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NIST GCR
02-830
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Non-participants |
ATP
participants |
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Variables | Mean (Std. dev.) |
Min. |
Max. |
Mean (Std. dev.) |
Min. |
Max. |
Patents | 37.71 (89.22) |
0.00 |
942.00 |
131.51 (204.31) |
0.00 |
1,413.00 |
Real R&D | 106.37 (215.10) |
0.00 |
1.490.14 |
436.51 (912.65) |
0.03 |
6,667.64 |
Sales | 2,911.33 (6,996.35) |
0.05 |
69,276.00 |
12,912.94 (24,036.08) |
1.29 |
16,537.0 0 |
Real sales 2589.07 | 0.05 (6,156.09) |
59,279.66 |
11,451.88 |
1.07 (21043.83) |
127,501.9 |
0 |
Real net capital stock | 2,273.38 (8,110.09) |
0.00 |
161,717.30 0 |
8,835.99 (15,904.86) |
0.37 |
95,607.24 |
Number of observations | 1,898 |
684 |
Table 2 presents
various specifications of equation (1). We add a full set
of year dummies to our regression, which partly controls
for the time-truncation effect in our patent data. Industries
are classified into seven sectors. Given our earlier concerns
about reverse causality, we estimate a random-effects specification
(column 1) and a fixed-effects specification (column 2).
In both cases, the measure of intensity of participation, C,
is positively correlated with patenting. The coefficient
is statistically significant in both specifications.
Columns 3 and
4 give the results when we include the log of the deflated
net capital stock as a control for the size of the participating
firms. This variable is included because previous research
indicates a positive correlation between firm size and the
propensity to patent. Controlling for firm size, the impact
of participation in ATP-funded consortia on research output
remains positive and significant in all specifications. These
results are quite striking given the many imperfections of
our data. It suggests that there may indeed be substantial
spillover-enhancing effects achieved through these research
consortia.
IMPLICATIONS
This section gives us a measure of the extent to which involvement in ATP projects impacts the entire research portfolio of participating firms. Since the participants in our data set include large firms with quite diversified research portfolios, we will only observe large, significant impacts in this analytical framework if the effect of participation is the generation of knowledge spillovers that transcend the generally narrow technical focus of the project itself. So how large is the effect of consortia participation on the overall patenting of participating firms? Perhaps it is most instructive to look at column 4 of Table 3the results from the fixed-effects specification with all controls in place. The coefficient on the variable C is 0.075. This suggests that participation in one additional ATP-funded research consortium per year would generate an increase in patenting in that year of nearly 8%. We note that the causal interpretation that we give in this statistical relationship between research productivity and participation is not the only possible interpretation. In the absence of a randomized experiment in which ATP makes awards to research consortia without regard to research quality, it is difficult to prove that the statistical relationship we document is causal. Nevertheless, our best efforts within the context of this framework suggest that ATP has a positive impact on the research productivity of the firms participating in its research consortia. We are not able, within this framework, to undertake a full cost-benefit analysis of ATPs funding; however, the statistical link we find between consortia participation and overall patenting is a necessary, though certainly not sufficient, condition of establishing that ATPs investment is socially productive.
Table
2. Estimation of a Patent Productuion Function: Overall
Effect of Participation (Dependent variable: Log
of patents granted per firm classified by the year of
application)
Variables | (1) Random Effects |
(2) Fixed Effects |
(3) Random Effects |
(4) Fixed Effects |
Real R&D | .567 (.019) |
.424 (.027) |
.315 (.029) |
.265 (.034) |
Real net capital stock | |
|
.351 (.030) |
.311 (.04) |
C | .081 (.026) |
.066 (.026) |
.088 (.026) |
.075 (.026) |
Chemicals | .353 (.148) |
|
-.089 (.143) |
|
Machinery | .185 (.160) |
|
-.101 (.152) |
|
Transportation | .031 (.202) |
|
-.41 (.193) |
|
Precision instruments | -.071 (.171) |
|
.041 (.160) |
|
Fabricated metals | -.286 (.435) |
|
-.577 (.408) |
|
Other manufacturing | .526 (.498) |
|
-.011 (.469) |
|
Year 1986 dummy | .028 (.049) |
.036 (.048) |
.0322 (.048) |
.036 (.048) |
Year 1987 dummy | .124 (.049) |
.139 (.048) |
.115 (.048) |
.124 (.048) |
Year 1988 dummy | .082 (.049) |
.108 (.048) |
.061 (.048) |
.088 (.048) |
Year 1989 dummy | .120 (.049) |
.151 (.049) |
.088 (.048) |
.109 (.048) |
Year 1990 dummy | .131 (.049) |
.161 (.049) |
.082 (.048) |
.104 (.049) |
Year 1991dummy | .121 (.049) |
.152 (.049) |
.077 (.048) |
.100 (.049) |
Year 1992 dummy | .115 (.05) |
.154 (.05) |
.066 (.049) |
.093 (.049) |
Year 1993 dummy | .051 (.05) |
.093 (.05) |
.009 (.049) |
.038 (.05) |
Year 1994 dummy | -.058 (.051) |
-.012 (.050) |
-.126 (.050) |
-.093 (.051) |
Year 1995 dummy | -.808 (.053) |
-.746 (.053) |
-.895 (.053) |
-.849 (.054) |
Constant | .594 (.120) |
1.20 (.096) |
-.473 (.145) |
-.149 (.198) |
R-squared | .7004 |
.6958 |
.7281 |
.7190 |
NOTES:
Return to Contents or go to next section.
Date created: January 24,
2003
Last updated:
August 2, 2005
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