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I. Economy Composition Analysis

I. Economy Composition Analysis. Query Order. Read CEW data – county and industry employment and wages (#1) If necessary – aggregate counties to study regions. (#2) Read US CEW data – industry employment and wages. (#3) Repeat Steps 1-3 for 1996 data. (#4, #5, #6).

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I. Economy Composition Analysis

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  1. I. Economy Composition Analysis

  2. Query Order • Read CEW data – county and industry employment and wages (#1) • If necessary – aggregate counties to study regions. (#2) • Read US CEW data – industry employment and wages. (#3) • Repeat Steps 1-3 for 1996 data. (#4, #5, #6)

  3. #7 - Economy Composition Query • 2006 Employment • Average Annual % Growth, 1996-2006 • Average Annual % Growth at US, 1996-2006 • Average Wages, 2006 • Location Quotient.

  4. II. Cluster Competitiveness And Growth-Share Analysis

  5. Cluster Identification • Re-create queries #1-#6, but set the Aggregation variable in CEW to pick out 4-digit NAICS, instead of 2-digit. (Queries #8-#13) • Create a query that calculates the Location Quotient of every industry in your study region, at the 4-digit level (Query #14) • Assign every high (LQ > 2) concentration industry to a cluster, or come up with a good reason why it shouldn’t be in one.

  6. Cluster Definition • Once you have a pretty good idea what clusters your high concentration industries fall into, create a more general cluster definition table. (Table #A) • This table should have one row for every 4-digit NAICS industry in one of your clusters. One column should be the NAICS industry – the other should be the cluster name.

  7. Creating the Growth Share Data • Join the cluster definition table to the employment by 4-digit NAICS query to estimate total employment by cluster in your study region. (Query #15) • Do the same thing for 1996, the US in 2006, and the US in 1996. Queries (#16-18) • Now create the growth-share query (#19), having the following fields: • Cluster name • Cluster employment, 2006 • Cluster location quotient, 2006 • Cluster differential shift, 1996-2006.

  8. Setting up the Shift -Share • Actual Industry Growth Rate = National Effect (US Total Growth Rate) + Industry Effect (US Industry Growth Rate) + Differential Shift (Local Factor) • Or, Differential Shift = Actual Growth Rate – National Effect – Industry Effect.

  9. Building the Growth Share Charts • Create the same growth share query for your comparison regions from the first half of your compendium. • You can re-use the cluster definition table, and the US employment by cluster queries for 2006 and 1996. • You just have to create new regional employment by cluster charts, 2006 and 1996, for each area. • Build one growth-share chart for all the clusters in your region. • Build 2 or 3 growth share charts showing your region and comparison areas, for clusters of your choosing.

  10. What Do the Quadrants Mean? • Lower Right – High Differential Shift, Low Location Quotient: Emerging Industry • Upper Right – High Differential Shift, High Location Quotient – a “True Cluster” • Upper Left – Low Differential Shift, High Location Quotient – Declining strength • Lower Left – Low Differential Shift, Low Location Quotient – No longer competitive

  11. III. Labor Demand and Supply

  12. Estimating Labor Demand By Cluster • 1. Download industry-by-occupation matrix (nat4d_dl) • 2. Adjust for 4-digit NAICS code • 4. Create OES total employment by industry estimate, as a query (#1) • 5. Create a second query that divides occupational employment by total employment to get staffing patterns. (#2) • 6. Import the Cluster Definitions from the earlier database (#A) • 7. Go back to the other database and create a make-table query that puts 4-digit employment in this database. (#B) • 8. Create a query that multiplies 4-digit employment by the staffing patterns and sums that employment by cluster to get occupation by cluster (#3)

  13. The Policy Use ofCluster Labor Demand Analysis • Since clusters as we define them are the export base of the economy, growing them generates secondary economic growth in the region. Stimulating local-serving industries doesn’t have this effect. • Supporting clusters with dedicated workforce programs improves their competitiveness – this labor demand analysis tells you which occupations are in most demand in the cluster. • Also – by making assessments about job quality – it can lead to prioritizing clusters – “I want cluster X to grow more because it is a great source of the jobs I want.” • Even further – it can lead to specific industry targets, which your region might not now have – that could grow because they are in the same cluster. • But this begs the question – what are the jobs you want?

  14. Economic Development for Job Quality:Two Philosophies • One approach to this problem says that the problem is always to create high-quality jobs, requiring high levels of education. These are generally the jobs that the U.S. is creating more of (as well as very low-wage, low-skill jobs which are not usually workforce and economic development targets). • The second approach says that the jobs you target should align with the educational backgrounds of the population, and should be mindful of existing divisions of labor in the workforce. • We’ll look at how to support both philosophies in this last section of analysis • What is the level of educational attainment in the region? • What occupations are typically associated with each level of educational attainment? • What is the premium associated with education in the region? • What is the gender and ethnic “division of labor” in your region – i.e. how does occupational employment break down by race and gender?

  15. Labor Supply • Labor Supply represents the sum total of labor power, skills, work histories, etc. of the people in a region. • Generally, we want to assess which clusters can grow given the educational attainment of the workforce and the educational requirements of the occupations in the cluster. • Also – we want to know which clusters we want to grow in the interest of matching jobs with educational attainment.

  16. Educational Attainment • By Race and Gender (B 15002s) • High School • Some College • Associate Degree • Bachelors and Beyond • Earnings by Educational Attainment (B 20004) • Compare all to U.S.

  17. Occupation by Education – Linking to Cluster Staffing patterns • 1. Download the EEO file of occupation by education for your region (or the core urban county in your region). Reduce the data to cover all races and genders by eliminating the rows and columns that provide that detail. • 2. Create a version of the file that contains the SOC code at the end of the Occupation string. Or, use the one in the sample database. (#C) • 3. Create a Query that estimates the employment associated with the each level of education (#4-#6) • 5. Create a query to total employment by cluster (#7) • 6. And finally, create a cluster that estimates aggregate cluster labor demand by educational level (#8). • 7. Compare this to the educational attainment profile of your region from the Census. How well does each cluster match that attainment level?

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