{ "id": "1996-april-economic-analysis-on-the-fly", "slug": "1996-april-economic-analysis-on-the-fly", "url": "https://dwillis.github.io/uplink/article/1996-april-economic-analysis-on-the-fly.html", "year": 1996, "month": "April", "monthNum": 4, "headline": "Economic analysis on the fly", "kicker": "A look at widgetmaking", "author_name": "Neill A. Borowski", "author_title": "reporter", "affiliation": "The Philadelphia Inquirer", "summary": "Neill Borowski explains how to use 'location quotients' to measure industry concentration in local economies compared to national averages.", "keywords": [ "location quotient", "economic analysis", "employment data" ], "topics": [ "Data & Databases", "Business & Economy", "Investigative Methods" ], "technologies": [ "spreadsheet", "County Business Patterns" ], "full_text": "When it comes to local economies, percep- tions may not be realities. Community leaders may THINK their re- gion is a leader in computer manufacturing, or financial services or other industries. But we don't really know this simply by looking at employment totals. Enter the \"location quo- tient\" รป a well-worn tool in regional economic analysis. We recently used this statistic to see just how important computer industries are to the Philadelphia area. Location quotients are fairly simple to com- pute. They show the concentration of employ- ment in a region compared to the United States. The quotients, which are measures of con- centration, also can be used with total payroll dollars (from County Business Patterns) rather than employment. Or they can be used to calcu- late the concentration of one population (race, etc.) compared with the nation (or other areas). Let's look at widgetmaking. The quotient calculation first requires calculating what per- centage widget employment is in this metro and the percentage widgetmaking is nationally. If there are 10,000 widget workers and 100,000 workers in a metro, the percent is 10 percent. If there are 5 million widget workers nationally and the total workforce is 100 mil- lion, 5 percent of the national workforce is in widgetmaking. Just by looking at the data (10 percent vs. 5 percent) you can see widget em- ployment is twice as concentrated in the metro than in the nation as a whole. You convey this with another quick calcula- tion: (10 percent/5 percent)*100 = 200. This is the location quotient, or index, of the metro, with the U.S. average = 100. With a spreadsheet or database manager you can quickly compute the quotients for all sec- tors in your local economy. If your quotients are around 100 (cut some slack for data reporting error), the concentration is equal to the national concentration. If your quotient is, say, 50, then your concentration is half that of the nation. We ran a bar chart with the Philadelphia metro vs. San Jose in several computing and other industries. A broken line through the charts marked U.S.=100. Why use index numbers when you could run a table showing 10 percent and 5 percent? For widgetmaking, you could. But what about ce- ramic-plated widgetmaking? The numbers are 0.0043 percent vs. 0.0021 percent. Still twice as concentrated, but a lot more difficult to under- stand in percentages (the index would be 205). In general, the percentages will be relatively small. By the way, round the index numbers; don't use decimals, which serve to confuse. Of course, there are assumptions and prob- lems with this. I used only non-agricultural businesses. Government employment wasn't a factor. If the metro is small enough, it could have a high concentration (index of 200 or more) but actually have only a few thousand employees in that industry. Or, the employment might be all at one company. (However, the County Business Patterns will suppress employment and payroll data if there are only one or two employers in a given industry.) Some purists might also say you have to subtract your metro from the nation before making the national computations. You don't have to use County Business Pat- terns. Your regional Bureau of Labor Statistics office can supply establishment employment (series 790) statistics. Or you could use data from the Bureau of Economic Analysis (U.S. Commerce Department). I chose the Patterns because they are seen as a pretty accurate mea- sure. Another measure is the series of economic censuses from the U.S. Census Bureau (they're done every five years in years ending in \"2\" and \"7\"; the data for 1992 came out last fall). Some possibilities: If agriculture is big in your area, use the Census Bureau's Census of Agriculture to com- pute location quotients (dairy revenues in your metro vs. other leading dairy metros). If the federal government is talking of more cuts, determine how concentrated federal em- ployment is in your metro. Is it much more concentrated than the nation as a whole? Is upscale retail hot in your area? Use the Census of Retail Trade to compute the relevant location quotients. For those fortunate enough to have motor vehicle databases, is there a concentration of luxury cars in your county vs. other counties?", "provenance": { "extracted_by": "pre-v2", "extracted_at": "", "verified": true }, "similar": [ { "slug": "1998-september-on-the-internet", "score": 0.18 }, { "slug": "1997-october-analyzing-property-tax", "score": 0.13 }, { "slug": "1997-march-seminars-bootcamps-conferences", "score": 0.107 }, { "slug": "2003-julyaugust-gps-units-add-depth-to-reporting", "score": 0.102 }, { "slug": "1997-august-separate-and-unequal", "score": 0.098 } ], "html_url": "https://dwillis.github.io/uplink/article/1996-april-economic-analysis-on-the-fly.html", "json_url": "https://dwillis.github.io/uplink/article/1996-april-economic-analysis-on-the-fly.json" }