals . . . . . . . . 9 1.9 19.4 9 1.0 7.6

 2895 Carbon Black ........................ 7 W W 7 W W

 28 Balance of Chemicals and Allied Products . . 114 2.1 11.8 114 1.6 9.5

 2911 Petroleum Refining . . . . . . . . . . . . . . . . . . . . 46 3.0 3.0 46 3.9 3.9

 29 Balance of Petroleum and Coal Products . . . 158 22.1 29.3 159 24.1 34.1

 3011 Tires and Inner Tubes ................. 1 W W 1 W W

 308 Miscellaneous Plastics Products, nec. . . . . . 353 8.8 23.8 353 8.5 24.0

 30 Balance of Rubber and Misc. Plastics

Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 15.0 39.6 135 13.8 37.4

 31 Leather and Leather Products . . . . . . . . . . . . 48 9.1 29.9 48 9.8 31.5

 3211 Flat Glass . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.9 1.9 11 2.3 2.3

 3221 Glass Containers ..................... 2 W W 2 W W

 3229 Pressed and Blown Glass, nec. . . . . . . . . . . 4 0.9 3.3 4 1.0 3.6

 3231 Glass Products Made from Purchased Glass 28 5.4 15.0 28 4.7 5.3

 3241 Cement, Hydraulic . . . . . . . . . . . . . . . . . . . . 23 16.3 17.6 23 14.5 15.7

 3274 Lime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 77.1 77.1 57 77.4 77.4

 3296 Mineral Wool ......................... 2 W W 2 W W

 32 Balance of Stone, Clay, and Glass Products 384 19.4 44.8 384 18.7 46.6

 3312 Blast Furnaces and Steel Mills . . . . . . . . . . . 59 2.7 2.7 59 2.0 2.0

 3313 Electrometallurgical Products . . . . . . . . . . . . 17 61.6 61.6 17 73.5 73.5

 331 Balance of Blast Furnace and Basic Steel

Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 12.5 35.0 49 14.3 42.3

 3321 Gray and Ductile Iron Foundries . . . . . . . . . . 34 7.0 17.5 34 7.1 18.3

See footnotes at end of table.
Descriptions of all EIA data collection activities are included in Energy Information Administration, Directory of Energy Data Collection Forms, 50

DOE/EIA-0449(90) (Washington, DC, January 1991).

Energy Information Administration/Manufacturing Consumption of Energy 1994 445

Table C2. Number of Establishments and Weighted and Unweighted Percentages of the Imputed Value of

Shipments and Value Added (Continued) 

Value of Shipments Value Added

SIC Industry Groups Number of Weighte Number of Weighted

Code and Industry Establishments Percent d Establishments Percent Percent

Percent

3331 Primary Copper .................... 3 W W 3 W W

3334 Primary Aluminum ................. 3 W W 3 W W

3339 Primary Nonferrous Metals, nec . . . . . . . 38 16.2 16.7 38 13.9 13.3

3353 Aluminum Sheet, Plate, and Foil . . . . . . . 12 2.5 2.5 12 2.4 2.4

33 Primary Metal Industries . . . . . . . . . . . . . 218 11.0 23.5 218 10.7 25.7

34 Fabricated Metal Products . . . . . . . . . . . 529 8.4 30.7 529 8.9 32.8

357 Computer and Office Equipment . . . . . . 9 0.2 5.2 9 0.2 7.1

35 Balance of Industrial Machinery and

Equipment . . . . . . . . . . . . . . . . . . . . . . . 545 5.0 27.2 545 5.6 28.9

36 Electronic and Other Electric Equipment 141 1.5 14.9 141 1.5 15.5

3711 Motor Vehicles and Car Bodies ....... 2 W W 2 W W

3714 Motor Vehicle Parts and Accessories . . . 36 1.0 8.8 36 1.2 10.1

37 Balance of Transportation Equipment . . . 76 1.1 11.2 76 1.2 12.9

3841 Surgical and Medical Instruments . . . . . . 20 1.7 12.3 20 1.4 10.6

38 Balance of Instruments and Related

Products . . . . . . . . . . . . . . . . . . . . . . . . . 102 2.4 16.2 102 2.1 16.3

39 Miscellaneous Manufacturing Industries . 141 10.2 44.2 141 9.5 43.8

Total 6,284 4.8 17.8 6,284 4.7 19.5

 W = Withheld to avoid disclosing data for individual establishments.

 Source: Energy Information Administration, Office of Energy Markets and End Use, Energy End Use and Integrated Statistics Division, Form

EIA-846, “1994 Manufacturing Energy Consumption Survey.” 

Comparability of MECS Estimates with Other Series

The Energy Information Administration (EIA) collects data from two distinct sources that, in their entirety, provide a

comprehensive picture of energy production, marketing, and consumption in the United States. One set of surveys is 50

directed to the suppliers and marketers of specific fuels (including electricity). The second group of surveys collects

comprehensive energy consumption and related data directly from end-use consumers. The MECS is a member of the latter

group.

Because there is a seeming correspondence between energy supplied and energy consumed, it is tempting to compare or

merge their results. However, there are important differences between the supplier and end-user surveys that need to be

taken into account in doing such comparisons or other analyses. 

An Overview of EIA Surveys

The End-User Surveys

The overall purpose of the end-user surveys is to provide comprehensive baseline data on energy consumption and related

characteristics for major sectors of the U.S. economy. Accordingly, the end-user surveys are conducted for the

manufacturing sector, commercial buildings, residential households, and residential transportation. These surveys collect

data directly from samples of the energy-consuming units comprising those sectors. The results of these end-user surveys

are available in a variety of EIA publications.
For a complete list of publications, see Energy Information Administration, EIA Publications Directory 1996, DOE/EIA-0149(96) (Washington, 51

DC, May 1997).

See Office of Management and Budget, Standard Industrial Classification Manual 1987 (Washington, DC, 1987). 52

Energy Information Administration, Monthly Energy Review, September 1993, p. 40. 53

446 Energy Information Administration/Manufacturing Consumption of Energy 1994

The Supplier Surveys

The EIA conducts numerous supplier surveys. The overall purpose of these surveys is to measure the quantity of a specific

fuel produced and/or supplied to the market, along with other information related to the fuel's production and supply. The

results of these surveys are published in several EIA reports.51

Combined Results of the Supplier Surveys

In addition to supporting fuel-specific publications of EIA, the results of the supplier surveys are combined to produce

estimates of total energy consumption by consuming sector. The consuming sectors consist of the commercial, residential,

industrial, transportation, and electric utilities sectors. The resulting combined estimates are published by EIA in the

Monthly Energy Review (MER), the State Energy Data Report (SEDR), and the Annual Energy Review (AER). 

Defining the Industrial Sector

In general, the "industrial sector" is defined as consisting of manufacturing, mining, construction, agriculture, fisheries, and

forestry. The approximate SIC equivalent of the industrial sector includes major group codes 01 through 39. There are 52

a few definitional irregularities, however, that preclude a perfect mapping of the supplier surveys to that range of SIC codes.

As pointed out in the MER,

. . . although end-use allocations are made according to [the sector definitions]

as closely as possible, some data are collected by using different classifications.

For example, data on agricultural use of natural gas are collected and reported

in the commercial sector rather than the industrial sector. Since agricultural use

of natural gas cannot be identified separately, it is included in the commercial

sector....[rather than the industrial sector.]53

Comparing the MECS and Industrial Sector Estimates

The MECS produces four separate estimates of manufacturing energy consumption, which are: (1) Total First Use of Energy

for All Purposes (Table A1), (2) Total First Use of Energy for Nonfuel Purposes (Table A3), (3) Total Inputs of Energy for

Heat, Power, and Electricity Generation (Table A4), and (4) Total Consumption of Offsite-Produced Energy for Heat, Power,

and Electricity Generation (Table A5). The differences among those estimates are discussed in detail elsewhere in Appendix

B of this report. 

The combined estimates for the industrial sector published in SEDR are conceptually similar to the MECS estimates of Total

First Use of Energy For All Purposes, because both series measure energy consumption as a fuel and as a raw material or

feedstock.

A Final Observation

Many of the substantial differences between the MECS estimates of first use of energy and the combined estimates resulting

from the supplier surveys can be reconciled by carefully reviewing the coverage and definitions of the data series involved.

It should be emphasized that the differences are not an indication of the relative strengths or weaknesses of either series.

Rather, the differences in the estimates simply reflect the differences in the intents of the end-user surveys and the supplier

surveys. The overall purpose of the end-user surveys is to provide baseline energy consumption and related characteristics

data for various groups of end users (manufacturers, residential housing and transportation, and commercial buildings). The

overall purpose of the supplier surveys, on the other hand, is to provide baseline data on the production and supply of various

fuels. To reiterate, data users should be extremely wary of attempting to compare or combine the results of the end-user and

supplier surveys without careful attention to the origins and purposes of the different estimates. The details of a study

comparing the 1991 MECS and Industrial Sector estimates can be found in Appendix D in Manufacturing Energy
Energy Information Administration/Manufacturing Consumption of Energy 1994 447

Consumption Survey, 1991 (DOE/EIA-0512(91)). The values are somewhat different in 1994 than in 1991, but the major

relationships and their explanations remain appropriate.