Evaluating the Impact of Manufacturing Extension Services on Establishment Performance

Abstract

This study examines the effects of receipt of business assistance services from the Manufacturing Extension Partnership (MEP) on manufacturing establishment performance. The results generally indicate that MEP services have had positive and significant impacts on establishment productivity and sales per worker for the 2002 to 2007 period with some exceptions based on employment size, industry, and type of service provided. MEP services have also increased the probability of establishment survival for the 1997 to 2007 period. Regardless of econometric model specification, MEP clients with 1 to 19 employees have statistically significant and higher levels of labor productivity growth. The authors also observed significant productivity differences associated with MEP services by broad sector, with higher impacts over the 2002 to 2007 period in the durable goods manufacturing sector. The study further finds that establishments receiving MEP assistance are more likely to survive than those that do not receive MEP assistance.

Full text

Author Accepted Manuscript. Published version available as: CA Lipscomb, J Youtie, P Shapira, S Arora,
A Krause (2017). “Evaluating the Impact of Manufacturing Extension Services on Establishment
Performance,” Economic Development Quarterly. https://doi.org/10.1177/0891242417744050
Evaluating the Impact of Manufacturing Extension Services on
Establishment Performance
Clifford A. Lipscomba,*
Jan Youtieb
Philip Shapirac
Sanjay Arorad
Andy Krausee
April 2017
a. Greenfield Advisors, 106 N. Bartow Street, Cartersville, GA 30120; Phone: +1 (770) 334-3952;
cliff@greenfieldadvisors.com
b. Enterprise Innovation Institute, Georgia Institute of Technology, Atlanta, GA 30308;
c. Manchester Institute of Innovation Research, Alliance Manchester Business School, University of
Manchester, Manchester M13 9PL, UK, and School of Public Policy, Georgia Institute of
Technology, Atlanta, GA 30332-0345
d. American Institutes for Research, Washington, DC 20007
e. Department of Property, University of Melbourne, Australia
* Corresponding Author

i
ABSTRACT
This study examines the effects of receipt of business assistance services from the
Manufacturing Extension Partnership (MEP) on manufacturing establishment performance. Our
results generally indicate that MEP services had positive and significant impacts on establishment
productivity and sales per worker for the 2002–2007 period with some exceptions based on
employment size, industry, and type of service provided. MEP services also increased the probability
of establishment survival for the 1997–2007 period. Regardless of econometric model specification,
MEP clients with 1–19 employees have statistically significant and higher levels of labor productivity
growth. We also observed significant productivity differences associated with MEP services by
broad sector, with higher impacts over the 2002–2007 time period in the durable goods
manufacturing sector. The study further finds that establishments receiving MEP assistance are
more likely to survive than those that do not receive MEP assistance.

2
1. INTRODUCTION
This study examines the effects of receipt of business assistance services from the
Manufacturing Extension Partnership (MEP) on manufacturing establishment performance. The
study seeks to advance previous work on the effect of manufacturing extension services on
establishment productivity. Examining the determinants of manufacturing establishment
performance is important because U.S. industry continues to face challenges due to the increasingly
competitive global business environment. Previous work on manufacturing establishment
productivity has examined an array of factors, including plant ownership change, technology
adoption, and deregulation. This paper adds consideration of business assistance services as a
potential productivity determinant, specifically services of the Manufacturing Extension Partnership
(MEP) program, which is administered by the National Institute of Standards and Technology
(NIST). These business assistance services are delivered at the establishment level; to gauge the
effects of such services, the measurement of productivity effects must take place at the
establishment level as well. However, publicly available establishment-level productivity information
is not accessible. Therefore, this work assesses the performance of MEP-assisted manufacturing
establishments by linking establishment-level MEP data on business assistance recipients to
establishment-level data from the U.S. Census Bureau.
The methodology used in this paper draws on two prior studies that examined the effect of
the MEP program on manufacturing establishment performance. The first study, by Jarmin (1999),
was conducted on manufacturing performance data for the 1987 to 1992 period. This time period
was prior to the full roll-out of the MEP program in 1999. The second study was performed by a
team from SRI International and the Georgia Institute of Technology on manufacturing
performance data covering the 1997 to 2002 period (Ordowich et al., 2012). Both of these studies
assessed the impact of MEP services on manufacturing productivity, sales, and employment growth.

3
This paper extends this body of economic development evaluation studies by using a novel
fuzzy logic matching program to confirm that MEP data and Census data are linked to the correct
establishment and by updating the analysis with data from the 2002 to 2007 period. In addition, we
also analyze establishment survival by testing the ability of establishments to maintain operations
from an earlier to a later period. Generally, we find that MEP services had a statistically significant
and positive impact on establishment productivity and sales per worker for the 2002-2007 period,
with some exceptions. Specifically, we find that 1) smaller establishments receiving MEP services
experience statistically significant and positive labor productivity growth across several econometric
specifications, 2) durable goods manufacturing establishments receiving MEP services experience
statistically significant productivity increases, and 3) receipt of MEP services increases
manufacturing establishments’ likelihood of survival.
In the section below, we begin with an overview of the MEP program. In Section 3, we
summarize the results of the Jarmin (1999) and Ordowich et al. (2012) studies. Section 4 describes
the methodology used in this paper, which involves linking information from MEP project
information files (PIF) and customer information files (CIF) to databases from the U.S. Census
Bureau. Section 5 describes the results of our study. Finally, we conclude with some implications of
the results.
2. THE MANUFACTURING EXTENSION PARTNERSHIP
The MEP program provides business, technology, and other forms of assistance, typically to
existing (as opposed to startup), small and midsize manufacturing establishments (Shapira et al.,
2015). The program deploys a network of manufacturing experts (also known as manufacturing
extension agents) with centers in all 50 U.S. states and Puerto Rico. The aim of the MEP program is
to strengthen U.S. manufacturing competitiveness. The program was established through the

4
Omnibus Trade and Competitiveness Act of 1988, which created the first three centers, with
additional centers added such that a national system was in place by the mid-1990s.
1
The total MEP annual system budget is about $300 million (National Research Council,
2013, p. 19). The federal government awards about one-third of funds for the program, which the
centers match from state funds, client fees, and other sources. Over half (55%)
2
of the centers
operate as not-for-profit organizations (under section 501(c)(3) of the Internal Revenue Service
code), with the others operating as university-based or state government-run programs.
The essential rationale for the MEP and similar technology and innovation advisory services
in other countries is that existing small and mid-size establishments often face market imperfections
and other systematic challenges in acquiring and deploying information, expertise, skills, and other
resources. These issues lead to difficulties in technological and business upgrading, contributing in
turn to lagging productivity, innovativeness, and competitiveness among many of these
establishments (NAPA, 2003; National Research Council, 2013). The MEP’s underlying program
theory seeks to bridge these gaps through services that directly provide expertise, diagnostics,
mentoring, training, and other support to help manufacturing establishments to upgrade, as well as
access and referrals to other public and private resources (Shapira and Youtie, 2014). The small and
medium-sized firms that engage with the MEP do so because its services are customized to their
needs; equivalent private sector sources are either more expensive or not available, the MEP’s
services are oriented to business outcomes (rather than to research), and it offers independent yet
comprehensive access to a range of expertise. If they are effective, MEP services should prompt
intermediate business actions (including, but not limited to, equipment investment, enhanced plant
layouts, employee training, process and quality improvements, cost reductions, and new products
1
For a review of the development and operations of the MEP, see National Research Council (2013).
2
This percentage is based on 58 centers (excluding non-operational centers in Alaska and Florida).

5
and marketing strategies) leading to improved business performance outcomes such as enhanced
productivity, sustainability, and growth for its clients.
The MEP flexibly operates through a decentralized network in which each center addresses
its local conditions and the needs of manufacturers in that region to enhance their productivity.
MEP centers deliver services with some mix of in-house specialists and third-party providers. More
than 1,400 non-federal staff and over 2,400 third-party service providers are involved in service
delivery (National Research Council, 2013, p. 15). MEP services are delivered through assessments
of all aspects of a company’s business or specific functional areas following a variety of outreach
activities, one-on-one technical engagements to address a particular problem, hosting manufacturing
networks for knowledge and current practice sharing, and training events depending on the needs
and preferences of the manufacturer. Currently, the MEP serves about 7,000 to 8,000 clients
annually through about 12,000 projects.
3
NIST MEP oversees the governance structure of the
system and maintains an extensive program of monitoring and evaluation.
3. PRIOR STUDIES OF THE MEP AND MANUFACTURING PERFORMANCE
A series of studies, using a broad range of methods, have examined various aspects of the
performance and impact of the MEP in the U.S. and other technology extension and advisory
services outside of the U.S. (For reviews of these studies, see Youtie, 2013; and Shapira and Youtie,
2014.) In this paper, we particularly focus on two earlier benchmark national studies of the effects of
the MEP on client performance using non-assisted control groups. These benchmark studies are
overviewed in the following two sections.
3
National Research Council (2013, p. 57). According to this study, MEP services peaked most recently in FY 2007, with
9,000 clients served through some 14,500 projects.

6
3.1. Study 1: Jarmin
Jarmin (1999) estimated the effect of MEP services on the productivity of establishments.
His analysis was based on an augmented Cobb-Douglas production function with physical capital,
employment, and other plant characteristics as shown in the equation below.
4
it it
Ext
it it it
Y Ae K L e



(1)
This equation serves as the theoretical basis for all of the analyses that follow, where Yit is value-
added for establishment i in period t, Lit is employment for establishment i in period t, Kit is book
value of the capital stock of plant i in period t,
it

is the error term, and Extit is a dummy variable
equal to 1 if establishment i received MEP services in period t, 0 otherwise.
Jarmin began his analysis by performing simple ordinary least squares (OLS) analysis. He
then used a Heckman (1976) two-stage model to control for selection bias. The selection model
used a dummy variable for whether or not the plant was located in a Metropolitan Statistical Area
(MSA) that contained a manufacturing extension center as an instrument for the likelihood of being
an MEP client. This variable was found to be associated with client standing (Jarmin, 1999, p. 111).
Jarmin specified the Cobb-Douglas production function as a linear regression equation by
taking the natural logarithm of the Cobb-Douglas equation and rearranging the results. He obtained
the following regression equation:
 
log log 1 log( )
i i i
ii
YK
Ext L
LL
    
   
        
   
   
(2)
In Equation 2, the deltas (
s
) reflect changes in the value of a variable between 1987 and 1992, and
the parameter

measures deviations from constant returns to scale. The dependent variable in
4
This model is based on the work of Solow (1957) and the augmentation of this function by Griliches (1996) with the
stock of research expenditures accumulated by the establishment.

7
Equation 2 is the percentage change in labor productivity between 1987 and 1992. (Note that in our
analyses we have measured changes for the periods 1997-2007 and 1997-2002, as well as for 2002-
2007.) The impact of the MEP program is measured by the parameter

, which measures the
percentage difference in productivity between client and non-client plants. This formulation assumes
that receiving MEP services would increase the productivity of a small plant by the same percentage
as it would a large plant. Using Equation 2, Jarmin estimated two OLS models, one for all plants (N
= 15,263) and one with plants with 19 to 500 employees (N = 7,782).
In addition to estimating the OLS equation above, Jarmin (1999) estimated two more models
(using the same plant size delineations) with two-digit standard industrial classification (SIC)
dummies to control for industry differences as shown in the equation below.
 
1...
log log 1 log( ) +
i i N i i
ii
YK
Ext L SIC
LL
     
   
        
   
   
(3)
3.2. Study 2: Ordowich et al.
Jarmin faced several limitations. He was only able to measure whether or not an
establishment received MEP services between 1987 and 1992. Data on the level and type of
treatment were not of sufficient quality for his analyses. Likewise, his study was situated in a period
before nationwide establishment of the MEP, when there were only a few centers funded. To extend
the Jarmin modeling effort, Ordowich et al. (2012) studied the effect of MEP services on labor
productivity and other outcome variables. Their study used new data to run a number of additional
OLS models replacing the binary treatment variable in the equations above (Exti) with a variety of
other measures. The treatment measures used included level of treatment (e.g. number of
engagements, cumulative hours of MEP assistance, cumulative dollar amount paid by establishments

8
for assistance), period of treatment, and type of treatment (e.g. delivery mode, type of assistance
received, and substance of assistance received).
Ordowich et al. (2012) used three databases: one with MEP client engagements occurring
between 1997 and 2002, another with demographic information for each client, and a third dataset
with MEP center characteristics. While complete data on MEP engagements only dates back to 1999,
the Ordowich et al. (Ibid.) study included all data available on MEP engagements between 1997 and
2002 to capture as many MEP clients as possible. Their study found about 47,000 engagements in
the MEP database that were delivered to about 20,000 unique establishments between 1997 and
2002. Their modeling approaches included difference-in-differences (DiD) models and lagged
dependent variable models to estimate the relationship between manufacturing extension and labor
productivity. The DiD model for panel data is:
 
log log 1 log( )
it it
it
it it
tit it
YK
Ext L
LL
    
   
   
   
   
          
(4)
The lagged dependent variable model for panel data is:
 
1
1
log log 1 log( ) log
it it it
it it it
it it it
Y K Y
Ext L
L L L
     


     
     
     
     
      
(5)
Their results were mixed and suffered from several data limitations. As discussed in Angrist
and Pischke (2009), the ideal situation is to estimate a fixed effects model with a lag term.
5
However,
as the Ordowich et al. study correctly pointed out, without stronger assumptions and more data,
5
Angrist and Pischke (2009), p. 245.

9
such a combined model may lead to inconsistent estimates.
6
In addition, the Ordowich et al. study
tried several different instruments based on MEP center locations and other methods to correct for
selection bias (such as propensity score matching), but none of these methods was correlated with
the likelihood of an establishment being an MEP client in the same way that Jarmin (1999) reported.
This current study, commissioned by NIST MEP in 2012, builds on the Jarmin and
Ordowich et al. studies by examining these relationships across three different years of Census of
Manufactures (CMF) data (1997, 2002, and 2007). This broader timeframe enables us to overcome
Jarmin’s focus on the pre-MEP extension period and Ordowich et al.’s focus on the recession-
tainted 1997–2002 time period. Furthermore, this project builds on the results from these two
models to bracket the effect of MEP services on labor productivity and output growth.
7
4. DATA AND METHODS
The present study extends the analysis to the CMF for 2007, providing an additional period
for observing productivity changes. The focus is on establishments that received MEP services
between 1997 and 2007 in two time periods: 1997–2002 and 2002–2007. The unit of analysis for all
analyses is the manufacturing establishment.
6
To estimate a model with both differences and a lag, one must have data from more than two time periods and assume
that error terms are only correlated across adjacent time periods.
7
Angrist and Pischke (2009), p.246-247.

10
4.1 DATA
4.1.1 NIST MEP Program Data
As an initial step, we processed data on every establishment that received MEP services from
1997 to 2007 from the NIST MEP program. Most of these elements are contained in the NIST
MEP Project Information File (PIF).
8
For each MEP client, we received a project-level record
containing various data elements, including unique IDs, project titles, the period MEP assistance was
received, delivery mode, the type of assistance received, the number of MEP staff hours spent on
the project, and the cost of the services provided. We also received a customer-level record that
included client name, address, number of employees, and five-digit North American Industrial
Classification System (NAICS) code. In addition, we received data on the MEP centers used to
complete each project. Center-level data included the year the MEP center was started, number of
staff in each center, total funding from NIST MEP for each center, location of each MEP center,
market penetration rate for each center, and type of center (university/501(c)3/state agency).
The location of each center and auxiliary locations (e.g., other offices affiliated with the MEP
center) was intended to be used to create an instrument for dealing with the problem of “selection
bias” (in which higher productivity growth is a precondition for manufacturers that consider using
MEP services). However, this instrument did not resolve the self-selection bias in the models that
were estimated. Section 4.3.1 describes the other instruments (year of firm establishment, rurality of
the county based on firm address) that were used. The distance from each establishment to the
location of the nearest center’s headquarters and offices was used as a control variable in the survival
analysis model.
8
Using some of these variables, we created variables to use in our analyses that quantified the total number of
engagements for each establishment as well as the total number of cumulative hours of service provided by the MEP
center.

11
We initially labeled these establishments as manufacturers using the following process:
(1) created a non-duplicate establishment name list comprising 61,919 records, 55,834 of which were
“non-blank” in the “Name” field; (2) selected all establishments that had received service during the
time period under analysis, which reduced the record count to 53,647; (3) separated manufacturing
establishments from non-manufacturers based on the former’s having NAICS codes in the CIF
beginning with 31, 32, or 33 (for those lacking NAICS codes, we looked them up in Dun &
Bradstreet’s Million Dollar Database and Reference USA); (4) reviewed the list of manufacturers and
removed any that had manufacturing NAICS codes but were clearly not manufacturers (this was a
manual process); (5) reviewed the list of non-manufacturers and added back any that appeared to be
manufacturers (this also was a manual process, which found a particular clustering of what were
actually manufacturers identified as having the NAICS code of “11111”); and (6) linked the resulting
list to the PIF data about MEP projects. The resulting database had 38,067 manufacturers served
from 1997 to 2007 that received an average of 3.3 project-based assists over this time period.
4.1.2 Census Administrative Data
Then, we accessed three databases through the Census Research Data Center (RDC) in
Atlanta, Georgia, after securing approval from the U.S. Census Bureau and the Internal Revenue
Service (IRS) to proceed with this study. These databases were the Standard Statistical Establishment
Listing (SSEL), Longitudinal Business Database (LBD), and the Census of Manufactures (CMF).
Because 1997 is the first year of our study, NAICS codes were used and there was no need for
Standard Industrial Classification (SIC) code information for establishments. The Annual Survey of
Manufactures (ASM) also was not used because small and midsize manufacturers are not fully
represented in this database.

12
4.1.2.1 Standard Statistical Establishment Listing (SSEL) and Matching
The SSEL contained many data elements for all establishments listed in the Business
Register (BR). For this analysis, we used data elements such as EIN, Legal Form of Organization,
NAICS code, State, County, Business Name, Mailing Address, and ZIP Code for all establishments
listed in the Business Register between 1997 and 2007.
The project linked Census Bureau data to the MEP business assistance recipients. This was
done using fuzzy logic code in the R programming software to match each establishment in the
MEP data set to a unique establishment identifier in the SSEL. Generally, the researchers removed
certain characters, such as commas, ampersands, slashes, and periods to leave only letters in the
address field. Then, after standardizing common features such as “street”, which may appear as “St.”
or “Street” or “St”, as well as other features such as “road” and “avenue”, the algorithm sought
matches on combinations of establishment name and address to obtain the highest quality and
number of matches. The R matching code enabled real-time review of individual matches. This
process took from April 2014 to August 2014. During our review process, we observed that any
record with a fuzzy matching score below 90% was likely not a true match. This high, but accurate,
threshold resulted in a match rate of 20% (approximately 7,500 establishments). This match rate is
similar to that in the Ordowich et al. study in terms of the number of MEP client establishments
(7,737 MEP client establishments) that were matched in both the LBD and the CMF datasets.
Indeed, after a visual inspection of the matched data at the 90% and lower scores, we are very
comfortable with the quality of the matches using the 90% score as the threshold vis-à-vis some
other score threshold.
9
Nevertheless, we acknowledge that it is highly likely that unmatched MEP
9
This low match rate calls for future collaboration between the Census Bureau, NIST MEP, and outside researchers to
address recordkeeping and other data elements that facilitate accurate matches across time.

13
clients exist in the control group (non-MEP clients), which means our results would be biased
towards zero.
As we expected, the employment size distributions differed between matched and
unmatched MEP clients. Looking only at the universe of establishments that received MEP services
(from the raw MEP records), we find that the large majority of establishments have fewer than 250
employees. We subsequently used the unique establishment identifiers in each file (i.e., the Census
file number and LBD number) to link the MEP business assistance records to the CMF and LBD
data sets. As a result of the linkage of multiple data sets, we are left with approximately 7,500
matched MEP client establishments. For this subset, we see in Table 1 that 71% of matched MEP
clients fell into three employment categories (20–99, 100–249, and 250–499). By contrast, we see
that non-clients were more concentrated among smaller establishments, particularly the 1–19
employment category. Table 1 shows the distribution of MEP clients and non-clients, from the pool
of matched establishments, across different employment size categories as well as the distribution of
MEP clients across employment size categories before any matching occurred. That the distribution
of establishments across employment size categories is somewhat consistent across the raw MEP
records and the matched MEP clients suggests that the matching algorithm did not discriminate in
favor of any particular size of establishments.
[INSERT TABLE 1 ABOUT HERE]
4.1.2.2 Census of Manufactures
The CMF includes all establishments in the manufacturing sector in years ending with a “2”
and a “7”. The most recent CMF data available for analysis at the Census Bureau at the time of our
initial proposal to the Census Bureau were from the 2007 Census. Our analyses used data from the
1997, 2002, and 2007 CMF. The key variables obtained from the CMF include EIN, Legal Form of

14
Organization, NAICS code, State, County, Total Employment, Number of Production Workers,
Total Value of Shipments, Value-Added, Total Capital Expenditures, and Salaries and Wages.
The primary use of the CMF data was to provide key information on the establishments that
was needed for the analysis, such as total employment (full-time equivalent or FTE), number of
production workers, value-added, and capital expenditures (used in the capital to labor ratio). To
meet Census Bureau disclosure requirements, the TVS variable was kept to conduct all disclosure
analyses to enable release of the results from the Atlanta Census Research Data Center (RDC).
4.1.2.3 Longitudinal Business Database
The LBD comprises information to enable access to prior CMFs. This database was used to
link to information from CMFs in 1997, 2002, and 2007. The project also used the LBD to link
establishments across time to analyze survival as well as changes in key variables (such as sales
growth), to obtain a measure of the establishment’s age, and to identify establishments that are part
of single-unit or multi-unit firms. LBD variables used included EIN, First Year Establishment is
Observed, Last Year Establishment is Observed, and Single-Multi Unit Identifier.
Figure 1 illustrates the linkages between each of these datasets.

15
[INSERT FIGURE 1 ABOUT HERE]
4.2 VARIABLES
The information in these MEP and Census Bureau databases was used to calculate variables
to be used in our analyses. These variables are classified as either outcome, treatment, or control
variables. Each variable and how it is calculated is described in Table 2. All dollar values were
converted into 2007 dollars using the Consumer Price Index (CPI-U) for All Urban Consumers.
[INSERT TABLE 2 ABOUT HERE]
4.3 MODELS
Our analyses examine changes in productivity as a function of other variables and MEP
assistance. In replicating and enhancing the analyses of the effect of MEP services on establishments
performed by Jarmin (1999) and Ordowich et al. (2012), we encountered many of the same issues,
including selection bias, the possibility of different methodologies giving us mixed results, limited
time coverage, an overemphasis on quantitative measures of productivity, sales, and employment
numbers that do not fully capture the effect of MEP in recessionary or slow economic growth
periods. In addition to replicating the prior analyses, we performed new analyses (e.g., survival
analysis) and considered additional CMF data (2007) that had been previously unavailable. The
models we utilize in this evaluation are described below.
4.3.1 Controlling for Selection Bias
Generally, several interrelated issues need to be addressed when evaluating the effect of
MEP services on establishment outcomes. First, establishments are likely more heterogeneous in
terms of their characteristics than can be captured by a single-line ordinary least squares (OLS)
regression equation. Second, selection bias occurs because establishments are not randomly assigned

16
to the treatment and control groups; establishments select whether or not to become MEP clients.
Jarmin (1999) found that companies with high sales growth but lower than average productivity self-
select into the group of MEP clients.
To control for self-selection bias, both Jarmin (1999) and Ordowich et al. (2012) used a
Heckman two-stage model, which is also commonly referred to as an instrumental variables (IV)
approach. For his instrument, Jarmin (1999) used a dummy variable to indicate whether or not an
establishment is in an MSA with a manufacturing extension center. Ordowich et al. (2012) used a
similar variable. The instrument was successful at controlling for self-selection bias in the Jarmin
(1999) study (as it was correlated with client standing), but not in the Ordowich et al. (2012) study.
The current study also estimates an instrumental variable model using the age of the
establishment and the 2003 USDA-ERS Rural-Urban Continuum Code (Ruralityi) as instruments.
The latter instrument ranges from 1 (counties with 1+ million population) to 9 (completely rural
counties with less than 2,500 population, not adjacent to a metro area). These instruments are
correlated with the likelihood of an establishment being a client but are not correlated with labor
productivity growth. In early testing, we also considered distance to the nearest MEP center as an
instrument, but that variable failed to control for self-selection bias as it did not sufficiently
distinguish client standing. We did use this variable in the survival analysis model to account for
center effects based on distance from the closest MEP office alone. Table 3 shows, for matched
establishments, the distribution of MEP clients and non-clients across the various rurality
classifications.
[INSERT TABLE 3 ABOUT HERE]
In preliminary analyses, the Heckman correction for selection bias produced mixed results.
For the DiD regression model that examined productivity differences between 2002 and 2007, the
Heckman correction did not produce more efficient estimates. In other words, the instruments we

17
used in the Heckman selection model (to handle the possible selection bias) did not make a
statistically significant difference in the impact of MEP services on productivity differences.
Therefore, we present the results of the OLS regression model below. However, for the DiD
regression model estimated on the 1997 to 2002 period, the Heckman correction did produce more
efficient estimates, but the coefficient on extension services was negative and significant, which is
consistent with the Ordowich et al. (2012) study’s finding.
We anticipated finding and applying instruments that would control for selection bias. The
age of the establishment and rural/urban location are correlated with client status, but not correlated
with productivity growth, so presumably they would be good instruments. However, we had mixed
success in applying them. Drawing on prior instruments used to control for selection bias, as well as
using other instruments in this study, we were unable to find a single instrument that controlled for
selection bias across all of the years in the study. Table 5 in the next section will show that MEP
assisted clients had higher productivity (as measured by value-added per employee) and employment
than non-clients, suggesting that the selection bias is a positive one. Future research could make
progress on this part of the analysis by trying additional instruments, including instruments built on
served and unserved establishments in the same firm or enterprise group. Nonetheless, we proceed
with this study by estimating the impact of the variable of interest, receipt of MEP assistance, on
productivity growth measures in an OLS framework.
4.3.2 Difference-in-Differences (DiD) Model
First, we replicated the DiD model in the Ordowich et al. (2012) study by re-estimating
Equation 4. This model controls for time-invariant characteristics of each establishment. This
includes both observable factors such as industry and location as well as unobservable factors such
as management ability (Mundlak, 1961). This model is estimated for two changes in productivity

18
(1997–2002, 2002–2007) for the set of continuing establishments as well as subsets of the data,
including five different employment groups as well as different NAICS sectors. For establishments
that survive through all three periods, this analysis tells us the differential impact of being served by
the MEP in one of these two 5-year periods. We also consider productivity differences by
employment size, industry, and substance of assistance and report on the use of instruments to
address selection bias.
4.3.3 Lagged Dependent Variable (LDV) Model
Second, we replicated the lagged dependent variable model that was also used in the
Ordowich et al. (2012) study by re-estimating Equation 5. With this model, variation in labor
productivity in a given time period is expressed as a function of contemporaneous capital to labor
ratios, contemporaneous employment, and labor productivity in a previous period. This model is
estimated to show the degree to which estimates of the impact of MEP assistance on establishment
productivity are validated by a different modeling approach. While the DiD model controls for the
aforementioned time-invariant attributes of establishments, the LDV model accounts for baseline
differences in productivity between served and unserved manufacturers and controls for the
likelihood that the outcome variable is correlated over time (Angrist and Pischke 2009; Ordowich et
al., 2012).
4.3.4 Survival Analysis Models
Survival analysis seeks to provide information on the factors that influence whether or not
establishments survive from one period to the next. Survival analysis has been used to study a range
of effects, from student attrition rates in universities to firm attrition rates from year to year. The
basic goal is to estimate the shape of the hazard function for the underlying survival process of, in

19
this research, manufacturing firms. We used two different models (Cox proportional hazards model
and logit model) to test whether the receipt of MEP services increases the likelihood of survival
from one time period to another.
In this study we tested very specific hypotheses about the characteristics of establishments
that survive from period to period using the Cox proportional hazards model, with one of those
characteristics being whether or not an establishment received MEP services. The Cox proportional
hazards model requires the creation of two special variables: 1) a duration variable denoting the
length of time a firm used MEP services (in years) and 2) a dichotomous variable denoting whether
the endpoint is censored or not. CENSORED = 0 if the firm continued to use MEP services by
2007 or CENSORED = 1 if the firm stopped using MEP services by 2007. One limitation of using
data in this format is that we cannot analyze “time-varying covariates” as a researcher might do
using panel data.
The Cox model estimates a hazard function
 
kki xxthth

 ...exp)()( 110
, where i
references each firm observation and
)(
0th
is the baseline hazard (that measures the value of the
hazard function common to each firm before the other risk factors x are taken into account). The
hazard function can be rewritten in its familiar log form:
kki xxthth

 ...)(log)(log 110
. (6)
In essence, this function tells us the aspects of firms that make an exit from the sample more or less
likely in a given time interval. Using the method of maximum likelihood, the Cox model maximizes
the Hosemer and Lemeshow (1989) partial log-likelihood function:
 
  
   

















 D
j Dk Ri ijk
j j
xdxL 1expln

. (7)

20
The second model we used to test the likelihood of survival is the logit model, which
estimates the probability of survival from one period to the next conditioned on a set of predictor
variables. Mathematically, the logit model is written in its most familiar form as 
 . For
both the Cox model and the logit model, the dependent variable is coded (0, 1), where 0 indicates
establishment survival between the two periods and 1 indicates an establishment’s death. This
operationalization, while counterintuitive compared to traditional OLS structures, is typical of
survival analyses and facilitates the interpretation of odds ratios less than one in the Cox model as
establishments having a lower probability of death, ceteris paribus. In the logit model, this
operationalization facilitates the interpretation of an establishment’s probability of death as
increasing (positive coefficient) or decreasing (negative coefficient).
5. RESULTS
Our results begin with descriptive statistics of the primary variables used in the analysis.
These are presented in Table 4. Note that the number of observations is rounded to the nearest
thousand to satisfy Census Bureau disclosure requirements.
10
[INSERT TABLE 4 ABOUT HERE]
Next, we conducted difference of means tests (using the student’s t-statistics) of the
differences in value-added, employment, and productivity between MEP clients and non-clients.
MEP-assisted manufacturing establishments had higher levels of value-added and employment than
non-clients (Table 5). These differences were significant at p < .05 with the exception of value-
added per employee in 2002 and 2007.
10
Some tabular and model details in subsequent parts of this section were not able to be released through the Census
Bureau disclosure process. These are summarized in more general models or in text only.

21
[INSERT TABLE 5 ABOUT HERE]
5.1. DiD Model
We replicated the DiD model used by Jarmin and Ordowich et al. over the 1997–2002 and
2002–2007 time periods. The specific model we used included more covariates than Equation 6.
Specifically, we included controls for the age of the establishment – relating back to the focus of the
program on established as opposed to startup manufacturers (Shapira et al., 2015)— and two
industry class dummy variables to represent durables and nondurables based on these establishments’
NAICS codes. This industry specification takes advantage of prior work into the greater productivity
of durable as opposed to non-durable manufacturers in certain business cycles (Kehrig, 2011). The
results show a positive but statistically insignificant coefficient for the extension variable in the
2002–2007 period (Table 6).
11
Receiving MEP services between 2002 and 2007 is associated with 1.0
percent higher productivity (value-added per employee) growth compared to non-clients (in the DiD
model), although again this result is not statistically significant at the 5 percent level. Other significant
predictors of the change in logged value-added per employee (VA/EE) are the capital to labor ratio,
the number of production workers, establishment age, and whether an establishment is located in a
more urban or rural county.
[INSERT TABLE 6 ABOUT HERE]
We validated these results by using the same independent variables to explain variation in
two different dependent variables: changes in the logged sales per production worker and changes in
employment. We found a significant and positive impact of the extension variable on the natural log
11
In interpreting this statistically insignificant result, keep in mind the possibility that some treated firms may
erroneously be included in the control group, leading to a downward bias in the absolute value of the coefficients.

22
of sales per production worker, ceteris paribus (Table 6). Receipt of MEP services in the 2002-to-2007
time period is associated with 2.6% higher sales per employee compared to non-clients. Also, we
found a statistically significant and positive impact of MEP assistance on the natural log of
employment (results not released). When we estimated the same model for the 1997–2002 period,
we observed similar results as the Ordowich et al. (2012) paper reported (that productivity was
statistically significant and lower for MEP customers). In sum, these findings suggest a level of
consistency that enhances the reliability of their DiD results.
We tested the hypothesis of whether the sign and significance of the extension variable vary
by the size or subsector of the manufacturing establishment. We estimated the same model
specification across various subsets of our data. First, we divided establishments into the following
size classes: Group 1 = 1 to 19 employees; Group 2 = 20 to 99 employees; Group 3 = 100 to 249
employees; Group 4 = 250 to 499 employees; and Group 5 = 500 or more employees. Table 7
shows the impact of the extension variable on change in value-added per employee from 2002 to
2007 across the various groups based on total number of employees. The extension variable shows
mixed results at this level of disaggregation: 3.0% growth for Group 1 and 0.3% growth for Group 2
versus -6.3% for Group 3, -7.2% for Group 4, and 0.2% for Group 5, although only the Group 3
and 4 coefficients are significant at the 95% level of confidence. These results can be loosely
interpreted as MEP services having the greatest effect on productivity for smaller establishments
which, presumably, have fewer other alternative activities (e.g., other consulting activities, other
activities aimed at increasing productivity) that affect outcomes. By contrast, larger manufacturers
likely have other influences on manufacturing performance that could crowd out the effects of MEP
services. As pointed out by a reviewer, one caveat of these results is that in 2012, establishments
with 1-19 employees accounted for approximately 78% of manufacturing establishments but only

23
9% of manufacturing employment and 4% of manufacturing value-added. Even if MEP assistance
doubled the productivity of client establishments, the impact on overall productivity would be minor.
[INSERT TABLE 7 ABOUT HERE]
To examine differences by industry group, we divided establishments into a Durables
subgroup (NAICS 33 sector) and a Non-Durables subgroup (NAICS 31 and 32 sectors). Based on
the durable/non-durable bifurcation, we found significant differences in the impact of MEP
assistance on productivity. Specifically, the coefficient on the extension variable (MEP assistance)
was positive and significant at the 95% level of confidence for durables manufacturers and negative
and insignificant, at the same confidence level, for non-durables (Table 8). Durables manufacturers
receiving MEP services had 3% higher growth in value-added per employee than non-clients in
these industries over the 2002-to-2007 time period. These results are consistent with the positive
productivity typically associated with durable, as compared with non-durable, establishments in
certain business cycles and suggest that MEP assistance may serve to enhance these advantages
(Kehrig, 2011).
[INSERT TABLE 8 ABOUT HERE]
We also examined differences in manufacturing productivity based on the type of
manufacturing assistance provided. We grouped MEP substance codes into two categories to reflect
a “top-line” (sales increasing) orientation versus a “bottom-line” (cost savings) orientation. The
“top-line” substance group comprises business services and engineering/technical services; the
“bottom-line” substance group comprises quality systems, manufacturing systems, information
technology, and human resources and organizational development. We acknowledge that some
bottom-line activities may spillover into the top-line activities and vice versa. However, because of
the breadth of the substance codes used in the PIF, we judged that these groupings best proxy the
differences in the two orientations. We subsequently weighted these two categories by the number

24
of hours of effort associated with engagements in these categories and normalized the results by
dividing by the total number of hours (because of variation in the number of hours of assistance
across clients). We use this method to account for the common situation where MEP clients receive
multiple types of services over the course of the period under study. Thus, the variable reflects the
emphasis of the service in one substance category (versus another substance category) rather than a
binary condition of selecting into (or not selecting into) a single substance code.
We then incorporated these variables into the DiD regression model covering the 2002–
2007 time period in lieu of the extension variable. The “top-line” substance variable had a positive
and statistically significant impact on the change in value-added per employee; the “bottom-line”
variable had a negative and statistically insignificant impact on the change in value-added per
employee. These results are not inconsistent with what might be expected. These results do not
imply that “bottom-line” assistance should be eschewed; some firms may have a great need for it,
including as an entry-level service (National Research Council, 2013). Although “bottom-line”
assistance contributes to reducing the cost of goods and services, which is a component of value-
added, “top-line” assistance may augment the sales component of value-added more directly.
5.2 LDV Model
To validate the results of the DiD model, we also estimated the LDV model as was done in
the Ordowich et al. (2012) study. The same covariates used in the DiD model were used in the LDV
model, with the lagged version of the dependent variable being the only additional independent
variable used in the model. For example, when the dependent variable is the natural log of labor
productivity for 2002, the lagged variable is the natural log of labor productivity for 1997. In the
other model for the 2002–2007 period, the dependent variable is the natural log of labor
productivity for 2007 and the lagged variable is the natural log of labor productivity for 2002.

25
The results of the LDV model are fairly consistent with those of the DiD model and are
displayed in Table 9. Generally, in the LDV model spanning 1997–2002, MEP clients have
statistically significant and positive productivity levels compared to non-clients overall (across all
employment groups) and for the smallest employment levels (1 to 19 employees). Receipt of MEP
services is associated with a 1.9% growth in value-added per employee (in the LDV model) during
this period across all manufacturing categories, with a 5.3% growth for clients in the 1-to-19
employment range. This result is consistent with the Ordowich et al. evaluation done across the
same time period. The independent variables used in these models are Ext, ln(K/L) in period t,
ln(Y/L) in period t-1, Estab_age, Rurality, and MinDist, which are defined in Table 2.
For the period spanning 2002–2007, the LDV model suggests that MEP clients have
statistically significant and positive productivity levels compared to non-clients for the 1–19
employee establishments; having received MEP services in this size class is associated with a 3.4%
growth in value-added per employee in the 2002-to-2007 time period. However, the sign for the
MEP assistance variable changes to negative (while remaining statistically significant) for
employment groups 100–249 and 250–499 employees. These results are quite consistent with the
results of the DiD model results displayed in Table 7.
[INSERT TABLE 9 ABOUT HERE]
5.3. Survival Analysis
The Cox model is constrained to follow the proportional hazards assumption (which means
that the hazard ratio is constant across time, not across observations). We confirmed in our
preliminary testing that the data do not violate this assumption, which means there was no
significant difference in the rate of change in the survival probabilities over time between
establishments as a whole. However, the evidence suggests that there were significant differences in
the survival probabilities for establishments that did and did not receive MEP services in the 1997–

26
2007 period. Both models that were estimated come to the same conclusion about establishment
survival being positively influenced by receipt of MEP assistance.
The results of the Cox model showed that establishments receiving MEP services had a
significantly higher likelihood of surviving (i.e., MEP extension services had a significant and
negative impact on establishment death rates). Put another way, MEP assistance increased the
survival probabilities of establishments from 1997 to 2007. As for subsets of the data, extension
services did not improve survival probabilities in the 1997 to 2002 period, which is consistent with
what we observed in the productivity equation. But, extension services did improve survival
probabilities in the 2002 to 2007 period. Using the results of the Cox model in Table 9 (assuming a
Weibull distribution), the hazard ratio of 0.82 suggests that MEP client establishments are 18% less
likely to die compared to non-clients and controlling for other factors. Also, the logit model results
reinforce the Cox model results. The logit coefficient of -0.54 suggests that MEP client
establishments have a lower probability of death relative to non-clients. Therefore, the increased
survival of establishments receiving MEP services survives different econometric specifications,
summarized in Table 10, across the 1997 to 2007 period.
12
[INSERT TABLE 10 ABOUT HERE]
6. CONCLUSIONS
Overall, the results portray a nuanced picture of the ways in which MEP services impact
productivity, sales, employment, and establishment survival when compared with a matched non-
client control group. This study considers the program as a whole and does not examine differences
12
The variable that measures the minimum distance to an MEP office was operationalized using a SAS routine that
computes latitude and longitude coordinates. We then generated the minimum distance between an establishment and
the nearest MEP office using the standard Haversine formula, which accounts for the circular nature of geographic
distance.

27
among centers. Potentially, the methodology could be applied to center-level comparisons, although
the results would be less robust, as was shown in Ordowich et al. (2012), in part because of the
smaller number of observations and client matches at the center-level.
We find, regardless of econometric model specification, that MEP clients with 1 to 19
employees have statistically significant and higher levels of labor productivity growth than non-
clients in this employee size range. In contrast, the extension variable is not positively associated
with higher productivity growth for MEP clients with 20 or more employees, and the coefficients on
the extension variable are negative and significant for the largest MEP clients. Establishments in the
medium-size employment category are often targeted as the most appropriate for MEP services,
whereas the findings from this study—as well as the earlier Ordowich et al. (2012) study—
underscore the MEP’s greater propensity to affect positive change in smaller establishments.
We observed significant productivity differences associated with MEP services by broad
sector, with higher impacts for 2002–2007 in the durable manufacturing sector than in the non-
durable manufacturing sector. One interpretation of this result is that MEP services may be
particularly oriented to durable goods establishments such as machine shops, component suppliers,
and other durable products manufacturers. Under this interpretation, it might not be surprising that
these types of firms would be most apt to have positive growth in value-added per employee as a
result of MEP services because MEP services are most suitable for their needs. It might also be
plausible to suggest that the broader durable goods industry group fared better than the non-durable
goods industry group over this time period (2002–2007), and this higher performance spilled over
into better MEP client performance for this group. However, this interpretation is not supported by
data from the U.S. Bureau of Economic Analysis. Durables had slower growth in current value-
added per employee than non-durables during these two time periods. Change in value-added per
employee for durables for 1997–2002 was -0.3% versus 0.4% for non-durables, while for 2002–2007

28
it was 1.0% for durables versus 1.2% for non-durables.
13
Therefore, it is particularly noteworthy that
MEP clients in the durables sector saw significant positive growth in value-added per employee
given these overall industry group trends. Another possible explanation is that durables
manufacturers are best able to absorb MEP services, many of which are particularly oriented to
these types of manufacturers.
We caution that while quantitative changes in value-added or sales are important program
impact measures, many small firms cannot readily provide this information (Shapira et al., 2004).
Additionally, MEP clients’ productivity improvements may not raise overall manufacturing
productivity, as indicated by BEA data for this period, if less productive firms crowd out more
productive ones, as a general equilibrium approach might imply.
The results also show that establishments receiving MEP assistance have a statistically
significant and higher probability of survival than those that do not receive MEP assistance. The
longer-term survival of a manufacturing enterprise can also be an outcome of program intervention.
Survival is not always unequivocally positive, however, especially if a federal program supports
inefficient firms. The analyses presented here suggest that this outcome is generally unlikely in that
MEP clients had higher productivity and employment. In an era of downward or slow economic
growth, the ability to enable an establishment to be sufficiently competitive to survive as a result of
MEP services may be an additional measure of program effect.
The program theory of the MEP, as discussed at the start of this paper, posits that
intervention through supplying manufacturing assistance services will improve business performance.
This study broadly confirms this program theory, in particular finding that MEP client companies
have higher value-added per employee, greater growth in sales per employee, and greater probability
13
Value-Added by Industry. Accessed November 17, 2014 from http://www.bea.gov.

29
of survival. The qualification is that these results are most strongly positive for smaller firms than
for medium-sized firms and for firms in durables industries. In addition, the paper presented two
models (the DiD and the LDV models), which show broadly similar results, especially for smaller
manufacturers. However, for the 2002-to-2007 period, neither model shows a positive and
statistically significant coefficient for growth in value-added per employee. The reasons for this
could be changes in the economic cycle, a possible shift in the services provided by the program,
and the conceivable accumulation of repeat customers and other customers looking for services
other than productivity improvement. Examples of the latter include services to address power and
energy consumption, environmental issues, new product development, and strategic management;
the results of these services may not readily appear in gross productivity valuations (Youtie et al.,
2016). Further study to update the results of these models and to examine MEP impacts on other
business outcomes would help to provide a fuller understanding of the MEP’s results over a longer
timeframe.

30
References
Angrist, J., and Pischke, J-S. (2009). Mostly Harmless Econometrics, Princeton University Press:
Princeton, NJ.
Griliches, Z. (1996). “The Discovery of the Residual: A Historical Note,” Journal of Economic Literature,
American Economic Association 34(3): 1324-1330.
Heckman, J. J. (1976). “The common structure of statistical models of truncation, sample selection
and limited dependent variables and a simple estimator for such models,” Annals of Economic and
Social Measurement 5(4): 475-492.
Hosemer, D.W. and Lemeshow, S. (1989). Applied Logistic Regression. New York: John Wiley.
Jarmin, R.S. (1999). “Evaluating the Impact of Manufacturing Extension on Productivity Growth,”
Journal of Policy Analysis and Management 18 (1): 99-119.
Kehrig, M. (2011). The cyclicality of productivity dispersion. US Census Bureau Center for Economic
Studies Paper No. CES-WP-11-15.
Mundlak, Y. (1961). “Empirical Production Function Free of Management Bias,” Journal of Farm
Economics 43(1): 44-56.
NAPA (2003). The National Institute of Standards and Technology’s Manufacturing Extension
Partnership Report 1: Re-examining the Core Premise of the MEP Program, Washington, DC:
National Academy of Public Administration.
National Research Council. (2013). 21st Century Manufacturing: The Role of the Manufacturing Extension
Partnership. Board on Science, Technology, and Economic Policy. Washington, DC: The
National Academies Press.
Ordowich, C., Cheney, D., Youtie, J., Fernandez Ribas, A., and Shapira, P. (2012). Evaluating the
Impact of MEP Services on Establishment Performance: A Preliminary Empirical Investigation. U.S. Census
Bureau Center for Economic Studies Paper No. CES-WP-12-15.

31
Shapira, P. Youtie, J., Wang, J., Hegde, D., Cheney, D., Franco, Q., and Mohapatra, S. (2004).
Assessing the Value of Information and its Impact on Productivity in Small and Midsize Manufacturers.
Georgia Institute of Technology and SRI International.
Shapira, P., and Youtie, J. (2014). Impact of Technology and Innovation Advisory Services. Nesta Working
Paper 13/19. London: Nesta.
Shapira, P., Youtie, J., Cox, D., Uyarra, E., Gök, A., Rogers, J., & Downing, C. (2015). Institutions
for Technology Diffusion. Washington DC: Inter-American Development Bank.
Solow, R. (1957). “Technical Change and the Aggregate Production Function,” Review of Economics
and Statistics 39: 312-320.
U.S. Bureau of Economic Analysis. (2014). Value-Added by Industry. Accessed at
[http://www.bea.gov/industry/gdpbyind_data.htm]
Youtie, J. (2013). “An Evaluation of the MEP: A Cross-Study Analysis,” Appendix B, pp. 390-427,
in: National Research Council (2013). 21st Century Manufacturing: The Role of the Manufacturing
Extension Partnership. Board on Science, Technology, and Economic Policy. Washington, DC:
The National Academies Press.
Youtie, J., Shapira, P. Li, Y., and Dodonova, D. (2016). Innovation in Manufacturing: Needs,
Practices, and Performance in Georgia 2016-2018. Retrieved from http://gms-ei2.org/wp-
content/uploads/2016/09/GMS-2016-report-of-surveyreduced.pdf.

32
Figure 1. Datasets and Links
Business Assistance Data
MEP
Unique establishment IDs
Establishment name
Establishment address
Number of employees
NAICS five
-
digit code
Title of project/event
Period of assistance
Delivery mode
Substance of assistance
Type of assistance
Assistance center hours
Affiliate hours
Cost of business assistance
Unique business assistance center ID
Standard Statistical
Establishment Listing
(Name and Address File)
Unique establishment identifier
Establishment name
Establishment address
Census of Manufacturers
Unique establishment identifier
Number of employees
Number of production workers
Value added
Total cost of materials
Total Value of Shipments
Cost of Goods Sold
Cost of Materials, Cost of
Materials and Parts
Cost of Purchased Services
Total Capital Expenditures
Salaries and Wages
NAICS five
-
digit code
Geographic area codes
Longitudinal Business
Database
Unique establishment identifier
Census file number
Employer Identification number
Longitudinal database number
Multiunit identifier
Matched
using fuzzy
logic code,
R software
Linked
using
establish
-
ment
identifier

33
Table 1. Distribution of Matched MEP Clients and Non-Clients by Employment Size (2007)
Employment Size Category
Matched
MEP Clients
Non-Clients
Raw MEP Records
1 to 19 employees
25%
70%
31%
20 to 99 employees
46%
22%
37%
100 to 249 employees
19%
6%
18%
250 to 499 employees
6%
2%
9%
500 or more employees
4%
1%
5%
Note: MEP clients, N > 7,500; Non-MEP clients N > 300,000; MEP records N = 39,349

34
Table 2. Variables Used in the Analyses
Variable
Description
Source
Calculation
Outcome Variables
it
it
Y
L
Value-added per employee
at establishment i in period t
CMF
 Y: Value-added in 2007
dollars
 L: number of production
workers


Sales per employee at
establishment i in period t
CMF
 Y: Value of shipments in
2007 dollars
 L: number of production
workers
Surv1
Firm survival from one
period t to period t+1
LBD
Coded as 1 if establishment is
not operating and 0 if
establishment is operating
(used in Cox model)
Treatment Variables
,,i X Y Z
Ext 
Binary variable for whether
a plant received MEP
services between years X
and Y14
NIST-MEP
Coded as 1 for received
services (all records from
NIST MEP) and 0 for all
other establishments (control
group from LBD/CMF)
Substancei
Substance of MEP
treatment
NIST-MEP
Calculated based on summary
statistics by MEP center
,,i X Y Z
CumHours 
The cumulative hours of
services received by
establishment i between
years X and Y on service
type Z
NIST-MEP
Aggregated from NIST MEP
data based on number of
hours (center/affiliate)
devoted to services of
specified type over the time
period of interest
Control Variables
it
L
Number of employees and
production workers at
establishment i in period t
LBD/CMF
Total number of employees;
total number of production
workers
it
it
K
L
Capital to labor ratio for
establishment i in period t
CMF
 K: total capital expenditures
expressed in 2007 dollars
 L: number of production
workers
NAICSi
The five digit NAICS code
for establishment i, coded
into dummies for durables
and nondurables
CMF
Directly from database; also
computed 2-digit sector
identifier and 3-digit subsector
identifier
14
Analysis by different time periods conducted to gauge how results change over time. Variables X and Y cover
different time periods (1997 to 2002, 2002 to 2007, and 1997 to 2007).

35
Variable
Description
Source
Calculation
MinDistij
A continuous variable that
measures the distance of
establishment i to nearest
MEP center j
SSEL/
NIST MEP
Use address from SSEL and
center data from NIST MEP
to identify the closest center
to each establishment in the
dataset
InitProdi
Productivity of
establishment i in 1997
CMF
Value-added in 1997 for
establishment i
PrevSalesi
Previous (1992–1997) sales
growth of establishment i
CMF
Use CMF data from 1992 to
1997 to calculate previous
sales growth
InitCapInti
Capital intensity level of
establishment i in 1997
CMF
Capital to labor ratio for
establishment i in 1997
Emp_groupi
Dummy variables for
different plant sizes
(number of employees) of
establishment i in 2007
LBD
Number of employees in 2007
in categories
Instrumental Variables
Ruralityi
Rural-Urban Continuum
Codes, 2003, (1 = counties
with 1+ million population,
…, 9 = completely rural
counties with less than 2,500
population, etc.)
US Department
of Agriculture,
Economic
Research Service
Calculated based on county in
address data in SSEL
Estab_ageit
Age of plant i in period t
LBD
Calculated as 2011 minus
commencement date of
establishment

36
Table 3. Matched Establishments, by MEP Clients and Non-Clients, Across the 2003
USDA-ERS Rural-Urban Continuum Codes
Continuum
Code
Description
Did Not
Receive MEP
Services
Did Receive
MEP
Services
1
Counties in metro areas of 1 million
population or more
53%
39%
2
Counties in metro areas of 250,000 to
1 million population
18%
24%
3
Counties in metro areas of fewer than
250,000 population
9%
12%
4
Urban population of 20,000 or more,
adjacent to a metro area
5%
8%
5
Urban population of 20,000 or more,
not adjacent to a metro area
2%
3%
6
Urban population of 2,500 to 19,999,
adjacent to a metro area
6%
8%
7
Urban population of 2,500 to 19,999,
not adjacent to a metro area
3%
5%
8
Completely rural or less than 2,500
urban population, adjacent to a metro
area
1%
1%
9
Completely rural or less than 2,500
urban population, not adjacent to a
metro area
1%
1%
Note: Cells are rounded, therefore columns may not sum to 100%. Although 2013 codes are
available, we used 2003 codes because they fell within the time frame of our analysis.

37
Table 4. Descriptive Statistics
Number of
Observations
Mean
Standard
Deviation
Value-Added Per Employee in dollars,
2002
360,000
98
645
Value-Added Per Employee in dollars,
2007
339,000
110
506
Sales Per Production Worker in dollars,
2002
356,000
306
2,718
Sales Per Production Worker in dollars,
2007
335,000
354
1,905
MEP Client (Yes/No)
654,000
0.01
0.1
Distance to Nearest MEP Center in
miles
542,000
51
65
Establishment Age in years
452,000
19
11
Number of Production Workers, 2002
371,000
29
209
Number of Production Workers, 2007
345,000
30
904
Capital to Labor Ratio, 2002
356,000
19
4,865
Capital to Labor Ratio, 2007
335,000
12
214
Rural-Urban Continuum Code
651,000
2.26
1.91

38
Table 5. Difference of Means Test Results
MEP Client
Status
Value-Added (VA)
Employment (TE)
Value-Added per
Employee (VA/EE)
Year
1997
2002
2007
1997
2002
2007
1997
2002
2007
Non-MEP Client
$4,759
$5,375
$8,120
43
43
43
$88
$101
$125
MEP Client
$13,087
$14,645
$21,185
121
112
111
$106
$114
$133
T-statistic
-11.83
-11.41
-9.74
-27.07
-6.82
-3.32
-7.06
-1.48
-1.04
Prob. (2-tail test)
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.13
0.29
N(mepcust=0) –
rounded
392,000
349,000
294,000
395,000
365,000
339,000
392,000
348,000
293,000
N(mepcust=1) –
rounded
5,000
5,600
4,900
5,100
5,800
5,000
5,100
5,600
4,900
Note: MEP clients, N > 7,000; Non-MEP clients N > 300,000.

39
Table 6. DiD Regression Results, 2002–2007
DV = Change in
ln(Value-Added Per
Employee)
DV = Change in
ln(Sales Per Production
Worker)
Variable Name
Coefficient
Estimate
T-statistic
(prob.)
Coefficient
Estimate
T-statistic
(prob.)
Constant
0.07
14.77
(0.00)
0.23
53.94
(0.00)
Change in ln(Capital to Labor
Ratio)
0.09
68.92
(0.00)
0.13
105.09
(0.00)
Change in ln(Number of
Production Workers)
-0.13
-46.59
(0.00)
-0.34
-111.73
(0.00)
Establishment Age
-0.002
-14.19
(0.00)
-0.006
-43.92
(0.00)
Rural-Urban Continuum Code
0.003
3.84
(0.00)
0.004
6.54
(0.00)
MEP Customer (Yes/No)
0.01
1.07
(0.28)
0.026
3.00
(0.00)
No. of Observations
(rounded)
173,000
175,000
R-Squared
0.07
0.28
F-statistic
1,759
6,295

40
Table 7. DiD Regression Results, by Employment Category, 2002–2007
DV = Change in ln(Value-Added Per Employee)
No. of Employees
All
Groups
1–19
20–99
100–249
250–499
500 or
more
No. of Observations
(Rounded)
173,000
99,000
50,000
16,000
5,000
3,000
MEP coeff estimate
0.011
0.030
0.003
-0.063
-0.072
0.002
T-statistic
1.07
1.20
0.26
-2.85
-1.89
0.05

41
Table 8. DiD Regression Results, 2002–2007, by NAICS Industries
DV = Change in ln(Value-Added Per Employee)
Non-Durable
(NAICS 31 and 32)
Durable
(NAICS 33)
Variable Name
Coefficient
Estimate
T-statistic
(prob.)
Coefficient
Estimate
T-statistic
(prob.)
Constant
0.08
11.10 (0.00)
0.06
9.69 (0.00)
Change in ln(K/L)
0.10
48.15 (0.00)
0.09
49.02 (0.00)
Change in ln(PW)
-0.16
-34.83 (0.00)
-0.11
-30.94 (0.00)
EstabAge
-0.003
-11.42 (0.00)
-0.001
-8.48 (0.00)
Continuum Code
0.004
3.42 (0.00)
0.002
1.93 (0.00)
MEP Customer
-0.029
-1.65 (0.10)
0.03
2.42 (0.02)
No. of Observations
(rounded)
78,000
95,000
R-Squared
0.08
0.06
F-statistic
924
850

42
Table 9. LDV Regression Results, by Employment Category, 1997–2002 and 2002–2007
DV = Change in ln(Value-Added Per Employee)
1997–2002
All
Groups
1–19
20–99
100–249
250–499
500 or
more
No. of Observations (Rounded)
186000
120000
48000
13000
4000
3000
MEP coeff estimate
0.019
0.053
0.012
-0.012
0.004
-0.002
T-statistic
2.330
3.290
1.080
-0.670
0.130
-0.060
2002–2007
No. of Observations (Rounded)
186000
107000
55000
16000
5000
3000
MEP coeff estimate
-0.014
0.034
-0.006
-0.077
-0.072
0.010
T-statistic
-1.650
1.930
-0.530
-3.840
-1.980
0.180

43
Table 10. Weibull and Logit Regression Estimates
Weibull Model
Logit Model
Variable
Hazard Ratio
Z-statistic
Prob.
Coefficient
Estimate
Z-statistic
Prob.
Constant
3.55E-010
-178.13
0.00
-0.01
-2.16
0.03
Estab Age
0.67
-166.70
0.00
-0.02
-70.00
0.00
VA97
1.00
1.41
0.15
---
---
---
VA02
1.00
-0.70
0.48
---
---
---
VA07
1.00
1.66
0.09
---
---
---
TE97
1.00
23.63
0.00
---
---
---
TE02
1.00
8.78
0.00
---
---
---
TE07
0.99
-37.78
0.00
---
---
---
MEP Customer
0.82
-3.92
0.00
-0.54
-18.56
0.00
Min Distance
1.00
2.83
0.01
p
9.47
Pseudo R2
0.01
1/p
0.10
N
(Rounded)
452,000
N (Rounded)
161,000
LogL
-301,235
LogL
-36,803

44
Acknowledgements
The authors gratefully acknowledge the ideas and guidance received from the Technical
Advisory Group: Ron Jarmin, U.S. Census Bureau; David Beede, U.S. Department of Commerce;
Barbara Fraumeni, Hunan University (China); and Carolyn Heinrich, University of Texas. Additional
comments were appreciatively received from Susan Helper and Cassandra Ingram, U.S. Department
of Commerce, and Kenneth P. Voytek, NIST-MEP. This project was prepared for NIST-MEP,
Contract # SB1341-11-SE-1446. The findings and observations contained in this report are those of
the authors and do not necessarily reflect the views of the Technical Advisory Group or the sponsor
(National Institute for Standards and Technology, Manufacturing Extension Partnership). Any
opinions and conclusions expressed herein are those of the author(s) and do not necessarily
represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no
confidential information is disclosed.
Author Biographies
Clifford A. Lipscomb is the Vice Chair and Co-Managing Director of Greenfield Advisors, an
economic and real estate consulting firm with offices in Seattle, WA and Cartersville, GA.
Jan Youtie is Director of Policy Research Services at the Georgia Tech Enterprise Innovation
Institute.
Philip Shapira is Professor of Innovation Management and Policy at the University of Manchester,
and Professor of Public Policy at Georgia Institute of Technology.
Sanjay Arora is a Senior Data Scientist at the American Institutes for Research in Washington, DC.
Andy Krause is a Lecturer in the Department of Property at the University of Melbourne.