Figure 1

I would like to explain what a “BUBBLE CHART” is, how it can be used and interpreted, what the disadvantages of a bubble chart are and how it can help in carrying out further analyses; all this through an example shared right here.

Bubble charts are popular tools for identifying and illustrating industry clusters. Essentially, these charts allow 4 different variables to be plotted within the same graph, making it easy to assess relative economic performance. Because they allow visual comparisons of well-understood measures, bubble charts are often used for pinpointing priority industries that should receive attention from a state economic development agency.

Bubble charts: what they are

Figure 1 illustrates industry cluster relationships for the 17 Pennsylvania targeted industry clusters (CWIA 2004).

The following four variables are plotted in this single graphic:

1. Average cluster wages in 2002: on the x-axis (horizontal)

2. Growth in jobs, 1998 to 2002; on the y-axis (vertical)

3. Employment size of the industry, 2002; indicated by the size of the bubble

4. The industry’s location quotient, 2002; indicated by the color of the bubble

With user-defined demarcations, location quotients show whether a state or region is more

specialized (>1.1149), less specialized (<0.95) or as specialized in a particular industry as is the nation or the reference region.

In this graphic,

clusters in which the state is more specialized than the nation - shown in red

clusters with less specialization - shown in green

clusters with average specialization - shown in blue.

Bubble charts: how they are used

Bubble charts show the most important clusters in a state or region as measured by-

total employment size (the bigger the bubble the better),

recent job growth (the further up in the graph the better), and

high-paying jobs (the further to the right in the graph, the better).

Depending on the state’s economic development objective – that is, whether the goal is to create more jobs or better-paying jobs, or both – the state agency responsible for economic development might choose to concentrate on industries with large bubbles or industries located in the right-hand side of the graphic. To many, the ideal is to focus on rapidly-growing, high-paying industries depicted in the upper right-hand corner of the graph.

For example, the biomedical industry was promising because it paid relatively high wages, had shown substantial growth in the last 5 years, and had a red location quotient (indicating that the state had some locational advantage in the industry). However, it is also had a relatively small industry in terms of employment size, in contrast to, for example, Life Sciences and Health Care.

While bubble charts can help identify “promising” clusters, an important shortcoming of this analysis is that they can’t identify “why” a particular region has an advantage. For example, is a cluster strong in a region because of access to resources or markets? Or, does the region possess a particularly skilled labor force?

Because bubble chart analysis can’t answer the “why” and “how” questions, it should be seen as an important part of economic development analyses, but it is only one part of the process.

Nonetheless, bubble charts are a good starting place for any discussion about cluster-based economic development policies. For example, in their cluster work with communities, the first question after identifying an important cluster was usually some variant of “why here?” That opened the door for rich conversations about the potential causes of local competitive advantage. And lessons can also be learnt from declining clusters: the 2nd question they usually got was “what’s going on?” as audiences look at historically important clusters that seem to be in decline.

For example, the decline in printing employment (high LQ) tends to raise questions about off-shoring and the supposed transition to a paperless, digital economy.

On that note, I would like to conclude my blog by adding that bubble charts provide not only a method for identifying clusters, but also an entrée for discussing more advanced topics, both theoretical (why?) and practical (how?).

Posted by-

EMI JAVAHARILAL (13134)

Operations Group 2