Using Matched Client And Census Data To Evaluate The Performance Of The Manufacturing Extension Partnership

This paper proposes a framework for evaluating the Manufacturing Extension Partnership (MEP). The MEP is administered by the National Institute of Standards and Technology (NIST) as part of its effort to improve the global competitiveness of U.S. manufacturing industries. As the name implies, the MEP is modelled after agricultural extension. Rather than farmers the MEP's target population is small and medium sized manufacturers, generally those with less than 500 employees. The MEP currently supports 44 manufacturing extension centers around the country. These centers provide technical and business assistance for manufacturers much as county extension agents do for farmers. The goal of evaluation is to see if MEP engagements lead to positive outcomes from the view of important MEP stakeholders (e.g., MEP clients, MEP centers, NIST, state and local governments and Congress). These outcomes are discussed in McGuckin and Redman (1995) and include: Process Outcomes (e.g., adoption of a new technology by a client); Intermediate Outcomes (e.g., reduction in the clients defect rate); Business Outcomes (e.g., survival and profits) and Policy Outcomes (increases in employment,wages and/or exports). The evaluation framework described in this paper has two components. The first component is an evaluation dataset which contains measures of many of the program outcomes listed above for both MEP clients and a representative control group of non- clients. This dataset will be constructed by linking MEP client records with plant level Census data housed at the Center for Economic Studies of the Census Bureau. The Census data provides measures of several outcome and control variables which are comparable across both plants and time. The Census data include observations for all manufacturing plants in the U.S. from which representative control groups can be constructed. The MEP client records provide data on the type and intensity of extension engagements. Linking these rich sources of information yields a comprehensive and powerful dataset for MEP evaluation. The second component is an evaluation methodology which exploits this rich dataset to make statistical inferences about the impact of MEP services, while carefully controlling for other influences. By using this methodology, we can address many of the shortcomings which plagued previous attempts to evaluate extension services. In addition to evaluation, the dataset described in this paper may be used to profile the characteristics of MEP clients and compare them to non-clients. The Census data contain the complete universe of manufacturing establishments in the U.S.