Hello everybody. I’m Scott Sheely and I am with the Lancaster County Workforce Investment Board. (Slide 1) I only have twenty minutes, so I am going to have to go really fast and skip around a couple different things. The main thing I would like to do today is to show you some of the work we have been doing at the Workforce Investment Board over the last four or five years. I think that work could provide a jumping-off base for some of the activities that Antonio is proposing the Center might pursue.
I should tell you that most of what I have to show you are details related to employment numbers. But it is interesting that the industrial resource centers around the state have undertaken a study with the Deloitte Consulting group to look at the same kind of thing by using input-output and productivity numbers. What was fascinating was that they pretty much came to the same conclusions: their driver industries were very close to the kind of industry clusters that we’ve identified in the state out of the employment data.
I will also have a few comments about what you have been hearing this morning, about how to open up those topics for further discussion.
I think, first of all, that it is really important, when we start talking about the Lancaster County economy, that we think in broader and deeper ways. One of the things that jumps out when you begin to look at data, such as those on employment changes over the last number of years, is that the trends vary across the different sectors of the economy. Each of the sectors has behaved differently. One of the things you’ll see very clearly on the charts and diagrams that I’m going to share with you is that two sectors were hit very hard during the recession. The first is the metals businesses. Metals is a traditional Pennsylvania industry. When iron and steel died in Pittsburgh and in the downtown Bethlehem area, they did not die throughout the rest of the commonwealth. Metals is still a very important industry group for us, but it was one of those industries which were very susceptible to off-shoring. That is the reason, I think, that the last recession had a lot of impact on it--the numbers are very problematic for the last few years. Precision-electronics is another one of those areas that had grown up as leading edge businesses during the 90s, but again it was very susceptible to off-shoring activity.
The other thing I want to say is that, in addition to looking deeper, we have to look wider as well. Most of the workforce intervention work we have done, we have done not just here in Lancaster County, but in conjunction with our colleagues in two other Workforce Investment Boards. Together, the three boards cover ten counties, and in reality, it’s the ten counties that constitute our labor market area, and in many ways we have come to see our own labor market in terms of a regional economy. Even when we get down to the point of looking at industry clusters, one of the things we see is that, geographically, the clusters seem to reach far. We ourselves are very much on the western edge of a huge biotech cluster that includes the Philadelphia area and extends into New Jersey. While there are a lot of research and development activities in that cluster, what we do here is to bottle Listerine and to package Tylenol. We’re on the packaging part of that cluster, and so we have to understand where we are in relation to that regional grouping. In a similar way, when we start talking about agriculture and food processing, it’s really the counties of Berks, Lancaster, and York that, together, form the core area where most of the competitive companies are located. Business doesn’t really care about our political boundaries: business goes where business needs to do business. So we have to keep that in mind too as we go forward.
Another element that I think is really important to remember--and this was one of the primary critiques of the PA economy that the Brooking Institution had--is that, yes we have a lot of manufacturing, and in itself that is not a bad thing. But when we are manufacturing mostly commodity products, that is bad--because if something can be made in Pennsylvania, it can also be made any place else throughout the world. So we need to begin focusing on how to get product innovation going, on how to get new start-ups going with products and services on the front end, instead of the back-end, of a product cycle. A good deal of the work that we’ve been doing with our data is trying to figure out how to intervene in a way that encourages innovation. I think that is an important theme on which we need to continue to focus, and for those of us who are actually dealing with the implications of the data, that will probably be one of the main factors in our going forward.
Now, with that said, let me show you a few of the products on which we have been working at the Workforce Investment Board--and I have handouts on them up-front here, if you’d like to pick them afterward.
But at this point, I’m just going to run you through this quickly. One of the things at which we have looked is industry clusters. We have taken the economy and segmented it into clusters of industrial activity. To do this, we have relied on two different theoretical sources. The first one is a theoretical model we have adapted from Michael Porter’s work, which says that, as we begin looking at the economy and segmenting it into parts, we ought to look at it not so much in terms of the traditionally organized industrial groupings, the SIC codes that many of you are familiar with, and that kind of thing, but in terms of more broadly defined chains of activity. We need to be looking at supply chains: where do companies get their inputs and how does that feed into the core of our manufacturing sector? And then we also want to look at how the products of our businesses actually go to market, so distribution chains also become a part of our concern as well. One of the things we see very clearly at the Workforce Investment Board is that there are good reasons for looking at these broad chains of activity. One reason is, in many cases, the skills-package that we are looking for in a workforce that would supports an overall conglomeration of industries: that package really runs through one such chain or another.
So for example, we hear stories of pharmacists in hospitals routinely getting tired of doing their work in that context and moving back to retail pharmacies. So what we have done is really to regroup things in such a way that, for example, in the agriculture and food cluster as we have defined it, we have included not only the people at Kelloggs and Tyson and Sauder Eggs, and the various other groups that do the food processing, but, looking back up the supply chain, we have included people like veterinarians and folks that contribute to the actual inputs these processors use. And then, on the other side of that chain, we have basically looked at the fact that we have a lot of wholesaling activities that go on as well, and that, in order really to look at the cluster, we have to look at all that stuff together.
So we have done some redefinition in the process of doing our clusters. We also know that the clusters overlap, but we have actually tried to get fairly mutually exclusive cluster categories. In the last five years we have identified 20 different clusters within Lancaster County, and they account for over 98% of the employment--again, there are charts that back all this stuff, and those charts are up here if you care to take a look at them. So part of this is getting at the conceptual framework for actually grouping things in some way that makes sense and is fairly in sync with what is going on in the Country.
The next thing for us to do has been to figure out the actual compositions of those clusters. Here we have relied heavily on a statistical package put together by folks at the Humphrey Institute at the University of Minnesota--and we were lucky enough in the early days of the development of our work actually to have the person who developed that package come and be a consultant with us. What we input into this statistical package is data that come primarily from the Department of Labor. It’s basically the most commonly collected employment information--the states collect it on behalf of the federal government, they feed it up to the feds, and then the feds give us that information back. Unfortunately, one of the problems in the past has been that the chain has been mostly from the local to the feds, and getting the information back in some useful form has been difficult.) So what we have done is to take this national data, run it through the statistical package, and then put it into our cluster categories.
Part of what we have done is to look at where to zero in and begin to get some information about the local economy. If you look on this graph, (Bubble Chart 1995-2000 PDF), you’ll see some location quotients. A location quotient is a measurement of the concentration of an industry at the local level compared to the concentration of that industry nationally. If you are at "one" on this index, you are at the national average. That’s basically what the line that goes vertically here is going to show you. Anything that is to the left of the line is below the national average in terms of concentration, and anything to the right is above that.
The other axis (Y axis) is plotting percentage employment growth rates. So, for example, the upper left quadrant contains industries which have grown but which, in terms of their concentration (and concentration is normally a proxy for competitiveness) are below the national average. The upper right quadrant is where everyone wants to be, because this area contains industries that are growing and whose concentration level is above the national average. So the blue circle on the top is construction, the one beneath that is automotive, the big retail bubble is up there, and chemicals, agriculture, and food basically fall in that category. The other thing to notice on this diagram is that the size of the bubble actually represents the employment, the relative employment number. So there are actually three different variables that go into this diagram.
One final element of the diagram to point out is that, from 1995 to 2000, industries were classified following the so-called SIC (Standard Industrial Classification) codes. In the year 2000, the Feds changed the classification to a new, NAICS (North American Industrial Classification System) code. That created a bit of a problem, since the pre and post 2000 data were no longer continuous--they did not go back and fit the pre-2000 data to their new NAICS codes. So we had to start all over. And here is the diagram for the 2000-2002 period: (Bubble Chart 2000-2002 PDF). But that actually turned out not to be all bad, because we were also at the same time looking at the recession period, and so what we have in this chart is not just our conversion from the SIC to NAICS codes but also a representation of how the recession impacted Lancaster County companies.
Now, this is what we see in this chart. First, it’s interesting to see that biotech continued to grow, although it remained fairly small. Second, we can also see how everything lined up along the x-axis. That’s basically saying that there wasn’t a whole lot of growth throughout the various sectors of the Lancaster County economy during this period. On the other hand, we didn’t lose—every cluster seems to have stayed stable during the recession. Look at the purple bubble at the right corner, which is the metals industry. You can see it drop below the horizontal line, which means that it actually lost jobs, but notice how it has nonetheless stayed fairly competitive. The long and short of it here is that we lost jobs, we lost a significant number of jobs, but when we look at the national numbers, we can see that we did not lose here as much as the industry did nationally. So we need to have a perspective of Lancaster County businesses as it operates in a larger context: it operates within a regional economy, within a national economy, and within a global economy.
If you’re wondering about the bubble at the top--it’s the one labeled ‘transportation’--we think that that was when Acme opened their distribution center near the turnpike. That was an anomaly, just because of that one company opening. Another thing of note is the position of the biotech bubble: it reflects only the beginning of the Wyatt layoffs, so that will actually go down dramatically as we go through the 2003 and 2004. But, aside from these transportation and biotech bubbles, everything kind of lines up along the line and there is not a whole lot of growth.
Now, as we have looked at all of this, and as we have tried to make some sense of it, our concern has been to use it basically for workforce development policy. If we have public dollars to put in the street, we need to figure out where to spend them in the most effective way. So, we are basically looking at these questions (Slide 4): which of our industries are growing and which are declining; what the relative importance of an industry is to the local economy--relative of course to the national economy; and how competitive are our regional industries when compared to their national counterparts. We would like to put the money where we have some hope that things are going to be successful. Also, given our workforce concerns, we are looking for companies that are growing, but we are also looking for companies that are growing good jobs. And again, that’s part of the drilling-down process in all of this. So we have done a little bit of reflection on where the winners and losers in all of this mix are, and particularly in this 2000-2002 period.
We have identified seven priority industry clusters. Of the 20 we have profiled and continue to profile, we believe that 7 actually meet this criteria of both being growing and offering good jobs. Here they are (Slide 9). You will notice that two which you might have expected to be there are not on there: hospitality and retail.
From the Floor
What about finance?
Finance is not on there either, and we can show you the numbers; I mean they’re ok, but they don’t look that great. It’s not bringing a lot of high-end jobs. Finance is one of those areas that, as an industry cluster, I would classify as an infrastructure cluster, which is a community-based cluster. But it can also be a trading cluster in terms of bringing wealth in from the outside. Unfortunately, trading clusters in business and finance are generally in Philadelphia and Pittsburgh, regional banks and big insurance companies, that kind of thing. But some of the things you expect are not as good as we think they are.
Moving on, then, what we’ve done in this document (Lancaster County Industry Cluster Profiles PDF) is to take each of the 20 industry clusters and list their component industries. And this is where you can see the idea of looking beyond just core industries. For example, in health care (Slide 10), we’re looking at not only the service providing part of it, but also at wholesale, retail, drugstores, insurance companies--that kind of thing. Also, for each one of these clusters, we have given a snapshot of the employment numbers. So this document will tell you how big the sector is, where it fits into the overall job mix, how fast it is growing, what the average earnings are, and also something about the location quotient in each. Here, for example, is the picture for health care.
Then we have done a little bit of work trying to do some projections. We’ve taken this out, I think, 7 or 8 years: a lot of the projecting is basically following the established trends into the future. So, you’ll see an employment projection for each of the 20 clusters. Moreover, for each of the clusters, we’ve tried to focus on what the drivers are. A cluster includes a lot of different industries, and each cluster is driven by some particular conditions or industries; so we have tried to indicate just what those driving forces might be for each one of the clusters. These, once again, are the drivers for health care (Slide 12).
We also have a slide (Slide 13) which ranks the top occupations. In my world, this leads to a study of things like where people enter into career ladders and where our counselors can begin helping people getting started in careers, research which can contribute to the work that we’re doing now, incumbent-worker training, that is, helping people who are already employed move up. So career-ladder and occupational information is very important. And then also, for each one of the clusters, we have identified the top employers in Lancaster County (Slide 14). This is the kind of thing that we have been doing on an ongoing basis, and it is fairly descriptive. I think you’ll find that, if you’re interested in this kind of thing, our work can be very helpful.
I have just two other documents to tell you about. As you do the occupational analysis like clustering, one of the things that you see very clearly is that there are certain occupations that go across clusters. One of the things that we have tried to do is to tell people in Lancaster County, particularly in the education world, is that there is no truth to the image of Lancaster County as a no-tech or low-tech area. There are many technology jobs in Lancaster County that actually are going vacant right now. We have another document that talks about the top 50 tech jobs in Lancaster County right now (The Top Technology Jobs PDF), and a little bit about the idea of gold-collar jobs (Gold Collar Workers, ERIC Digest PDF) and how that all fits together.
Another question on which we at Lancaster Prospers are trying to focus is the question of local competitive advantage within these various clusters, the question of how we begin, in an intentional way, to push innovation within those clusters, to try to figure out where the local competitive advantage lies and then enhance that particular area in some way or another.
Just to give a quick example: we have a very large cabinetry industry in the County but when we looked into it and started to ask people in that industry about where the local competitive advantage was why so many high-end custom kitchen manufacturers were located right here, we found out that that advantage was to be found in the "finishing" skills of the local workforce. When people buy $70,000 to $100,000 kitchens to put into $500,000 homes, that’s what they want: the hand finishing. So, it’s the quality of workmanship, embedded in the local workforce, that really makes a difference and gives that industry a competitive advantage here. So when we’re looking at starting a center of excellence which would support that local competitive advantage, the center of excellence will actually not be in the cabinetry industry at large, but in the wood finishing part of it, because that is where the competitive advantage can be found.
We have 7 centers of excellence that really address the competitive advantages of our industries (Regional Industry-Driven Centers Summary PDF). But I’m going to stop at that point, and if you have any questions later, I’ll be happy to answer them.