Multiple Imputation in the Annual Survey of Manufactures 

T. Kirk White** 

Jerome P. Reiter* 

*Duke University 

jerry@stat.duke.edu 

**U.S. Bureau of the Census 

tkw2@duke.edu 

PRELIMINARY AND INCOMPLETE1 

Introduction 

Missing data due to item non-response is a pervasive problem in social and economic data. Often 

economists simply throw out observations with missing data. Estimates based on the remaining samples 

can be inefficient or even biased. A large and growing literature in statistics show to impute values for 

missing data, and a literature dating back at least to Rubin (1987) has shown how to use multiple 

imputation to estimate the additional uncertainty introduced into the data due to imputation. Methods of 

single imputation, such as plugging in the industry mean or a ratio estimate, lead to underestimation of 

uncertainty in many analyses. This paper will apply a particular version of multiple imputation, 

Raghunathan et al.’s (2001) sequential regression multiple imputation (SMRI), to a particular dataset, the 

Annual Survey of Manufactures (ASM). The goal is to improve inferences for a commonly used 

confidential economic dataset. But the SMRI method is applicable much more generally, and thus our 

empirical results may be of interest to researchers using any dataset that contains missing or imputed items. 

Although the SMRI method is due to Raghunathan et al. (2001), here we briefly describe the 

method and motivate its use. SMRI is a multivariate technique for imputing missing values 

X1 X1

using a 

sequence of regression models. The basic idea is to impute from a regression of on 

( , , .) 2 3 X X etc X2

X3

( , , .) 1 2 X X etc

, impute from a regression of X2on ( , , .) 1 3 X X etc , impute X3from a regression 

of on , and so on. The regression models are specified to match the distribution of the 

outcome variable. For example, use a multinomial logistic regression for a multinomial variable, a logistic 

regression for a binomial variable, and a linear regression for a continuous variable with normally 

distributed errors. An advantage of this strategy is that it is generally easier to specify plausible conditional 

models than plausible joint distributions. A disadvantage is that the collection of conditional distributions 

is not guaranteed to correspond to a proper joint distribution, particularly when the models use different 

conditioning sets. 

Two other advantages of the SMRI method relative to the current Census imputation methods are 

transparency and flexibility. The Census Bureau routinely imputes values for missing data, but it is often 

not clear to researchers using the microdata how this imputation was done. And, while the imputation 

methods used by the Census Bureau (and other data collection agencies) may be appropriate for Census 

1 This report is released to inform interested parties of ongoing research and to encourage discussion of 

work in progress. Any views expressed on statistical, methodological, technical or operational issues are 

those of the authors and not necessarily those of the U.S. Census Bureau. The research in this paper was 

conducted while the first author was an employee of the U.S. Census Bureau at the Triangle Census 

Research Data Center. This paper has been screened to ensure that no confidential data are revealed. 

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Bureau goals such as industry tabulations, they may not be appropriate for researchers using confidential 

microdata. Use of SMRI as a general method for imputation enables researchers to tailor the imputation to 

their particular application. 

Tim Dunne (1998) and other researchers have noted that identifying and dealing with missing and 

imputed data are important problems for researchers using the Census of Manufactures (CMF) and Annual 

Survey of Manufactures (ASM) microdata. Until very recently, the ASM and CMF data available in the 

Census Research Data Centers (RDC) contained no item-level flags to identify imputed data. Dunne 

(1998) documented ways to identify imputed data in the CMF and ASM. In one industry frequently 

studied by economists, roughly 40% of the data appears to be imputed (Collard-Wexler, 2007).2 To the 

extent that economists deal with missing data issues at all, they typically throw out observations identified 

as imputed (Foster, Haltiwanger, and Syverson, 2007; Collard-Wexler 2007; and many others). Throwing 

out observations with missing or imputed data can lead to biased estimates and deflated standard errors 

(Little and Rubin, 2002). On the other hand, treating imputed data as if it were complete data will typically 

lead to confidence intervals that are “too narrow”, i.e., the confidence intervals will understate the amount

of uncertainty in the estimates due to missing data. 

The U.S. Census Bureau uses a variety of methods to impute data in the ASM and CMF. Based on 

our analysis of the item-edit flags in the 2002 CMF and the 2003-2005 ASM, one of the most common 

methods appears to be “cold-deck.” The cold-deck method typically involves using data from other 

records, perhaps in the same industry, sometimes from earlier years, to impute missing data items. While 

we have made inquiries at the Census Bureau through the appropriate channels, at the time of this writing 

we have not been able to ascertain precisely how the Census Bureau implements the cold-deck method in 

the ASM and CMF. Nevertheless, we can say for certain that only one imputation per missing item is 

recorded in the ASM and CMF data available in the RDCs. From a researchers’ perspective, having only 

one imputation per missing data item will typically cause the researcher to understate the amount of 

uncertainty in her estimates. 

This paper uses new data and new methods to address both problems mentioned above: identifying 

and dealing with missing data in the ASM/CMF. The Census Bureau does many different types of edits of 

the data it collects. The Census Bureau classifies some types of edits as “imputed,” and others as “not 

imputed.” The 2002 CMF and 2003-2005 ASM data contains item edit flags that identify imputed items, 

and the name of the method used to impute them. For any data item for which the item edit flag is in the 

“imputed” category, we consider that data item “missing.” We apply the sequential regression imputation 

method (Raghunathan et al., 2001) to multiply impute missing values. Then we compare our method to a 

version of an imputation method currently used by the Census Bureau. First we estimate a model of 

missingness based on the data. We use this model to create missing data, that is, we “poke holes” in the 

records with complete data. Then we create multiple imputations for these artificially missing values 

using the sequential regression method, and we create single imputations using a version of the cold-deck 

method (described below). We find that industry means based on our multiple imputations tend to be 

closer to the true mean (based on the real data) than estimates from the data imputed (singly) using the 

cold-deck method. Further, we find that our estimated confidence intervals tend to be wider than the 

confidence intervals from the single cold-deck imputed data, and our confidence intervals are more likely 

to cover the confidence intervals from the real data. 

Data 

As mentioned above, we use the 2002 Census of Manufactures (CMF) and the 2003-2005 Annual Surveys 

of Manufactures, since these years of the data have item-level edit flags. Roughly a third of the plants in 

the manufacturing data come from Administrative Records (AR) data. These plants are not sent a survey 

form. We follow most of the economics literature and drop these plants from our sample. The long-run 

goal of the project is to estimate plant-level and aggregate total factor productivity (tfp), so we are 

primarily interested in missing/imputed data in variables that are typically used to compute tfp: the total 

value of shipments (TVS), the total cost of materials (CM), production workers hours (PH), production 

2 Roughly one third of the data in the CMF comes from establishments which have fewer than 5 employees 

and are not sent a survey form. Data for these plants comes from Administrative Records (AR). The 40% 

figure in Collard-Wexler refers to non-AR records. Likewise, throughout this paper, when we refer to 

percentages of imputed or missing data, we are referring to percentages of non-AR records. 

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worker wages (WW), total salaries and wages (SW), expenditures on electricity (EE), capital expenditures 

on buildings (CBE), and capital expenditure on machinery (CME). For each of these variables there is a 3-

digit item edit flag explaining whether the recorded value was reported and/or edited or imputed in some 

way. As mentioned above, we treat as “missing” any data item which the Census Bureau item edit flags 

classify as “imputed.” We then proceed to create our own imputations for these “missing” data items. 

Table 1 shows the rates of “missingness” for our variables of interest across all non-AR manufacturing 

plants in each year of our sample. Payroll (total salaries and wages) can be found in administrative 

records, and the Census Bureau does not classify these payroll items as “imputed” (and thus we do not 

consider them missing). This may account for the low rate of missingness reported for this variable in 

Table 1. Among non-AR plants in the 2002 CMF, 44% of the CBE items are “raked.” This means that the 

sum of reported detail items (such as capital expenditure on building and capital expenditures on 

machinery) do not balance to the reported total (such as total capital expenditures). The Census Bureau 

changes the details proportionally so that they add up to the reported total. These “raked” items are not 

considered imputations, and thus we do not count them as missing. This accounts for the low rates of 

missingness reported in Table 1 for the 2002 capital expenditures. 

2002 2003 2004 2005 

Total Value of Shipments 28 29 27 26 

Total Cost of Materials 42 35 33 34 

Plant Hours (production workers) 39 30 27 27 

Worker wages 19 25 22 22 

Total Salaries and wages 0 1 0 0 

Electricity expenditures 46 30 30 30 

Capital expenditures (buildings) 0 36 27 28 

Capital expenditures (machinery) 0 36 27 31 

Sample size 215,683 64,417 55,645 57,155 

Table 1: Percentages of missingness/imputation among all non-AR plants in the 2002 Census of 

Manufactures and the 2003-2005 Annual Surveys of Manufactures, according to item edit flags. 

The Table 2 reports the standard deviations of the percentage missing for each 5-digit NAICS 

manufacturing industry for each year of our sample. The table shows that the rates of missingness vary 

considerably across industries within the manufacturing sector. 

Variable 2002 2003 2004 2005 

Total Value of Shipments 14 11 10 9 

Total Cost of Materials 12 13 11 10 

Plant Hours (production workers) 10 11 9 10 

Worker wages 13 11 8 8 

Total Salaries and wages 0 1 1 1 

Electricity expenditures 14 12 11 10 

Capital expenditures (buildings) 0 13 10 10 

Capital expenditures (machinery) 0 13 10 10 

Table 2: Standard deviations of 5-digit NAICS industry percentage missing, among non-AR plants in the 

2002 Census of Manufactures and the 2003-2005 Annual Surveys of Manufactures, according to item edit 

flags. 

An Imputation Model 

As mentioned above, Raghunathan et al. (2001) develop a multivariate technique for multiply 

i

X1

mputing missing values, SMRI, that uses a 

X1

( , , .) 2 3 X X etc

sequence of regression 

X2

models. The basic 

X2

idea is to impute 

from a regression of on , impute from a regression of on 

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( , , .) 1 3 X X etc , impute X3

from a regression of X3on ( , , .) 1 2 X X etc , and so on. The regression 

models are specified to match the distribution of the outcome variable. For example, the user can specify a 

multinomial logistic regression for a multinomial variable, a logistic regression for a binomial variable, and 

a linear regression for a continuous variable with normally distributed errors. 

In preliminary analysis we found that most of our variables have large positive first order 

autocorrelations, but insignificant autocorrelations at higher lags. The exceptions were the capital 

expenditure variables, which also have significant second-order autocorrelations. Thus we hypothesize that 

an imputation model using just current and one-period lagged values might do a good job. Using just OLS, 

we tried several different regression specifications for plants with complete data. Based on the R-squared 

of the regressions our preferred specification is: 

jk kijt ijt

ijt j j ijt kj kijt k j ijt

X

Y X X X

a e

a a a a

+ +

= + + + + +

-

- - + -

2

,2 1

2

0 1 1 1 1 ,1 1 1

(ln )

ln ln ... ln (ln ) ...

(1), 

where Yijt is any of our variables listed in tables 1 and 2, for plant i in year t; the X’s are lagged values of 

all of these variables, including the variable Y; and ε is an error term. We ran this regression for 86 4-digit 

NAICS industry groups and found that the R-squared of the regression exceeded 0.90 for all variables and 

all industries. 

Using the specification in equation (1) as a starting point, we then applied the method of 

Raghunathan et al. (2001) using IVEware, code based on the SAS macro language. IVEware implements 

the sequential regression approach, conditioning on all variables (which in our case includes squared terms) 

in the specified models. We want to ensure that our industries are homogenous enough so that using the 

same imputation model for all plants in the industry makes sense. At the same time we need to keep the 

industry grouping coarse enough to keep the analysis of many industries feasible. To balance these two 

aims we assume that all establishments in the same 5-digit NAICS industry can use the same imputation 

model. For this preliminary analysis we allow for a year dummy variable, but otherwise we assume the 

parameters of the model are the same across all years within the same industry. Thus our full imputation 

model is: 

kj tij kj kijt kj tij kj kijt ijt

ijt j j ijt jk kijt kj ijt kj kijt jt

Y X Y X

Y X X j X X

b b b b e

b b b b b d

+ + + + + + +

= + + + + + + +

+ - - + - -

+

2

4, 1

2

12, 1, 3, 1 13, 1,

2

2,

2

0 1 1 1, 1

ln ... ln (ln ) ... (ln )

ln ln ... ln (ln ) ... (ln )

(2), 

where i indexes the plant, j is the industry, t is the year, and k indexes the explanatory variable. Note that 

lagged values of the dependent variable appear as predictors. The parameter δjt is an industry-specific year 

dummy. The SMRI procedure first imputes initial values for all missing data, drawn from models 

estimated with the complete data. The procedure then cycles through all the variables, replacing missing 

values based on equation (2). That is, for variable Y, the imputations are drawn from the posterior 

predictive distribution defined by the regression in (2), where the parameters of the regression have noninformative prior distributions. This involves estimating the parameters in equation (2) using the current 

version of the completed data, then randomly drawing a value of Y using the drawn parameter values. At 

each draw, the procedure imputes new values for the originally missing values, using the imputed values of 

covariates from previous iterations. In practice we iterate 10 times for each imputation and keep 20 

imputations for each missing value. Thus we do 200 imputations for each missing value and keep 20. 

Comparison to Cold-deck Imputation 

To assess the performance of the sequential regression imputation method for missing values in the ASM 

and CMF, we compare our results to results based on a version of “cold-deck” imputation. The single 

regression method can handle missing values in any of the observed variables and it allows for different 

types of missing data patterns. In particular, it does not require monotone missingness.3 However, to keep 

3 IVEware does assume that the missing data mechanism is ignorable. 

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the comparison simple, we focus on samples with missing items in only one variable at a time. 

Specifically, we begin by selecting plant-year observations in which the Total Cost of Materials (CM) 

variable may or may not be missing, but all other variables in table 1 are reported on the survey form (and 

not imputed).4 Although the item-edit flags only exist for the years 2002-2005, we want to use lagged 

values of the variables as predictors. Thus for year 2002 CMF observations, we included lagged values 

from plants in the 2001 ASM. Using this sample, we estimate a logit model of missingness for each 5-digit 

NAICS: 

ln ln ... ln )

Pr( ( ) )1 ( ln ... ln

1, 1, 1 1 2, 1

0 1 1

jCM tij kj ijt kj kijt ijt

ijt j j ijt jk kijt

CM X X

I CM X X

g g g e

g g g

+ + + + +

= = L + + +

- + - -

(3), 

where I() is the indicator function: I(CMijt)=1 if CMijt is observed and I(CMijt)=0 if CMijt is missing; Λ is 

the cdf of the logistic distribution; and X1ij through Xkij are all the variables in table 1 except the Total Cost 

of Materials. We keep the predicted probabilities from (3) for each plant. Then we select only the plantyear records in our sample with “complete” data, meaning all the variables in table 1 are observed, 

including CM and one-year lags of all variables. For each complete data record, we take a draw from a 

Bernoulli distribution with probability equal to that record’s predicted probability from (3). Based on the 

Bernoulli trial we set CM to missing or we keep the observed CM. Basically we are “poking holes” in the 

CM variable in the complete data. 

Having created artificially missing data from our complete records, we do two types of imputation: (i) 

multiple imputation using sequential regression and (ii) single imputation using a cold-deck method. For 

the purpose of our comparison and to avoid disclosure issues we select the 89 5-digit NAICS industries that 

have more than 300 complete plant-year records. To ensure some degree of comparability of plants within 

an industry we throw out industries with 9’s in the NAICS code, since these tend to be catch-all categories 

(e.g., 33399=”All Other General Purpose Manufacturing”). This leaves us with 66 industries and 261 

industry-years (three industry-years did not have enough complete records). The appendix has a complete 

list of the industries in our sample. 

For the multiple imputations using the sequential regression method, we used a model in the form of 

equation (2). IVEware allows the user to perform stepwise regressions: for each industry the program adds 

explanatory variables to the regression specification one at a time until the R-squared of the regression 

increases by less than a specified number (we choose 0.01). Thus in general the regression specification is 

different for different industries. We found that on average our imputations were closer to the real data if 

we excluded the capital expenditure variables.

5 We construct 20 imputations for each missing value and 

then use Rubin’s (1987) combining formulas to compute confidence intervals which reflect not only the 

uncertainty from sampling, but also the uncertainty in our estimates due to the missing data. 

For comparison, we also impute single values for the artificially missing Total Cost of Materials (CM) 

data using a simple ratio method, which we also refer to as a “cold-deck” method. For each artificially 

missing CM item, we impute CM_impijt=TVSijt*(CMjt/TVSjt), where TVSijt is plant i’s total value of 

shipments in year t, CMjt is the mean cost of materials in industry j in year t and TVSjt is the mean value of 

4 For the current preliminary paper, we report results only for the Total Cost of Materials imputations. We 

plan to compare our imputations to relevant alternative imputation methods for all the main ASM variables. 

For some variables, other imputation methods are more common than the cold-deck method. For example, 

for electricity expenditures (EE), the most common imputation method used by the Census Bureau seems 

to be plugging in the “industry average.” Thus for the electricity variable we plan to compare our 

imputation results to results where the industry average is substituted for missing values. 

5 In principle capital expenditures should have explanatory power for the cost of material inputs and other 

variables. Capital expenditures differ from other variables in that capital investment is more “lumpy” 

(Doms and Dunne, 1998; Power, 1998; Sakellaris, 2004), with many observed 0 values. In principle, the 

IVEware program can handle variables with mixed discrete/continuous distributions like these capital 

expenditure variables. However, so far, including the capital expenditure variables has caused the 

imputations to be far from the real data. In future work we plan to look into this further. 

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shipments in industry j in year t. We use this method as our benchmark for comparison for three reasons: 

(i) it is simple and transparent; (ii) in most industries at some of the CM items that are flagged as imputed 

by the “cold-deck” method seem to be imputed this way—i.e., many cold-deck-flagged CM observations in 

a given industry have the same CM/TVS ratio; (iii) at the time of this writing we have not yet been able to 

find out how the other CM items flagged as “cold-deck” were imputed. Table 3 presents a comparison of 

our results using the sequential regression multiple imputation method and the single value ratio method. 

Mean s.d. 25th 

Percentile 

Median 75th 

percentile 

True mean minus 

MI mean 

-418 1843 -458 -75 24 

True mean minus 

Cold-deck mean 

-732 3305 -652 -228 22 

R_MI 0.98 0.02 0.98 0.99 1.00 

R_cold-deck 0.95 0.06 0.93 0.96 0.98 

W_MI 1.04 0.14 1.00 1.01 1.04 

W_cold-deck 1.02 0.16 0.99 1.00 1.01 

Table 3. Comparison of industry-year means for sequential regression multiple imputation versus 

the single value ratio method for the Total Cost of Materials, thousands of dollars. All the statistics 

in the table are computed from industry-year means; thus each statistic represents many plant-level 

observations. See text for explanation of R and W statistics. 

The first row of table 3 shows the across-industry distribution of the difference between the 

industry-year mean computed from the real (complete) data and the combined industry-year mean from our 

20 implicates using the sequential regression imputation method. The first column of the first row the 

mean across all our industries of the difference between the industry means from the real data and the 

industry means from our multiply imputed data. This cell shows that on average the industry-year means 

of the Total Cost of Materials from our preferred imputations are about $418,000 higher than the industryyear means from the real (complete) data. This is perhaps not a great performance, but it is significantly 

better than the $718,000 upward bias in the estimates from the single imputation ratio method. The first 

two rows of the second column show that the standard deviation of the difference between the means from 

our preferred imputations versus the real data is significantly smaller than the standard deviation of the 

mean differences from the single value ratio method. At the median of the distribution of mean differences, 

our method dominates the single value ratio method: a $75,000 difference versus a $228,000 difference. 

In principle all single imputation methods suffer from the fact that the confidence intervals of any 

estimates from singly imputed data tend to understate the amount of uncertainty in the estimate due to 

missing data. Therefore we would like to compare confidence intervals for estimates computed from our 

multiply imputed data to confidence intervals from the real data and from the singly imputed data. For 

each of our 20 implicates, we compute a 95% confidence interval for each of the 261 industry-year means. 

Then, for each industry-year we used Rubin’s (1987) combining formulas to combine the confidence 

intervals from our 20 implicates. Then we compute the 95% confidence intervals for each industry-year 

mean from the real data. For each industry-year, we find the intersection of the (combined) confidence 

interval from our multiple imputations with the confidence interval from the real data. Define R_MIj as the 

length of this intersection divided by the length of the confidence interval from the real data. 

This measures how much the confidence intervals from our multiple imputations overlap with the C.I.’s 

from the real data. The third row of table 3 reports the mean, standard deviation, median and 25th and 75th 

percentiles of the distribution of these ratios. The results shows that the C.I.’s from our multiply imputed 

data overlap more than 90% of the C.I. from the real data across the distribution of industry-years. The 

fourth row of table 3 shows the distribution of the analogous ratio for the cold-deck imputations. The C.I.s 

from the cold-deck imputations cover less of the C.I.’s from the real data, but not much less. 

We want to check that the coverage of our C.I.’s is not driven by having unreasonably large C.I.’s 

from the multiple imputations. For each industry-year we compute the ratio of the width of the 

(appropriately combined) C.I.’s from the 20 implicates to the width of the C.I. from the real data. A ratio 

close to 1 means that a C.I. is not “too” large. The fifth row of table 3 shows the mean, standard deviation, 

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median and 25th and 75th percentile of the distribution of this ratio across industry-years. The width of the 

C.I.’s from our multiply imputed data is typically quite close to width of the C.I. from the real data. The 

sixth row of the table shows the distribution of the analogous ratio for the cold-deck imputations. 

Comparing these to row 5, the C.I.’s from the single cold-deck imputations tend to be smaller than the 

C.I.’s from the multiple imputations, but not much smaller. 

Tentative Conclusions and Next Steps 

As emphasized throughout, the results presented here are preliminary. The sequential regression multiple 

imputation method produces less biased results that single value ratio imputation method. The difference 

between the confidence intervals is in general not as large as we expected. One reason for this may be our 

sample selection criteria. To avoid disclosure issues we chose industries with many complete records. The 

vast majority of these industries have fewer than 10% missing values in the Total Cost of Materials 

variables, and, by construction all of the other variables in table 1 are observed in our sample. Tables 1 and 

2 show that for the typical manufacturing industry far more than 10% of its data items are missing. When 

the rates of missingness are higher, one would expect the confidence intervals from single imputation to be 

much smaller than the true data intervals, and the correctly combined confidence intervals from multiply 

imputed data to better reflect the uncertainty. 

The results presented here only include imputations for missing values in one variable, the Total Cost of 

Materials. In future work we plan to apply the sequential regression method for imputations of all the 

variables in tables 1 and 2, and possibly other variables, and to use these imputed data to compute plantlevel total factor productivity (tfp). Plant-level tfp is often estimated from something like the following 

equation:6 

ln ln ( ln ln ln )

ijt TVSijt 0 j kj Kijt lj Lijt mj CMijt tfp = - a +a +a +a , 

where K is a measure of the plant’s capital stock (usually constructed from capital expenditures and assets), 

L is a measure of labor inputs (constructed from plant hours, production workers’ wages, and total salaries 

and wages), and the other variables and indices are as described above. The cold-deck ratio method 

described in the main text forces the ratio of materials to shipments to be the same for all imputed 

observations in the same industry and year. Thus one might expect the ratio method to understate the 

amount of dispersion in plant-level tfp. Dispersion in within-industry plant-level tfp is an important feature 

of the U.S. manufacturing data (Abraham and White, 2006; Collard-Wexler, 2007; others). Since the 

sequential regression imputation method conditions on all the observations, it can potentially capture more 

of the dispersion of tfp seen in the observed data. 

While we have focused on the implications of missing and imputed data for researchers using the 

microdata via the Census Research Data Centers, this research may also have direct implications for 

Census Bureau programs. While we reiterate that these results are preliminary, the first two rows of table 3 

seem to indicate that sequential regression multiple imputation estimates are less biased than estimates 

from data imputed use the ratio method, even for simple industry-level means. We hope to investigate 

these implications further when and if we find out exactly how the Census Bureau does “cold-deck” 

imputations in these data. 

6 Typically, the dollar-valued variables would also be deflated so that the measures are in real (constant 

dollar) terms; other variables such as energy inputs or two types of labor or capital inputs might also be 

added to the specification, and the coefficients might be allowed to vary over time; or proxy variables 

might be used as in Olley and Pakes (1996) or Levinsohn and Petrin (2003). In all of these cases, the 

principle is the same: sequential regression allows for more flexibility to capture the assumed relationship 

between the variables than simple ratio methods do. 

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Appendix. Table A.1 lists the names and 5-digit NAICS codes of the 66 industries used to compute the 

statistics in Table 3 (in descending order of number of complete observations). 

NAICS 

code 

Name NAICS 

code 

Name 

32311 Printing 32733 Concrete Pipe, Brick, and Block Mfg 

32221 Paperboard Container Mfg 33361 Engine, Turbine, and Power 

Transmission Equipment Mfg 

33351 Metalworking Machinery Mfg 33151 Ferrous Metal Foundries 

32732 Ready-Mix Concrete 33641 Aerospace Product and Part Mfg 

33232 Ornamental and Architectural Metal 

Products 

32612 Plastics Pipe, Pipe Fitting, and 

Ulaminated Profile Shape Mfg 

33441 Semiconductor and Other Electronic 

Component 

31311 Fiber, Yarn, and Thread Mills 

31161 Animal Slaughtering and Processing 33152 Nonferrous Metal Foundries 

33271 Machine Shops 32518 Other Basic Inorganic Chemical Mfg 

33281 Coating, Engraving, Heat Treating, and 

Allied Activities 

32512 Industrial Gas Mfg 

33231 Plate Work and Fabricated Structural 

Product Mfg 

32312 Support Activities for Printing 

33211 Forging and Stamping 32551 Paint and Coating Mfg 

32412 Asphalt Paving, Roofing, and Saturated 

Materials 

31142 Fruit and Vegetable Canning, Pickling, 

and Drying 

32121 Veneer, Plywood, and Engineered Wood 

Product Mfg 

32616 Plastics Bottle Mfg 

32111 Sawmills and Wood Preservation 33243 Metal Can, Box, and Other Metal 

Container Mfg 

32191 Millwork 33131 Aluminum Production and Processing 

33721 Office Furniture (Including Fixtures) Mfg 32621 Tire Manufacturing 

31151 Dairy Product (Except Frozen) Mfg 32212 Paper Mills 

33272 Turned Product and Screw, Nut and Bolt 

Mfg 

33637 Motor Vehicle Metal Stamping 

33712 Household and Institutional Furniture 

Mfg 

32615 Urethane and Other Foam Product 

(Except Polystyrene) Mfg 

31111 Animal Food Mfg 33111 Iron and Steel Mills 

31181 Bread and Bakery Product Mfg 33221 Cutlery and Handtool Mfg 

32611 Plastics Packaging Materials and 

Unlaminated Film 

31122 Starch and Vegetable Fats and Oils Mfg 

32721 Glass and Glass Product Mfg 32712 Clay Building Material and Refractories 

33531 Electrical Equipment Mfg 31141 Frozen Food Mfg 

33341 Ventilation, Heating, A/C Mfg 33251 Hardware Mfg 

32521 Resin and Synthetic Rubber Mfg 33291 Metal Valve Mfg 

33621 Moter Vehicle Body and Trailer Mfg 32213 Paperboard Mills 

33711 Wood Kitchen Cabinet and Countertop 

Mfg 

31321 Broadwoven Fabric Mills 

33261 Spring and Wire Product Mfg 31121 Flour Milling and Malt Mfg 

31211 Soft Drink and Ice Mfg 32223 Stationery Product Mfg 

32541 Pharmaceutical and Medicine Mfg 32561 Soap and Cleaning Compound Mfg 

33331 Commercial and Service Industry 

Machinery Mfg 

33661 Ship and Boat Building 

32222 Paper Bag and Coated and Treated Paper 33311 Agricultural Implement Mfg 

8 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References 

Abraham, Arpad and T. Kirk White. 2006. The Dynamics of Plant-level Productivity in U.S. 

Manufacturing. Center for Economic Studies Working Paper. CES-WP-06-20. 

Collard-Wexler, Allan. 2007. Productivity Dispersion and Plant Selection in the Ready-Mix Concrete 

Industry. Working Paper. 

Doms, Mark and Tomothy Dunne. 1998. Capital Adjustment Patterns in Manufacturing Plants. Review of 

Economic Dynamics 1, 409-429. 

Dunne, Timothy. 1998. CES Data Issues Memorandum: 98-1. Center for Economic Studies Data Issues 

Memo. 

Foster, Lucia, John Haltiwanger, and Chad Syverson. 2005. Reallocation, Firm Turnover, and Efficiency: 

Selection on Productivity or Profitability? Center for Economic Studies Working Paper. CESWP-05-11. 

Levinsohn, James and Amil Petrin. 2003. Estimating Production Functions Using Inputs to Control for 

Unobservables. Review of Economic Studies. 70(2). April. 317-342. 

Little, R. J. A. and Rubin, D. B. 2002. Statistical Analysis with Missing Data. New York: Wiley. 

Olley, Stephen and Ariel Pakes. 1996. The Dynamics of Productivity in the Telecommunications 

Equipment Industry. Econometrica. November. 

Power, Laura. 1998. The Missing Link: Technology, Investment, and Productivity. Review of Economics 

and Statistics. May. Vol. 80, No. 2, pages 300-313. 

Raghunathan, T.E., J.M. Lepkowski, J. Van Hoewyk, and P. Solenberger. 2001. A Multivariate Technique 

for Multiply Imputing Missing Values Using a Sequence of Regression Models. Survey 

Methodology. Vol 21. No. 1. 

Rubin, D. B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: Wiley. 

Sakellaris, Plutarchos. 2004. Patterns of Plant Adjustment. Journal of Monetary Economics. Vol. 51. pp. 

425-450. 

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