Click for next page ( 116


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 115
AS SES S ING THE IMPACT OF FEDERAL INDUSTRIAL RES EARCH AND DEVELOPMENT COP END ITURE ON PRIVATE RESEARCH AND DENIES OPMENT ACTIVITY Frank R. Lichtenberg* Graduate School of Business Co lamb ia Univers ity and National Bureau of Economic Research A number of studies have attempted deco determine how federal expenditures for research and development (R&D) performed in industry have affected the quanti~cy of private inures vent in R&D . The s tudies have analyzed the statistical relationship be~cween federal and L company R&D spending at the five, industry, and aggregate levels. lathe possibility that priorate decisions to invest in R&D may be influenced by federal industrial R&D spending is regarded as worthy of careful investigations in part because numerous studies have demonstrated that priorate R&D expenditure has ~ significant, posi~ci~re imp ace on Coccal factor productivity (TEE). Although most analysts have failed to find evidence of a similar direct productivity impact of federal R&D expenditures, 2 federal R&D nevertheless may have a considerate, e indirect impact (positive or negatived on IFP if it influences private R&D investment decis ions . Most9 if not all, of the econometric studies of the effect of federal expenditures on private R&D resource allocation are "reduced-form" is nature. They a~ctempt to determine the direction and magnitude of the effect without providing an explicit theoretical basis for a link between the two. The purpose of this paper is to fondue ace, and to review or develop evidence concerning, several maj or hypotheses that could account for the existence of such a link. The implications of these hypotheses are investigated in the context of a model predicated upon the assumption that priorate sponsors of R&D allocate R&D resources optimally, that is, they employ R&D resources until the point at which the expected marginal re turns to R&:~) input equal ache marginal cost . This ~ equilibria " * Financial support from ache National Science Foundation (aria grant PRA 85-12979 deco ache National Bureau of Economic Research) and from the MacAr thus Foundation Faculty Research Program at Columbia Unifiers ity is gratefully acknowledged. Donald Siegel provided capable research assistance. The remaining errors are Chose of the author. 113 -

OCR for page 115
FIGURE 1 Supply and Demand Model of Privately Financed R&D Output Quar.ti Be termination and Price De te Ruination $ P* S - - - - - - - - 1 - - - - ,,, !Q* Quantity of privately financed Red) output - L 1 Lo -

OCR for page 115
mode 1 o ~ priorate R&D resource allocation is sketched in ache next section. In that section, three main hypotheses that could account for the existence of a relationship between federal and private R&D expend) Cure are offered ~ The three hypotheses, referred deco as "crowding out, n "spill overs, ~ and "demand pu119 n are examined in detail in subsequent sections. The section devoted to the demand-pull hypothesis is the longest and the only one containing new econometric evidence. Lee emphasis on that h~rpo~chesis reflects ache belief that insufficient attention has been paid deco ache role of federal demand for goods and serrices in stimulating pr~ra~ce R&D investment. As a result, estimates of the relationship between federal and private R&D have been misincerpre~ced as reflecting solely the effects of federal R&I) on the supply (cost) of privately produced innovations . The econometric evidence presented sugges ts an alternative interpre~ca~cion. The final section provides a summary and ten~cati~re cone lus ions . THE MARKET FOR PRIVATE R&D OUTPUT Economists engaged in the study of technical change sometimes find it useful to represent research and development as a production acti~ri~cy, an activity in which the services of R&D inputs (for examples scientists and engineers, Rho plant) are employed deco produce improvements in products or processes, which in turn constitute the output of R&D3 The measurement of R&D output, of course, poses maj or difficulties ~ In the absence of dla~ca on ache value, in cons Manic dollars' of improvemen~cs in products and processes, chose interested in measuring R&D output are confined to the analysis of " indicators" o f R&D output, such as patent counts e While economists are fully cognizant of ache serious problems associated with measuring R&D outpu~c (and, hence, witch measuring productivity of R&D inputs ), ache concept is both well defined and useful enough to play a key role in this analysis. We representation of R&D as an activity devoted to the production of R&D output is useful because it enables one deco apply ~ appropriately, the author believes ~ a standard economic model of the determination of ache quantity and price of an indus~cry's output deco investigate ache effects of federal R&D on equilibrium priorate R&D output. In the standard textbook model, as depicted in Figure 1, the equilibrium quantity and price of an indus~cry's output is determined by the intersection of the industry supply and demand schedules. The supply schedule is derived from the marginal cost schedules of ache individual producers . The position ~ intercept) of ache supply schedule depends on both input prices and input productivity; increases in input prices shift the supply schedule up, and increases in input productivity have the opposite effect. Offend the slope of Ache supply schedule is hypothesized to depend on the time horizon, with supply assumed to be less elastic (steeper) in the short run than in the long run (in which case it may be perfectly elastic?. 1 15

OCR for page 115
The primary reason for a less than perfectly elastic supply of output in the short run is a Less than perfectly elastic supply (that is, a short run immobility) of inputs . This paper proposes Deco analyze the impact of contract R&D expenditures on private R&D activity (both output and input) within ache context of an equilibria model of industry output determination. It is assumed that private sponsors of ~ produce Rho output until the point at which the marginal cost of improvements in products and processes eq,'= Is the (-expected) marginal retunes . Lee demand 5 or marginal willingness to pay, for privately produced R&D output is assigned to be a nonincreasing function of output. The marginal cost, or supply, Of n irmova~cions " (R&D Otltpu~C), on the ocher hand, is assumed to be a strictly increasing function of output. The latter assumption may be justified on both theoretical and empirical grounds . First, an ~ intangible ~ input critical to ache production of industrial innovations - ~ the underlying scientific and technological "knowledge base"--is, arguably, "quasi-fixed, n meaning that it is more expens Eve to increase the stock of knowledge quickly than slowly. Thus, increases in the employment of conventional R&D inputs, such as scientists and laboratory equipment S are likely to be subj ect to diminishing .returrts . Second, evidence will be presented ehat the supply of conventional R&I) inputs is less than perfectly elastic in tone short run. Additional ~ indirect) evidence of a positively sloped supply schedule of R&D output is found in the work of Nadiri and his colleagues. Their econometric studies suggest that the level of R&D activity responds quite sluggishly deco its hypothesized de~cerm~nants; the low observed ffspeed of adj,'-ct:ment" of R&D spending to its long- run equilibrium level. is consistene with the hypothesis of sharply increasing short-n~n marginal costs of producing R&I) output. Shifts in the supply of and the demand for priorate R&D output result in changes in the equilibrium quantity and price of R&D output. The next three sections outline in detail echoes hypotheses chat imply that changes in federal R&D expenditure either cause shifts in the pri~ra~ce R&D supply schedule, or are correlated witch (but do not cause) shifts in the demand for privately produced improvements in products and processes. Hence, each of the hypotheses court account for Ache existence of a correlation between federal and prince R&D. The first two hypotheses, referred to as "crowding out A and "spi3~lo~rers," may be regarded as instances of Ache general phenomenon of n economies ~ or diseconomies ~ of index try growth,. a phenomenon perhaps first identified (or at least labeled) by the eminent 19~ch-century economist Alfred Harshall. Economies (diseconomies) of growth are said to occur when expansion in the total output of an industry per se results in reductions (increases) in each producer's unit cost of production According deco the crowding-out hypothesis, increases in federal R&D expenditure impose "external" (pecuniary) diseconomies on private R&D sponsors. Spillo~rers, on ache other hand, may be interpreted as "internal" ~ technological) economies in the priorate production of R&D Output generated by increased federal R&D expenditure. - 115

OCR for page 115
I e the supply-and-demand model of private R&D output de~er=~nation is useful for evaluating some of the possible impacts of federal R&D expenditure on private R&D activity, it is less well suited to analyzing others. Two features of federal industrial it&l) activity whose impact on private R&D it is difficult to assess ~ in this framework and perhaps in general ~ are its relatively high degree of concentration and its relative instability. As Table 1 indicates, federal R&D ascends deco be performed by a smaller number of large companies than privately sponsored R&D. Thus, increases in ache federal share of total industrial it&l) expenditure would be expected to result in an increased concentration of R&D resources. Table 2 illustrates the substantially greater volatility in the growth of federal Red) than in the growth of company it&l) between L956 and 1983. Two measures of volatility, the coefficient of variation and the range of the growth rate, are, respectively, 5 . 6 and 3 . ~ times as large in ache case of federal R&D as they are for company R&D. This contrast reflects the episodic nature of federal commitments to devote resources to industrial Red). A troubling possibility is that the relatively volatile pattern of federal investment destabilizes the market for private R&~) output by increasing uncertainty about the future course of technological change and, perhaps, as a result, depresses the quantity of private R&D investment in the long run CROWDING OUT lye crowding-out hypothesis is that increases in federal R&D activity increase ache private cost of producing R&D output by driving up the prices of specialized R&D inputs. The hypothesis is predicated on the assumption that federal and pri~ra~ce R&D employ similar types of resources, such as scientists and engineers, the aggregate supply of which is less than perfectly elastic. A graphic representation of ache crowding - out phenomenon is gird in Figure 2 . Suppose, for concreteness, that the graph represents ache market for R&D scientists and engineers. The supply schedule of scientists and engineers is labeled S. and the federal and priorate demand schedules are labeled DO and Dp, respectively. 'the market-clearing wage race W is determined by the in~cersection of ache supply schedule and the aggregate demand schedule D ~ the horizon~cal sum of OF and I):, I, and equilibrium employment in each of the two sectors* (L ~ and L p) is found by evaluating ache demand schedules at ~ . An outboard shift in DF would result in an increase in W and a reduction in L p. The magni~cude of these effects depends, of course, on the slopes of ache demand and supply schedules. Suppose that these schedules are linear, so that they may be written as follows: supply: L aS t. bSW federal demand: LF aF ~ by priorate demand: Lp ~ ap - bpU bS, bF, bp 2 - 117

OCR for page 115
TABLE 1 Comparison of the Extent of Concentration of Federal and Company R&D Funds, 19 81 Federal Funds Percent of ~co~cal R&D funds spent by companies witch: 25, 000 or more employees 83 Fewer than 1, 000 employees 3 Percen~c of total R&I) funds spent by 20 largest RED performers Source: . 68 65 6 41 Company Funds Research and Developmene in Industry, 1081 . NSF 83 - 325 . Washington, DC: National Science Foundation, 1983, Tables B- B-ll, B-7 9. ! 1 8 - - _,

OCR for page 115
TAB Lo: 2 Comparison of the Extent of Stability of the Growth in Real Federal and Real Company R&D Spending, 1956~1983 Growth Rate 0 f Real Federal ILL Growth Race of Real Company 8&1) Spending Mean O 021 O 049 S tandard deviation . 074 . 031 Coefficient of variation 3 O 52 DO 63 )irlim~ ~ ~ t35 - . 028 Maximum O 231 . 090 Range .366 .118 Note ~ Federal and company R&D funds deflated using GNP implicit pric deflator ~ .. Source: Research and DeveJopmenc in Indusery 1983 National Science Foundation, 1985. - 119 - Washington OC . e

OCR for page 115
FIGtJRE 2 Marker for Scientists and Engineers: Impact of Shift in Federal Demand from O~ to D F on Cage Rate and Private Employment of Sciert~cists and Engineers ~~ t 4, It \ 1 OF l I ~ F - - - - \.~ Dp ~ S ~ \ D 1)' L'; Lp - 190

OCR for page 115
The equilibrium condition is L - LF + Lp. The maricet-cl~aring wage rat te is ~cher.efore ,,*_ aF + ap - aS and the effect of a unit shift in the federal demand curve on W is dW, 1 daF bS + bF ~ bp This effect will be larger, the smaller each of the parameters b. by and by is ~ that is, ache less elastic the supply and demand schedules are ~ . Unfortunately, very little is known about the values of the demand parameters by and bp. In view of the national security orientation of much federal R&D, and the limited incentives deco minimize costs provided by federal con~cracting methods (for example, cost-plus ), one might reasonably cony endure chat bF is fairly small, perhaps very small, re latitude deco bp . Me lack of info rmation about b is particularly unfortunate, since the effect of a shift in federal demand on private RED employment, A, is -bp. OCR for page 115
Freeman analyzed the behavior of the markets for physicists and for engineers and the role of federal R&~) spending in determining their starting salaries during the period from the ~ ate ~ 940' s to the early 1970' s . He found that federal R&D spending played a preeminent role in determining the starting salaries of both Occupations: Changes in R&D expenditures were the chief factor influencing [eng~sleers' star~cing] salaries during ache post-Uc~rld War II period.. Me upswings and downswings in salaries car be traced to changes in federal R&D policy. And, Changed demand, due primarily deco changes in federal &1) spending, was the chief source of changes in physics salaries. ~ Emphasis in original ~ Freeman estimated the elasticity of engineers' starting salaries with respect deco federal R&D expenditure (both is constant dollars ~ to be ~ n the range . 26 to .41. In ache case of physicists, he estimated the federal R&D elasticity of starting galaxies by decree tearer. The estimated elasticities were in the ranges 0 34 deco .36 for individuals with bachelor's degrees, .54 to .56 for these with master's degrees, and .33 to 044 for those with Ph.D. 's. Freeman's estimates are summarized in Table 3. Federal obligations for R&D, in current dollars, have increased from $38 0 7 bills on in fiscal year 1983 ~co an estimated $520 3 billion in fiscal year 1985. This represents ~ nominal increase of about 30 percent or Lassoing a two-year increase in the price laurel of 10 percent or less ~ in increase in real federal R&D expenditure of at least 20 percent. Freeman's estimates imply that the federal R&D elasticity of both engineers' and physicists' starting salaries is in the neighborhood of one third or perhaps somewhat higher. An elasticity of one third would imply that the. growth in real federal R&D spending between 1983 and 1985 has increased real starting salaries by roughly 7 percent over two years (10 percent if we use the estimated growth in R&D obligations to industrial firms). Evidence about the effect of recent changes in the growth rate of defense spending generally (not just federal R&D spending) on the growth of engineers' and technicians' starting salaries has been obtained from college placement officers. Real defense spending increased rapidly beginning in about 1979 until this year when Congress and the Reagan Administraelon agreed to end the buildup by maintaining a zero race of real spending increase. College placement officers report that increases in starting pay for engineers and technicians, which "have ranged from ~ to 12 percent over the last half-d~-n years, are expected to average only 3 percent his year. n 122 -

OCR for page 115
D TABLE 3 Estimates of Elasticities of Real Starting Salaries of Engineers and Physicists with Respect deco Real Federal R&D Expenditures ~ estimates refer co the period from the late 1940 ' s to the early 1970 ~ s Es timated Range of Elasticity End neers . 26 ~ . 41 Physicists B . S O . 34 - . 3 6 ,Y . S O ~ S4 ~ . 5 6 Ph O D G ~ 33 ~ .44 Source: R. B. Freeman. "Supply and Salary Adjustments to the Changing Science Manpower Marice~c: Physics, 1948 - 1973, American Economic - Retries, Vol. 65, No. [(March 1975), pp. 27-39; and R.B. Freeman. "A Cobweb Model of the Supply and Starting Salary of New Engineers, " Industrial and Labor Relations Reviews, Vol O 29, No . 2 (January 1976 ), pp . 236 - 248 . ~ 123 _

OCR for page 115
.o Cal company- funded R&D, total sales, and sales to the government for over 9, 000 business segments, classified by extent of government orientation. Government-or~ented segments are defined as Chose for which the ratio of reported sales deco the government to the to Cal segment sales was at least 10 percent. This definition was adopted because a segment is required to disclose government sales only if the ratio of ache latter deco total sales is at least 10 percent. 1 The ratio of topical government sales reported by govers~mes`t - oriented segments to their total reported sales is S3 .5 percent; the corresponding ratio for ocher segments is less than one half of one percent . The average privately financed R&D inters icy of go~rernment-oriented segments is lo 72 percent, compared deco a value of O . 54 percent for other segments . lathe R&D intensity of go~rernment- oriented segments is 3.19 times ~cha~c of other segments, reflecting the fact that the total sales of ocher segments were 320 7 times those of government-oriented segments, Anile the total R&D expenditure was 10.3 times as high. The estimated difference between the R&D intensities of gover~en~c-oriented and other segments is almost identical to the estimated difference between the "used" R&D intens ities of defense and consumption expenditures implied by Scherer's deco. The substantial difference between the R&D intensity of federal expenditures and that of other components of the GNP suggests dacha. it is useful to represent the average private R&D intensity of the U. S . economy as a weighted average of the two R&D in~censi~cies, with shares in GNP as we fights: CEt.D ~ C=FED GNPFEI:) ~ C9DNON GNP GAPPED GNP GNPNON crd ~ crdf ~ f + crud . ( 1 where CAD GNP CRDF0 - G=FED , C8DNoN GNENON crd crdf crd-, GNPNON ~- GNP f) aggregate U. S . company- funded Red) expenditure gross national product company-funded R&D oriented toward federal purchases of goods and services federal purchases of goods and ser~r~ces CAD - C3DrED GNP ~ GAP FED CRD / GNP CRDFED / GAFFED GNPFE~ / GNP CADNoN / G~PNON . - 138 -

OCR for page 115
Suppose Marc crdf and crdn are cons cants . Then, the above equation imp lies that the variation over tinge in average R&D intensity (crd) can be interpreted as attributable in part to variance on in the federal share in GNP (f): crd ~ Sac ~ ~ crdn + ~ crdf - crdn) . f ~ ~ ~ The regression of crd on f has been estimated using annual data for the period 1956-1983. Because both series exhibit trend (positive in the case of crd, 2:ega~cive in the case of f), a time trend is included in the equation. In the presence of ache time trend, the coefficient on f captures the effect of a deviation of f from its trend value on the deviation of crd from its trend value. Loosely speaking, the coefficient measures the short-run (transitory) . rather than the long- run (permanent) influence of f on cord. Estimates of the model are presented in Table 10. The estimated coefficient on f is positive and its difference from zero is highly statistically significant, a finding consistent witch the hypothesis that changes in ache composition of final demand account for a significant fraction of the short~run ~raria~cion in crd.2 lithe point estimate of this coefficient, .036 9 is much larger- -about three times larger- - than the estimated difference ~ crap - crdn) implied by both the Scherer and the business segment data. This discrepancy may reflect ache distinction between short-run and long~run effects on R&D intensity of changing composition of demand. In any extent, all the evidence presented points to the conclusion that the amount of R&D expenditure firms choose taco sponsor, conditional on their total Parolee of sales, depends upon the distribution of their sales between government and other purchasers. Because ache underlying priorate R&D irrtens icy of goods purchased by the federal government is substantially higher than that of goods purchased by other final demand sectors (households, stance and local governments, for example), the equilibrium quan~ci~cy of private R&O is likely to be influenced by the composition (federal versus other) as well an the level of final demand. In econometric studies of the determinants of private Red) expenditure, it is conventional to control for total sales (at the firm or industry level) or GNP (~t ache aggregate level) but not for the composition of sales or GNP. Thus, these models embody the a priori restriction that a given increase in federal sales or demand has ache same impact on priorate R&D expenditure as a corresponding increase in nonfederal sales, a res Friction that is unlike ly deco be true . lathe fo flowing evidence suggests that inappropriate imposition of this restriction results in seriously (upwardly) biased estimates of the partial effect of federal R&D expenditure on priorate R&D expenditure. Estimates (based on annual time series data for the period 1956 -1983 ~ of the regression of aggregate company- sponsored R&D expenditure (CAD) an aggregate federal industrial R&D Expenditure (=D), GNP, and a time trend can serve as a benchmark. The CRD, - 139 -

OCR for page 115
T^~L~- ~ O Regression of Aggregate Company-Funded R&I) Intensity on Share of .-ederal Defense Purchases in GNP, Annual Data, 1956-1983 Es ~ ima ted S tandard NTar'~ble Coe~ic;-en~c Error t- "at 0 f O.03611 0.01016 3. 5~3 ~ ime trend 0.00021 0.00002 10.054 interc ep sac - O . 415 0 . 042 6 - 9 . 7 3, p ~ . .377 sad. error (p) ~ . 189 R2 ~ . 96 10 Notice: f is defined as ache ratio of federal purchases of goods and se~~.ces to GNP. 1'.0 -

OCR for page 115
FRY, and SAP are all deflated us dung the GNP implicit price deflator.' Because of the uncertainty about the timing of the relationship between COD on the one hand, and E.RD arid GNP on the o ther, a future and a past value, as well as the con~cemporaneous value, of both FAD and GNP are included in the equation. Me estimated equation was of the form: So + 51E=~t + 1) + B2F~D(t) + B3=D(t ~ L) 1 ~ t2G~P(t) + <3GNP(t - 1) + /- t Estimates of the effects of ERD and of GNP on COD are based on the es timated sums of the corresponding coeff indents, that is ~ B B2 ~ B3) and (~: + Y2 +~3) . respec~ci~rely. Estima~ces of these suns, their standard errors, t- ratios, and associated probability; values are shown in the left-hand Coleman of Table 11. In this model, the point estimate of the son of the ED coefficients is 033, a value similar to (slightly larger than) th'8corresponding parameter estimate obtained by Levy and Terleckyj using a similar data- set and specification The estima~ced sum is highly significantly different from zero, a finding also consisten~c with Leery and Terleckyj ' s results . The right-hand Calvin of Table 11 displays statistics about the suns of coefficients when GNP is disaggregated into its components, FEI)GNP (federal purchases of goods and services) and NONGNP (GNP - FEDGNP). The res~criction Chat FEDGNP and NONGNP have identical coefficients, imposed a priory in the lef~c-hand column estimates, is no longer maintained in the right-hand column estima~ces. As expected, the estimated sea of the coefficients on ache FEDGNP terms is much larger- - about ten times larger - than the estimated son of coefficients on the NONGNP terms. lathe hypothesis that the sues of ache FEDGNP and NONGNP coefficients are identical may be rejected at the . 05 significance level. It is the reduction (relative to the restricted model) in the size and significance of ache sun of the FRD coefficients that is of primary concern. The estimated effect of ERD on CAD is only one shirt as large when FEDGNP and NONGNP are allowed to have different coefficients as it is when these coefficients are constrained to be equal. Moreover, the estimated see is insignificantly different from zero. Even though FEDGNP appears, in a highly restricted fashion, on the right-hand side of the restricted model, the fact that FEDGNP is a relatively small fraction of total GNP (in the neighborhood of 10 percent) implies that the restricted model does not adequately control for variation in federal demand. Relaxing the restriction in effect controls for the influence of federal demand and makes it possible to obtain a truer estimate of the partial effect of federal R&D expenditure on private R&D. Because FRD is a component of FEDGNP (~hat is, federal purchases of R&D services are included in total federal purchases of goods and services), the statistical insignificance of FRD signifies not that

OCR for page 115
TABLE 41 S to tistics from Aggregate Real Comply- Funded R&D - Expends Cure Regressions ~ ~ values in parentheses ~ Res tric~ced~ Made ~ Unres Eric ted "ode t Sum of FAD .330 .log coefficients ~ ~ (2045) (0. 60) ~ ~ O 013 a ~ 280 Sum of GNP O 006 coe ~ficients ~ ~ c 19 ~ ~ ~ ~ 125 Sin of FrDGNi . 052 coefficients (2 . 09 ~ ~ ~ . 028 She of NONGNP . 005 coefficients ~ 0 . 9 2 ~ a ~ .187 YEAR . 301.7 327.0 ( 1 e 54 ) ( ~ ~ 5 8 ) p ~ 404 ~ 173 (1~. 82) (O ~ 66) Wrote: ~ denotes probability value for testing null hypothesis that sum of corresponding coefficients equals zero. - 142 -

OCR for page 115
~ has no effect on priorate it&l) expenditure, but rancher Chat one cannot rej ect ache hypothesis that FRD has the same impact on private R&D expenditure as do federal purchases of non-R6`D services and products. In other words, the insignificance of AD implies that federal R&D expenditures do not have an impact on prince R&D above and beyond that of federal non-~&l) expenditures, although ache point estimate of the impact is about three times as large. If a similar model were es timated on a larger data set ~ for example, cross - sectional or longitudinal data for a large number of firms ), the estimated difference between the effects of federal R&D and non-A&D purchases Light be significantly different from zero. Butch as in the case here, controlling for ache composition of sales or demand. is likely deco reduce the estimated effect of FRD on CRD substantially. SUMMARY ANI) TENTATIVE CONCLUS IONS It has been argued in this paper that there are two principal mechanisms or paths by which federal industrial R&D may influence the cost to private R&D sponsors of producing given improvements in products and processes. Increases in federal R&D expenditure may, in principle, increase the average and marginal cost of private R&D performance by driving up the prices of R&D inputs of which the supply is less than perfectly elastic. On the other hand, federal R&D may lower private R&D costs by generating knowledge that is useful in the priorate production of innovations, hence raising the producti~ri~cy of privately employed R&D inputs. There is reasonably strong econometric evidence supporting the hypothesis that increases in federal R&13 result in significan~cly higher ~ starting) salaries of scientists, engineers, and technical personnel, at least in the short run. Because the supply of technical manpower is much more elastic in the tong run than it is in the short run, the effect on wages and salaries of a federal R&D increase is probably much smaller in the long run than it is in ache short run, and it may even be zero. Evidence regarding ache incidence of cost-reducing (from the perspective of private R&D sponsors ~ spillovers from federal R&D is extremely limited. Data on the distribution of contract R&D by restages of R&D, on the distribution of property rights to government-financed contractor inventions' and on the extent of licensing of government-owned patents all suggest that most federal industrial R&D expenditure produces little or no spillover. It is probate ly the case, however, that a small fraction of federally supported R&D generates very large spillo~rers (some of which may be negative ~ . I.c does not appear pass ible Deco estimate directly, from data actually or even po~centially available, the magr~i~cude of private R&D cost reduction yielded by the "average" dollar of federal R&D expend) ture . - 143 ~

OCR for page 115
Because it is not feasible to observe or directly estimate the net effect of federal R&D on the supply schedule of private R&D output, a number of inves tigators have attempted to draw ind~rec t inferences about this effect from estimates of the reduced- form relationship between privately funded and federally funded R&D expenditures, controlling only for focal demand. If the demand for privately produced innovations were stable, or if shifts in demand were uncorrelatad Erich changes in federal R&D, then the finding of a significant positive relationship between federal and company Rap) expenditure would provide rather strong evidence of spillovers. It has been demonstrated here, however, that changes in real federal R&D spending are correlated with changes in the composition of final demand, which shift the aggregate demand for private R&D Output. When statistical controls for the composition as well as for the level of demand are used, the hypothesis that federal R&D expenditure has ache same effect on priorate R&D expenditure as federal expenditure on non-A&D services and products (which stimulates considerably more private R&D than other components of final demand) cannot be rej ected. Because federal R&D expenditures are a relatively small fraction of total federal. purchases of goods and services, and do no appear to have ~ significan~cly larger effect on pri~ra~ce R&D than federal nonskid purchases, one must consider. and take account of government procurement behavior as a whole, and not just R&D contracting, deco understand the extent and nature of federal influence on private decisions to invest in R&D. 144

OCR for page 115
NOTES AND REFERENCES For a critical review of some of this literature, see F. Lichtenberg;. 'the Relat' onship Between Federal Contract R&D and Company R&D. ~ In American Economic Association Papers and Proceedings. Nashville, TN: May 1984. See, for example, Z. Griliches and F. I-ichtenberg. "R&D and Productivity- at ache Industrial Level: Is There Still a Relationship? " In ~R&D, Parents, and Productive By. Evinced by Z. Griliches. Chicago: University of Chicago Press, 1984. 3 . There also are problems associated with measuring ache quant' ty of ~ real ~ R&D input emp toyed for a particular purpose . Three such problems are worth noting. First, there may be problems of measuring total R&D expenditure in current dollars. A recent National Science Foundation report ~ see A Comparative Analysis of Information on National Industrial R&D Expenditures. NSF 85 - 311. t~ashing~con, DC: National Science Foundation, 198S retreats discrepancies between es timatPs of R&D expenditure reported by firms in their responses to the NSF/Census RD- ~ survey and Chose reported in 10-K reports to ache Securities and Exchange Commission; evidently, these discrepancies are due large ty to different treatment o f expenditures on " engineering and routine technical ser~rices.'' Second, there are problems associated with the deflation of nominal R&D expenditure required to obtain an index of real R&D input . ~ Industry- specific R&D deflators, however, have been developed recently by Mansfield; see E. Mansfield. "Price Indexes for R and I) Inputs, 1969-83. " video. ~ Third, the segregation of tomcat. R&D expenditures into company- and go~rernmen~c- funded components, particularly at the firm or industry Petrel, is problematic. The difficulty arises, in part, because of the way in which Independent Research and De~relopmen~c (IR&D) expenditures are reported in the official (NSF) R&D statistics. (For a detailed discussion of this issue, see J. Reppy. The IRKS Pro gram of the Departmen~c of Defense. ~ Cornell University Peace Studies Program Occasional Paper No. 6, March 1976; and J. Reppy. "Defense Department Payments for 'Company-Financed' R6iD, ~ Research Policy, Vot. 6(1977), pp. 396-410.) Serious as these problems of R&D input measurement are, they pale in comparison deco the conceptual and practical difficulties of measuring R6:D output. 4. See, for example, M. Nadiri and I. Prucha. "Comparison and Analysis of Productivity Growth and R&D Investment in ache Electrical Machinery Industries of the United States and Japan. " Pacer presented at ache National Bureau of Economic Research Conference on Productivity Growth in Japan and the United States, Cambridge, MA, August 25 - 28, 1985 . ~ 145

OCR for page 115
~ . E. Mans fly eld. "Commentary: Capital Formation, Technology, and Economic Policy. n In "Industrial Change and Public Policy, " from ~ shakos in sponsored by the Federal Reserve Bank of Kansas City, Missouri, 1983, p. 261. The smaller be is, the greater do /da~ is, the wage increase resulting from a given federal demand shifts but ache smaller the private employment response to a gives wage increase . S ince the latter is a first ~ order effect and the former is a second~order effect, for gibes bS and bF, the absolute value of P is increasing in be,: P _ - (1 + S + F ) i daF bp bp 7 . R. B. Freeman. "A Cobweb Model of ache Supply and Starting Salary of New Engineers," Industrial and Labor Reasons Review, Vo: . 29, No ~ 2 ( 1976 ), p ~ 244. 8 . R. B Freeman. "Supply and Salary Adjustments to the Changing Science Manpower Market: Physics, 1948-1973, n American: Economi c Revi en, To 1 . 6 i, No . 1 (March 19 7 5 ), p . 3 7 . 9. Federal obligations Go industrial firms for R&D, in current dollars9 have increased more rapidlyabout 40 percent from $18 0 6 billion deco an estimated $270 7 billion. 10. See 'For Military Suppliers, Growth Uncertainties, n New Fork Times, Section 12 (October 13, 1985 ), p . 10 . Research and development, or the management or ad~m~nis~cration of R&D, was the "primary work activityn of 62 percent of the 186,000 engineers reported deco be working in the area of national defense in 1982 (see 1982 Postcensal Sunder of Scientists and Engineers. NSF 84- 330. Washington, DC: Na~ciona1 Science Foundation, 1984, Table Be 13) . Hence, most of the changes in engineering salaries associated with changes in overall defense spending probably are at~cribu~cable to changes is defense R&D expenditure. 11. M. Shankerman and A. Palces. "Estimates of the false of Percent Rights in European Countries during the Post-1950 Period. ~ National Bureau of Economic Research Forking Paper No. 16SO, June 1985, pp. 19-21. . Product and Service Codes. Fairfax, VA: Federal Procurement Da~ca Center, 1982, p. 8. 3. Annual Report on Gotrernmenc Parent Policy. Washington, DC: Federal Council on Science and Technology, December 1971 and December 1972 combined. 1' 6

OCR for page 115
14 . Government and Technical Progress: A Cross - Indusery Analysis. Edited by R. Nelson. New York: Person Press, 1982. 15. Jacob Schmookler. Invenriora and Economic Growth. Cambridge, but\ Harvard Unifiers ity Press, 1966 . 16. M. KAmien and N. Schwartz. market Structure and Innovation. Cambridge: Cambridge University Press, 1982, p. 36. 17. D. Mowery and N. Rosenberg. "The Influence of Market Demand Upon Innovation: A Critical Review of Some Recent Empirical S tudies, " Research Policy, sol . 8 ( 1979 ), pp . 102 -15 3 . 18. Ibid. 19. F.~.. Scherer. "Using Linked Patent arid R&D Data to Measure Interindustry Technology Flows." In R&D, Parents, and Productivity. Edited by Z. Griliches. Chicago: IJni~rersi~cy of Chicago Press, 1984. 20. National Science Board. Science Indicators, 1982. Washington, DC: U. S . Government Printing Office, 1983, pp . 7-8 . 21. A relatively small number of segments for which the ratio is be low 10 percent also report government sales . 22. In view of the rule for reporting government sales, this is a lower bound estimate of the true ratio for other segments. I t is cus tomary, and appropriate, when analyz ing re la~cionship s among time series, to de trend or otherwise render stationary the series. Inclusion of the time trend reduced substan~cially, but did not eliminate, serial correlation of ache residuals, so an adjustment for first-order serial correlation was made in es timating the mode l . 24. The partial r2~ ~c2/(t2 + resid. do f. ~ ~ on f is . 34. 25. An equation is which CRD is expressed as a function of FRD and GNP (or itch components ~ may be interpreted as the reduced form of a system of supply and demand (of private RED output) equations in which "D appears in (shifts) the supply equation and GNP appears in the demand equation. Such a system may be represented as fo flows: supply) Ps ~ aO + alC8D + a2FRD (demand) PD ho + b~CRD + b2GNp where PS denotes the " suppl3r price " (marginal cost), and PD the " demand price n (marginal returns ), of private R&D output . equilibrium, PS ~ P:> Equating the right-hand sides of the supply and demand equations, and soldering for CRD, _ ~ 4 7

OCR for page 115
COD (50 ~ Do) 2 ~ + 2~ GL;? (~1 - b:) (~1 ~ bl) (a1 - b: ) GNP mav be expressed as the soul of two co~npones~ts, FEDGNP ~ federal purchases of goods and services ~ and NONGNP (GNP F_DGNP). The evidence cited above suggests that a billion-dollcar increase in FEDGNP strifes the demand for private R&D output substantially more than a corresponding increase is NONGNP does. Hence, the restriction that FDGNP and NONGNP have identical coefficients in the demand equation should be relaxed ~ and tested). Since FIND is correlated fairly highly with FEDGNP, relaxing this restriction is likely to have a nontrivial effect on the size and significance of the reduced- foot coefficient on FEDRD . 26. Unfortunately, Mansfield' s it&D-deflator time series begins one y in 1969. See E. Mansfield. "Price Indexes for R and I) Inputs, 1969 - 83 . " Video . 27. lathe author experimented witch a large number of alternative specifications and found that estimates of stems of coefficients were much less sensitive to minor changes in equation specification than were estima~ces of individual coefficients. 28 . D. L eve and N. Teriecicyj . "Effects of Government R&D on Or ~ date R&D Investment and Productivity: A Macroeconomic Analysis," Bead Journal of Economics,, Vol. 14~1983~, pp. S51-561. Because values corresponding to periods ~ ~ + 1 ), ~ t), and ~ t - 1 ) of FRD, FEDGNP, and NONGNP are included as regressors, and each of these series is fairly smooth 5 individual coefficients Thor example, the coefficient on FEDGNP(~c) ~ are estimated imprecisely. Thus, tt is nor possible to rej ect the hypothesis tha. (using variable names to represen~c the names of their respective coefficients) FEDGNP (~c + j ~ - NONGNP (t ~ j ), j - -1, O. 1-- the hypothesis that one would test by comparing ache residual sums of squares of the restricted and unrestricted models. The hypothesis ~; (FEDGNP(t + j ~ NONGNP(t + j ~ ~ ~ O. however, can jest, be rej ected at the 5-percent level of significance . 30. Because, however, as is observed below, FED is a component of FEDGlJP, Me point estimate of the effect of Fed on CRD is . 161 ~ ~ .109 + . 052), about half of ache estimated effect in the restricted model. 31. Disaggregating GNP into its components also has the (desirable) effect of reducing the extent of serial correlation of =he res iduals substantially . 148