patents, innovation and growth

THE ECONOMIC RECORD. VOL. 16. NO. 234, SEPTEMBER 2030.255-262

Patents, Innovation and Growth MARK CROSBY*

Department of Economics, University of Melbourne, Parkville, Victoria 3052

New growth theories emphasize the role played by innovation in promoting economic growth. Since it is difficult to quantify the amount of innovation undertaken in an economy, there is little available empirical evidence assessing the contribution made by innovation to growth, in contrast to abundant evidence on the role of physical capital accumulation in the growth process. In this paper patent data are used to proxy the amount of innovation undertaken. in an economy. The patent data are used to explore two questions. First, how important is innovation to economic growth in Australia, and second, are reductions in innovations sourced in Australia offset by innovations in Australia?

I Introduction Recently in Australia a great deal of attention

has been given to the need for higher national savings. It is assumed that higher national savings will lead to both higher economic growth, and a reduced reliance on foreign savings to finance domestic expenditure, and so a lower current account deficit. Policy measures in the 1996 Federal Budget aimed at increasing savings included cuts to funding to tertiary education, and reductions in subsidies to R&D. In this paper it is pointed out that such cuts may be growth reducing, if these policy changes lead to reductions in innovation levels in Australia. The results in this paper are consistent with new growth theories, which emphasize the role played by human capital and innovation in economic growth, as well as emphasizing the role played by savings and investment.

The structure of the paper is the following. In

* I would like to thank Les Rymer from Ip Australia for his helpful suggestions and assistance with the col- lection of the data, and seminar participants at the Mel- bourne University macroeconomics breakfasts and at the Conference of Economists in Hobart for helpful comments.

increases in foreign sourced

the next section some related literature on economic growth and R&D is discussed, and the strengths and weaknesses of the patents data are examined. In Section III the data sources are described. Section N contains empirical estimates of the impact of innovation on growth, and of the reIationship between domestic and foreign sourced innovation in Australia. The final section offers concluding comments.

11 Related Literature In the last decade, macroeconomists have

renewed attempts to explain what determines technological progress. Endogenous growth models, such as Romer (1986), stress the role of innovation and imperfect competition in determin- ing economic growth. These models can be thought of as complementary to traditional growth models such as Solow’s (1956), which treat technological progress as exogenous, and instead focus on the role played by capital accumulation in driving economic growth. A difficulty with the new growth models is that they are very difficult to implement empirically. When hying to quantify the impact of innovation on growth, innovation is usually measured using imperfect proxies such as R&D expenditure, or employment in the R&D

2.55 2000. The Economic Society of Australia. ISSN 00134249.

256 ECONOMIC RECORD SEYlEMBER

sector. In this paper I use pate& data as a measure of innovation. W e this measure also suffers from certain deficiencies, which are discussed in this section, it is hoped that an accumulation of empirical results using different data and sources will lead to some empirically based conclusions about the factors driving technological progress. In this section I begin by dis- cussing some related literature which uses R&D data as a proxy for innovation, then discuss the literamre which uses patents as a proxy for innovation. I also discuss the potential pitfalls of using patents data to measure innovation.

Two proxies which are often used to measure the amount of innovation occurring in a country are the level of R&D expenditure, or the level of employment in R&D. Lattimore (1991) surveys recent trends in R&D statistics by industry and by country. Compared with similar countries overseas, Australia has had low, but rapidly growing, levels of business expenditure on R&D. Lattimore argues that it is very difficult to assess the notion that R&D levels are too low, though he does note that the 150 per cent R&D tax concession in Australia seems to have stimulated a modest amount of new R&D. The Bureau of Industry Economics (BE 1993) reached a similar conclu- sion. The BIE also estimated the net social return to the tax concession, which they found ranged from -5 to 25 per cent

With international flows of capital and ideas it is not neccessarily the case that small countries need to conduct large amounts of R&D. It might be the case that countries like Australia can borrow foreign R&D through either the purchase of new goods and processes. or through the purchase of patent rights. If there are economies of scale in innovation production, and if innovations are not too specific to the innovating country, then this can be cheaper for Australia than conducting R&D directly. However, R&D will be important to Australia’s growth whenever foreign innovations are not easily transferable to Australia, or when foreign innovators are able to expropriate large rents from their innovations. In this paper some idea about whether there is any substitutability between domestic and foreign innovations is gauged by estimating the response of Australian patenting activity to movements in foreign sourced patenting activity in Australia.

Coe and Helpman (1 995) use R&D statistics to construct an innovation capital stock variable, which they use to assess the impact of foreign innovation on the domestic economy. They find

that foreign R&D has beneficial effects on domestic productivity, and these effects are stronger the more open is the economy. They also find that estimated rates of return on R&D are very high. Lichtenberg and van Pottelsberghe de la Potterie (1996) extend the work of Coe and Helpman, and find that foreign direct investment and imports are important channels through which innovations are diffused across countries. Rogers (1995) con- structs knowledge capital stock variables for Australia using both R&D variables and patents data, and uses the constructed variables as regres- sors in equations to explain multi-factor productivity from 1972 to 1990. He finds that knowledge capital stocks are sigxuficant explanatory variables in these equations, and also that differences in the measured spillovers when patents data rather than R&D data are used is small.

R&D data suffer from several problems when used as a proxy for the level of innovation. Fit, R&D is an input to innovation outputs, rather than a measure of innovation occurring in an economy. The relationship between R&D and innovation outputs is likely to be time varying, possibly non- linear, and is also likely to occur with uncertain lags. Patents data suffer from some of these problems, and some others, but have the principal benefit that patents are more likely to be related to innovation outputs than R&D data. In addition, patents data are available for a far longer time span than R&D data, so that it is possible to conduct time-series analysis on patents data, which is not possible with existing R&D data. Patents data suffer from the problem that certain patents are likely to reflect important, productive, inventions, while other patents are unlikely to increase productivity and GDP. Similarly, an equivalent amount of R&D expenditure at two points in time can sometimes produce no productive innovations, wMe at other times can produce innovations likely to greatly increase productivity and growth. This implies that both R&D and patents measure innovation with error. Since this is the case, it is important that both data sources be utilized to assess the importance of innovation to economic growth.

Schmookler (1966) provided a detailed examination of the usefulness of patents data, and concluded that patents data are complementary data on important inventions, and that ‘it will be sufficient to think of patent statistics merely as an index of the number of inventions made for the private economy in different fields and periods’ (p. 23). Schmookler also found that the numbers

,

U X X ) PATENTS, INNOVATION AND GROWTH 257

of technological workers and R&D expenditure are reasonably highly correlated with patents statistics. These conclusions are consistent with the idea that patents data will provide a useful measure of innovation. Schmookler also examined the role played by aggregate demand in stimulating innovation, along with the role played by innovation in stimulating technological progress.

Encel and Inglis (1968) examined Australian patents data, and noted the role played by foreign corporations and individuals in Australian patenting activity. They also used graphical techniques to examine the relationship between GDP and patent applications. They found that there is no signifcant relationship between the annual series for the period 1920-1939, though they 'show a similar general trend from 1924 onwards.' Like Schmookler, they point out that the number of patents granted is affected by wars, reflecting staffing levels within the patents office, rather than changes in inventive activity. For this reason Encel and Inas choose only to examine the inter- war data when examining the relationship between patents and GDP. They also find that Australia's dependence on imported know-how rose after 1945. Interestingly, Stubbs (1968), writing on innovation in Australia, downplays the usefulness of patents data, on the grounds that patents ascribe equal importance to all patents, 'whether crackpot inventions, or works of genius'. This is really the point about measurement error made above, and is applicable to R&D data, as well as to patents data.

Devinney (1994) characterizes patenting activity in a panel of countries, and estimates the relationship between patents and economic growth for his panel. In looking at the relationship between the changes in these two variables Devin- ney is implicitly highlighting the short-run (high frequency) comlations, where he finds that patents and growth are positively correlated. This is in contrast to the discussions of Schmookler, who argued that the long-run relationships should be positive, but the short-run relationship may in fact be negative. This is the idea that during a recession individuals tinker away in their shed and innovate! In this paper it is the long-run oow frequency) correlations which are examined in the Australian time-series data

The Australian Industrial property Organization (AIPO, now reriamed IP Australia) survey the uses and properties of patents statistics (AIPO 1996). and argue that patent applications and grants data can be used to derive information on

technological trends and capabilities. However, some weaknesses of patents data are also noted. In particular, a signrficant fraction of technological innovations made in manufacturing do not result in the seeking of patent protection. If this is the case the patents data will be downwardly biased as a measure of innovation. However, as long as the propensity to patent has not changed over time, we can still use trends in patents to identify trends in innovation. A number of institutional changes in the last 20 years may also work to offset this bias. Specifically, the creation of the European Patent Office and the establish- ment of the Patent Cooperation Treaty were both designed to simplify international patenting. More recently, changes in some countries to ensure that they are Trade Related Intellectual Property (TRIPS) compliant will also increase patenting activity, though it is unlikely to be the case that this change will affect the data during the time fiame u ~ e d in the empirical sections ofthis paper.'

An additional pitfall with the patents data is that some companies patent using the names of subsidiaries, and also some multinationals may patent in countries other than the country where research was conducted. Hence a change in the mix of patenting conducted by domestic as opposed to overseas residents could simply reflect a change in this activity. Some information on whether this is a problem can perhaps be gained by examining Australia's patenting activity in the United States. In this case, it is found that 13 per cent of patenting activity is conducted by firms, 17 per cent by large Australian institutions (such as universities) and the remainder by individuals. While foreign patenting in Australia is likely to be quite different to Ausmlian patenting in the US, these figures do give hope that trends in patenting activity by foreign residents in Ausralia do reflect trends in innovation by foreign residents, rather than changes in the nature of multinational patenting activity.

In short. both patents and R&D variables measure innovation imperfectly. However, the aim of this paper is to provide a piece of evidence on the relationship between innovation and growth, in the hope that cumulation of evidence from different sources will give us a clearer picture of this relationship. I would not argue that this paper, or any other, in this empirical literature captures perfectly the relationship between innovation and growth.

See IP Australia (1998) for further details on these institutional changes.

258 ECONOMIC RECORD SEPTEMBER

III The Data In this paper the patents data utilized are annual

data on patent applications made in Australia from 1901 to 1997. Patent applications rather than patents granted was chosen because applications should reflect actual innovative activity in a given year, while patents granted can vary from year to year because of changes in the number of patent examinm rathm than rates of innovation (this problem is noted by Schmwkler 1966, and by ADPO 1996). It should be noted that patent applications are neccessarily made before the commer- cial exploitation of an invention, and many atent

The patent data were collected from three sources. Data for the period prior to 1951 is from annual patent indexes, and from WIPO (1983). While totals are available for patent applications, data do not appear to be available for the years 1919-1950 for the country of residence of patent applicants. Data for the period since 195 1 is taken from WIPO (1983). and from the Patent, Trade Marks and Design Office Annual Reports (various issues).3

Data for GDP and civilian employment is taken from Maddock and McLean (1987) updated with data from the Reserve Bank of Australia Bulletin (various issues) for GDP, and from the DX database (the annual average of the series VNEQ.AN-") for civilian employment since 1961.

In Figure 1 the logarithm of GDP is graphed, along with the logs of total patents, and patent applications by domestic and by foreign residents. Several features of this figure are of interest. First, while total patent numbers show considerable variability, the trends in this series and in GDP appear similar until around 1970, when there was a sharp decline in patent applications. Second, there has been a steady increase in patent applications sourced from overseas residents. The data available until 1920 show domestic patenting to

2 In order for a patent to be granted, an.jnventim must be both novel and useful. This obviously leads to some issues relating to the definitions of novel and useful. Schmmkler discusses the impact that a change in a US patent judge had on patent rates in the I US in the 1950s. because this judge required 'a flash of genius' of an invention for it to be patentable. Once again this is really an issue of measurement error-in measuring long-tun relationships it is hoped that there are no systematic trends in this type of error.

Data for 1941 for total patent numbers were missing. In the regression analysis the midpoint of 1940 and 1942 data was used.

applications do not lead to patents granted. P

FIGURE 1

GDP and Patents Data in Logarithms

be greater than foreign patenting over this period. Since the early 1950s. however, most applications have been made by foreign residents. Both of these features of the data were also apparent in the US data analyzed by Griliches (1990). Finally, all of the decline in patenting in the 1970s is due to a decline in patenting by foreign residents. Indeed, after remaining fairly static from 1951 to 1980, patent applications by domestic residents rose sharply in 1980, and have remained high since. In the next section some formal empirical work is used to examine the data more carefully.

N Empirical Results (i) Innovation, Productivity and Growth

The patent data are used in this section to explore two questions. First, how important is innovation to growth, and second, is there a dif- ference between domestic and foreign sourced patents in terms of their impact on productivity and growth. Since patent applications may take some time to become innovations, and because innovations may take some time to improve growth, I focus on the long-run relationships. In order to explore the relationship between innovation and growth a procedure outlined by Fisher and Seater (1993), and based on the following VAR is utilized

ecL>M, = WMY, + et'

where L is the lag operator, A is the first differ- ence operator, Pt is the log of the number of patent

?WAY, = WW, + +*

m PATENTS. INNOVATION AND GROWTH 259

TABLE 1 ADF Statistics

~ ~~

lny, Luny, Inprod, . W r o d , InP, W, ADF statistic (1 lags) 0.60 -6.63 .15 -6.37 -0.39 -5.31

(2 lags) 0.65 -5.28 .I6 -5.58 -0.28 -5.58 (3 lags) 1.01 -5.38 .46 -6.47 -0.02 -6.32

The 5% critical value for this test is -2.86.

applications in year t , y t is either real GDP or labour productivity in year t, and are error terms. Fisher and Seater (1993) argue that we can measure the long-run impact of P on y using the long-run derivative,

where hj, a P r + j / a ~ ~ # 0. This last require- ment ensures that shocks to the patents data per- manently affect the level of patents (or that the patents data are Z(1))4 The long-run derivative telIs us whether these permanent shocks to the level of patent applications have any long-run effect on real GDP or on labour productivity.

Under the assumption that COV(E l, E 2, = 0 and that the patents data are exogenous5, the my,, = q(l)/y(l) can be estimated consistently usmg the estimator limk-, Bk in the following OLS regression:

Yr - Yr-k-1 = a, + BkVr - PI-k-1) + ekr (2)

In practice the researcher will not have an infinite data set, and so it is typical to examine the graph of pk for k as large as is reasonable given the data set. In this paper the maximum k chosen was 30,

4 The statistical properties of the patents data and of GDP wiU be examined below. Different tests are appropriate for different orders of integration, though the principles remain the same. This section is written assuming both variables are 1(1). See. Fisher and Seater (1993) for further details.

5 These two assumptions are sufficient to identify the model. The assumption that the residuals in the two equations are uncorrelated is a standard one in VAR analysis. The exogeneity requhment amounts to assuming that the number of patent applications are not affected by contemporaneous GDP or productivity. This assumption is presumably correct since even when patent numbers are affezted by GDP it is likely that there is a lag between a fall in GDP. say, and a rise in patent applications. This lag is probably at least a year.

the same as the maximal k chosen by Fischer and Seater (1993) with a similar length data set. With the maximum k set equal to 30 the first 31 obser- vations cannot be used in the above regressions. Once the maximum lag length has been chosen

constructed for the relevant series for each k from one to thirty. T h t y regressions are then run, one for each k, and the graph of p1 to h0 and the appropriate confidence 'intervals indicate whether the long-run derivative is significantly different from zero and of (the anticipared) positive sign. The long-run impact of a change in patents on output or productivity is assumed equal to h0.

Before examining the Bk it is neccessary to explore the statistical propexties of yr and P, For shocks to P to have permanent effects on y, it must be the case that both P and y contain stochastic trends (are Z( 1)). In other words, if both P and y are stationary, then changes in P can have no long-run impact on y, since y is always return- ing to a deterministic trend. There have been a number of papers examining the statistical prop erties of GDP in Australia, and these papers generally find that Australian GDP is Z(1). Strong and Tan (1991) find that a stochastic trend model best represents Australian GDP, while Layton (1994) finds that a stochastic trend with stochas- tically switching drift characterizes the data. In this paper Augmented Dickey-Fuller tests are used to determine the order of integration of y and of P . Results from these tests are presented in Table 1. In each test a constant but no trend is included, along with the number of lags specified.6 nese tests suggest that we cannot reject the null of a unit mot when considering each variable in (logs of) levels, but the unit root null can be rejected when we examine the differenced variables. In other words, each variable is stationary in first differences.

the variables yr - Yr-k-l and Pt - Pr-k-1 C a n be

6 These regressions were also run with a trend included and the same concIusions were reached.


0.-

O N

op

P

f (LID

FIGURE 2 Coefficient on Patents

Dependent Variable Real GDP

I-. , . , ..

FIGURE 3 Coeflcient on Patents

Dependent Variable Productivity

I

4.1 I

We now consider the long-run neutrality tests based on (2). In Figures 2 and 3 we present graphs of the long-run coefficient estimates, along with two standard error bands. The dependent variables considered for these regressions are real GDP and labour productivity, and the independent variable is the total number of patents (domestic and foreign sourced). All variables were available from 1901 to 1997. For both dependent variables the long-run coefficient estimate is both significantly different from zero, and positive. This is consistent with increases in patent applications (innovation) leading in the long-run to increases in both real GDP and in labour productivity. The long-run coefficient estimate, b0, is

approximately 0.36 for GDP, and 0.14 for labour productivity. This is consistent with a 1 per cent rise in patents leading to a .14 per cent increase in the level of GDP per worker. Taken at face value these results suggest that the decline in innovation beginning from the late 1960s is reducing GDP growth and labour productivity. It should be noted that it is not a priori obvious what the sign of these coefficient estimates should be. Romer (1990) argues that it is possible that too many resources be devoted to innovation, if the monopoly rents accruing to innovators are high enough. This would imply a negative long-run relationship between changes in innovation and economic growth. It is also noted that the coefficient estimates are negative in both sets of regressions for k less than 15. This is consistent with the findings of Devinney (1994), who finds that the short-run correlation between patenting and GDP growth is negative. A simple interpretation of this finding is that increasing the level of patenting uses up resources which would otherwise increase GDP and productivity, but leads to increases in output at a later date.

A number of checks on the robustness of these results have been performal. The graph of the patents data shows a fall in the number of patents applications during both of the world wars, which may be affecting the results. It is also possible that the data are of poor quality prior to World War II, and that this influences the long-run coefficient estimates. In Table 2 estimates of A. for just the post-war subsample, and when wartime dummies are included, are presented. The table shows that the results are generally robust to the inclusion of wartime dummies, or to examination of only the post-war subsample. The impact of the total number of patents on GDP and on productivity remains positive and significantly different from zero, though the long-run elasticity is slightly smaller in both cases. It is also possible using the shorter data sample to separately examine the influence of foreign and Australian sourced patents on GDP and productivity. These long-run estimates are also presented in Table 2. These estimates are significant for only two of the reported four cases, but the coefficient estimates are negative. However these results do not appear to be very robust. In al l cases & is insignificantly different from, and closer to, zero. Changes in the size and sign of the estimates for small changes in k do not affect the other results reported in the paper, and it is concluded that the available data do not allow precise estimates of the separate

PATENTS, INNOVATION AND GROWTH 261

TABLE 2 Estimates of k0

~~ ~~

Dependent Variable

GDP GDP

Productivity Productivity

GDP Productivity

GDP Productivity

.Patent Variable

Total Patents Total Patents Total Patents Total Patents Aust Patents Aust Patents

Foreign Patents Foreign Patents

Sample Period

1901-97’ 1946-97

1946-97 1950-97 1950-97 1959-97 1950-97

1901-97’

a, .25* .24* .12* .09*

-.08* - .08 -.32* -.19

Notes: + indicates that the regression included additive dummies for the world wars. * indicates significantly different from zero at the 5 per cent level.

influence of Australian sourced and foreign sourced patents on GDP and productivity?

(ii) Relationships between Foreign Sourced and Domestic Patenting Activig

A natural question to ask about recent reductions in subsidies to R&D expenditure is whether we ought to be concerned about any subsequent falls in innovation activity in Australia. A possible reason why we might not be concerned about such falls is if it were the case that declines in innovation in Australia were simply offset by a greater propensity to ‘borrow’ innovations from overseas. This could occur through Aushalian firms and individuals looking overseas for patent innovations, or by overseas fums patenting more actively in Australia if opportunities for profits from innovations rise with the decline in domestic innovative activity. These explanations are consistent with the recent strong growth in foreign sourced innovations in Australia coinciding with slower growth in patenting by Australian residents. Similarly, we might be concerned that declines in patenting activity overseas would lead to a reduction in total patenting activity in Australia. The extent to which this is the case will depend on the substitutability between overseas and domestic innovations. Substitutability should show up in the patents data as a negative relationship between domestic and foreign residents patenting activity in Australia

In order to examine any substitution effects the long-run coefficients were estimated using regressions such as (2) where, first, the regressand was

Note that for the post-1950 estimates of & there are only 16 degrees of freedom remaining.

patenting activity by Australian residents, and the regressor is patenting activity by overseas residents in Australia Data are available for these variables from 1950 to 1997. The long-run coefficient estimates were examined rather than higher frequency correlations because it is thought to be the case that it may take a number of years for foreign innovation to substitute for domestic innovations and vice versa.* The results suggest that increases in foreign patenting activity lead in the long run to falls in the level of patenting by Australian residents. However, the long-run coefficient estimate is -.36, implying that only 36 per cent of any rise in foreign patenting rates would be offset by falls in domestic innovations. This suggests that recent strong growth in patenting by non-residents in Australia will lead in the future to lower levels of patenting by domestic residents than would otherwise have occurred. However, resident patenting activity will not be fully ‘crowded out’, and total patenting in Australia will be higher because of the growth in non-resident patenting.

To test the hypothesis that reductions in domestic patenting levels lead to increases in patenting activity by foreigners in Australia we can simply reverse the regression above. If we do

8 There is a potentid pitfall with this and sis, as the long-run coefficient estimates rely on cov(E j‘, E f> # 0. If it is the case that changes in foreign patenting activity in part reflect Werentid rates of patenting activity of subsidiaries of multinationals over time, this covariance may be negative rather than zero. In addition it is difficult to detect long-run relationships with only 48 years of post-war data available (as noted above, the estimates of B0 leave few remajning degrees of M o m ) . For these reasons these results should be created with care.


so we find a long-run coefficient estimate of .7, and a confidence interval which includes 1. If we assume that the point estimate of 0.7 reflects the true relationship between domestic and foreign patenting, and assume that these two sources of innovation are equally important to growth, it will be the case that any policy which reduces dornes- tic rates of innovation will reduce economic growth, since overall patenting activity in Australia will fall. Reductions in subsidies to R&D are likely to have the effect of reducing Australian residents’ innovative activity, thereby reducing growth. Putting the two sets of results together implies that Australia is more inclined to borrow foreign innovations when domestic innovation rates decline, than we are to replace foreign innovations with domestic innovations when foreign innovative rates fall.

V Concluripzs In this paper the importance of innovation in

promoting Australian economic growth has been explored. It has been found that increases in patenting activity l e g to increases in both labour productivity and economic growth. These results suggest that part of the decline in productivity in the 1970s and on might be attributable to declines in innovation from the late 1960s. Further, it has been shown that falls in innovation rates in Australia are not met fully by rises in foreign sourced patenting activity in Australia Since this is the case, cuts to R&D subsidies will likely lead to declines in both the level of domestic sourced innovations, and in the total quantity of innovations in Australia and might be expected to reduce economic growth in Australia. It is also suggested that the recent strong gowth in patenting activity in Australia will lead to higher growth and productivity, though this increase may take up to 15 years. This paper also suggests that these results should be treated as tentative, and that the relationships explored in this paper deserve further attention using different measures of innovation.

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