TAX INCENTIVES FOR HIGH TECHNOLOGY BUSINESSES

Chapter Five

ECONOMIC IMPACT OF THE PROGRAMS

The legislative goals for economic impact analysis of these programs were separated into job creation, employment growth, company growth, growth in R&D, product line diversification and diversification of the overall state economy. These items are both symptomatic of the results of the tax incentive programs, as well as potential measures of future benefits to the state if research and development growth is successful in creating marketable products that in turn create manufacturing, wholesaling, retailing and service jobs that use or sell the products. They also recognize the fundamental contribution of R&D to maintaining and enhancing Washington’s economic vitality. In addition, research and development activity that is successful can create the need for more highly skilled and/or highly educated workers. However, research and development has a relatively long lead time for new discoveries or ideas to manifest themselves in products that are produced and sold.

There are certain difficulties in measuring the results of programs such as these, primarily in the availability of historic data, as well as data that firms might consider proprietary. The impact of other tax incentive programs and publishing lags for data also create difficulties in measuring the impact of the high tech programs, particularly for this initial report with the relatively short period of time in which the programs have been effective. So far, the high tech programs have generated only two years of data, so at best the results can only be tentatively indicated at this time.

The effectiveness of R&D spending is critical to the effectiveness of these programs. This is because the results of these research activities cause further development, processing, manufacturing or trade activities to be stimulated. The effectiveness of R&D spending is the most difficult part of the study to quantify. Measurements of R&D effectiveness will need to be developed for future studies, utilizing not only administrative information but also survey results from the participants.

Patents and copyrights are one indicator of the effectiveness of R&D activities. Patents and copyrights provide evidence of successful research and may be a quantifiable example of activities that could produce future economic development.

Copyrights are generally used to protect written materials such as software from use by others. Since a large part of the participation in these programs is by advanced software technology, it would be beneficial to be able to obtain quantitative copyright information by region in the state over time to evaluate trends. However, we have not yet had success in locating a source for this information. It is possible that this information has been developed by some of the program participants but might be considered proprietary information by them. In future studies we may be able to do analysis in this area if such information can be developed.

Patents are used to protect owners from unlawful commercial use of their ideas as well, but these are generally used for tangible assets. It is our understanding, though, that some patents may be used for software/equipment combinations. While it is true that not all product ideas are converted into patents, and that not all research results in useful ideas, and there is a potentially long lead time between the time of application and grant, nonetheless granted patents do provide reasonable evidence of success in research. Patent statistics are available in a fairly timely manner from the U.S. Patent Office and are published for geographic areas down to the county level, so they can be a useful tool for analysis.

Job Creation

SALES/USE TAX EXEMPTION

Based on information submitted from applicants, the estimated number of jobs created for projects completed in 1995 is 81. Estimated jobs created for projects completed in 1996 is 1,667 and in 1997 is 3,329. This information has not yet been verified by reference to actual employment data. However, not all applicants provided estimates of new jobs that were expected to be created. The above information only reflects estimates of job creation that were reported to the Department of Revenue.

Sometimes a research and development facility is a separately identifiable facility with employment separately reported to the Employment Security Department. If any of these facilities become identifiable as separate units, it may be possible in the future to identify job creation for research and development activities. At this time, however, only total employment is available for most firms.

B&O TAX CREDIT

The B&O tax credit is potentially available to all participants in the sales/use tax exemption program. Since all firms performing research in the designated five technology categories are eligible for the credit program, but only those building or expanding facilities are eligible for the deferral/exemption program, B&O tax credit participants could include participants of both programs. However, only 37 of the 64 participants in the exemption program also take the B&O credit.

In Table 5.1 total employment for all program participants is shown. During 1995 (1996 employment was not available at the time of the report) total employment was 42,718 or 3,260 jobs more than in 1994. Since research and development spending represents only an average of about 14.5 percent of total gross activity and R&D wages are higher than average, it is likely that about 300 - 400 new R&D jobs were created during 1995 by participants.

Gross Business Income

CONCLUSIONS

Applicants for the sales tax exemption indicate that 5,077 new jobs are attributable to R&D investments to date. It is too soon to verify these employment changes and attribute the jobs to the tax incentive programs, but it is likely that employment was higher than it might have been expected to be (see discussion about patents in later sections). Clearly the completion of a new research facility generates more employment (assuming that positions are not transferred from an old facility) because participants are investing capital that requires a higher level of labor to support. Future studies may be able to obtain more employment data to analyze this question.

Employment Growth for Participants

Table 5.1 shows that employment growth for participating firms in these programs has historically been high but growth has slowed in the last two years.

This table also shows a history of wages for high-tech participants. In 1995 the average employee in these industries earned over $71,000 per year and had a history of rapidly increasing wages. However, the trend of base wage growth is not always seen in the data.

For example, average wages were higher in 1992 than in 1993 and for several companies employment increased significantly during that time while total wages decreased. This apparent anomaly is believed to be caused by the timing and magnitude of stock options being exercised.

While the average employment per firm for these industries is over 100, the median employment for participating firms is 6 employees. This indicates that the majority of high-tech firms participating in these programs are quite small in terms of number of employees.

CONCLUSIONS

It appears that employees in these high-tech industries have enjoyed rapid increases in employment opportunities and wages over the years. Recent slowing in growth of employment still leaves growth above statewide average growth for all industries. The slow-down likely represents a period of consolidation until markets absorb spending levels of recent years. While both large and small firms participate in the programs, the majority of firms are small.

Company Growth

The following two charts show the growth in gross business income (GBI) for participants in each of the high-tech programs. The definition of GBI used here is the same as that found in the Quarterly Business Review, published by the Department. It is important to note that not all businesses reported income in every year represented on the charts. In fact, many of the companies participating in the programs are new and were only organized in the past few years.

A review of these charts indicates that the firms participating in the deferral/exemption program are considerably larger and have also grown faster than those high-tech firms that only participate in the R&D credit program.

Gross Business Income

Gross Business Income

Looking at Table 5.1 it can be seen that the growth in the number of companies per year has been less than the growth in gross income; this has allowed the average sales level per firm to increase. However, the slowing in total sales growth in the last two years has reduced opportunities for new firms to enter these technology areas and may have encouraged mergers between companies. This is shown by a decline in the growth count of companies from 20.3 percent in 1995 to 6.9 percent in 1996.

Table 5.2 shows GBI growth for firms in the high-tech programs compared to GBI growth for all firms in selected two-digit SIC categories. The SIC categories that were selected are indicative of the SIC categories of businesses participating in the high-tech programs. The table only shows GBI for businesses that were present in 1990 or in other terms, the table shows GBI for the same number of businesses in 1990 as in 1996. Selected SIC categories are:

   28   Chemicals And Allied Products
   36   Electronic And Electrical Equipment
   50   Wholesale Trade
   57   Home Furniture, Furnishings, And Equipment Stores
   737  Computer Programming, Data Processing, And Related Services
   87   Engineering, Accounting, Research, Management, And Related Services

As can be seen by the table, businesses participating in the high-tech programs show a steady growth in GBI. Most of this growth is due to the large percentage of participants that are in the 737 SIC category. Most of the large software development firms in Washington State are in the 737 category. Other SIC categories show various growth trends that do not correlate well with the high-tech category.

CONCLUSIONS

Company and gross income growth has been rapid for these technologies for at least several years, but some slowing has been noticed in the last two years which has reduced opportunities for new firms to enter the market. Those firms which have been able to become large enough to have strong financial support are those that are able to expand research and development facilities and are therefore the primary participants in the deferral/exemption program.

Table 5.2 Percent Growth in Gross Business Income

Selected SICs vs. Participants in High-Tech Programs

High

All SICs

Year

Tech

High Tech

28

36

50

57

737

87

1990

14.7%

10.8%

15.1%

4.5%

8.7%

-3.6%

52.0%

12.7%

1991

18.0%

5.4%

-38.4%

3.1%

-1.8%

7.8%

38.9%

1.9%

1992

17.1%

8.1%

78.7%

11.4%

6.7%

9.2%

21.1%

10.2%

1993

16.8%

8.3%

0.0%

6.2%

0.0%

10.9%

44.5%

2.6%

1994

16.7%

9.5%

11.0%

8.5%

9.3%

14.8%

34.6%

10.5%

1995

14.7%

10.2%

-40.1%

20.3%

6.0%

9.5%

7.4%

6.0%

1996

11.6%

7.9%

9.5%

7.3%

9.4%

10.7%

10.1%

8.7%

Growth of Research & Development Investment

Table 4.1 shows the estimated research and development spending by technology category, and credits taken for 1995 and 1996. It appears that R&D credit growth is moving at about the same growth rate as gross income growth during 1996 for the combined five technology groups. Growth during 1995 can not be analyzed since data earlier than 1995 are not available, because the program did not exist prior to 1995.

Some differences exist in R&D spending between technology groups for the period reviewed. Biotechnology companies spent a higher share of gross income on R&D (33.9 percent) than the other technologies (which averaged 14.5 percent) but growth in R&D spending was lower for biotechnology (8.8 percent) than the average 15 percent. Also, firms in the electronic device technology category had the highest growth in R&D spending (25.4 percent) but had the lowest share of R&D spending to gross (8.9 percent). These differences are probably due to the difficulty of expanding research budgets from high levels compared with low levels. For example, when a research budget is already 33 percent of gross revenue, it is more difficult to increase it 15 percent (or about 5 percent of sales) than it is when the research budget is 8 percent (or about 1 percent of sales). It is not likely that changing markets caused these differences in spending, because growth in the number of companies participating in these technologies (shown in Table 4.1) was similar (20.6 percent for biotechnology and 18 percent for electronic devices) indicating that markets were still relatively easy to enter.

Environmental technology firms had a decline in R&D credits taken in 1996 which was explained by three firms dropping from the list of participants. This category accounts for about 1.4 percent of total credits taken.

Advanced materials technology had the highest growth rate in new firm entrants (27.3 percent) of the five groups but was also a small group amounting to only 2 percent of the total credits taken.

The largest technology category is the advanced computing group which is comprised of 207 firms and represents 42.5 percent of the credits taken. The group had an increase in R&D spending of 14.3 percent during 1996 and the number of participants increased at a healthy 19.7 percent rate.

CONCLUSIONS

While we are not able to say anything about growth in R&D spending compared to periods prior to the beginning of the programs at this time (at least using information provided by participants), R&D spending growth during 1996 has moved in tandem with company gross revenue during 1996. All of the technology sectors except environmental technology took greater credits in 1996 than in 1995 but that sector may have had unqualified firms in 1995 which dropped out in 1996. In future years audited credits may provide better data on R&D spending.

Product Line Diversification

Data on production line diversification are presently not available for businesses participating in either of the programs. Because of the time it takes for R&D investment to yield new products and the short time these programs have been in effect, it was not reasonable to attempt to obtain product information for this study. However, it is anticipated that this data may be obtained by surveying participants in the two high-tech programs for subsequent analyses of the incentive programs.

Diversification of State Economy

At this time no specific definition exists for the term "diversification of the state economy" as indicated in the statute. A wide range of economic and demographic indicators may be chosen to represent "diversification" as the term means different things to different people. For example, an increase in employment across SIC categories may represent a more diversified economy. Diversification of activity geographically across the state is another way of looking at the data. For future versions of this study, measurable indicators of diversification will need to be developed; they will likely include both geographical and industry diversification measures.

One way of viewing diversification is geographic diversification across the state. In Table 5.3 patent counts per 1000 population are shown by county and are ranked by the level of activity and by the change in level since 1994. This is to show the trend after the beginning of these programs. Obviously, there is a time lag between the time that an application for a patent is made and the time it is granted, so patents granted in 1995 and 1996 are probably not closely related to research during the same period. Nonetheless, this information is helpful in establishing a base for comparison. It is interesting to note that many rural counties generate a high level of patents and have raised the level of patent generation in recent years. For example, Lewis, Walla Walla, Jefferson and Franklin counties have all had significant increases in patents in recent years. Of course, about 75 percent of program participants are located in King, Snohomish and Clark counties and about 75 percent of all state patents are also granted in the same counties.

It is interesting to note that the county location of participants correlates highly with the location where patents are granted. While the participants in the R&D credit program are generally located in the Puget Sound area they are distributed across most of the state. This implies that originality is not just centered in the Puget Sound area, but that the R&D activity which precedes the issuance of a patent is probably more concentrated.

Table 5.4 shows patent counts for Washington as compared with other states. The purpose of this table is to give an indication if patent activity is higher during 1995 and 1996 than might have been expected based on trend levels in 1990-1994 for the entire country. The result of this calculation is that the number of Washington patents granted in 1995-1996 was about 14 percent above what might have been expected given the level of patents granted in the entire country. When compared to other states the only state of comparable size that had higher relative growth than Washington was Indiana. The other states with higher relative growth in patent generation, Wyoming, Montana, Kentucky, Alabama and Vermont, all had smaller population bases and had lower per capita patent generation.

It is interesting to note that many of the states with higher historic levels of patent generation such as Massachusetts, New York and Texas were all adding patents at lower growth rates than might have been expected by historic trends. In fact, on average states were computed to be 4 percent below the expected trend in patent generation, which indicates that the comparison period 1990-1994 was a relatively higher growth period for patents on a national basis.

Gross Business Income

CONCLUSIONS

Firms participating in the high-tech programs are distributed throughout the state in about the same proportions that patents are generated in the state. This indicates that they mirror the historic creativity areas around the state and future patent growth is likely to be in the same areas. This implies that program participants are not likely to distort the historic pattern of geographic creativity by concentrating it in the Puget Sound area. It is interesting that some rural counties have had increases in growth in patents (which may be too early to attribute to these programs) which may indicate a possibility of future growth in products for those areas and a positive force for diversification.

Washington has generated more patents in recent years than might have been expected according to historical trends, and the state has performed well in patent growth in comparison with other states. While it is not yet possible to determine whether the tax incentive programs are causing the growth in patents, they are at least in support of the positive trends that appear to currently exist.

Gross Business Income