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?p麳?Ky?FK?酝0壳????∟. 諽?襛d6hV斖K?K?K?K?K?K?K?K?K?K?K?K?K?K?K?K?K?K?  MACROBUTTON MTEditEquationSection2  SEQ MTEqn \r \h \* MERGEFORMAT  SEQ MTSec \r 1 \h \* MERGEFORMAT  SEQ MTChap \r 1 \h \* MERGEFORMAT  Productivity Growth and the Returns from Public Investment in R&D in Australian Agriculture John Mullen President of the Australian Agricultural and Resource Economics Society, Principal Research Scientist, NSW DPI Adjunct Professor, CSU and University of Sydney Presidential Address to the 51st Annual Conference of AARES, February 13  16, 2007, Queenstown, NZ Abstract Investment in R&D has long been regarded as an important source of productivity growth in Australian agriculture. Perhaps because research lags are long, current investment in R&D is monitored closely. Investment in R&D has been flat and yet productivity growth has remained strong, relative to both other sectors of the Australian economy and to the agricultural sectors of other countries. Such productivity growth at a time when the terms of trade facing Australian farmers has been flat, may have enhanced the competitiveness of Australian agriculture. Econometric evidence about the sources of productivity growth and their magnitude is rarely clear. However recent analysis suggests little change in research efficiency and in the returns from research from earlier analyses in the early 90s by Mullen and coauthors. Some scenarios about the importance of domestic and foreign R&D and other sources of productivity growth are developed indicating that returns to investments in domestic research are likely to have been in the order of 15  30 percent. Keywords: Productivity, research and development, research evaluation With the usual caveat, I am grateful for advice from Chang Tao Wang, who conducted some of the econometric work reported here as part of his Honours thesis at the University of Adelaide, Eran Binenbaum, Jason Crean, Nick Austin, Grant Scobie, Bob Chambers, Julian Alston, Phil Kokic, Phil Knopke, and colleagues in various State Departments. The project has been partly funded by the Australian Farm Institute. Disclaimer: The views expressed in this paper are solely the views of the author and do not represent in any way policies of the NSW Department of Primary Industries (DPI) The Australian data on R&D expenditure used in this report have largely been extracted from ABS reports. DPI takes no responsibility for any errors or omissions in, or for the correctness of, the information contained in this. The paper is presented not as policy, but with a view to inform and stimulate wider debate. Productivity Growth and the Returns from Public Investment in R&D in Australian Agriculture Introduction Public sector investment in agricultural research in Australia and New Zealand has been much larger than that by the private sector, contrasting strongly with the experience in larger OECD countries (Pardey, Alston and Bientema, 2006). Likely as a consequence, there has been continuing interest in research policy related to the funding and management of research and in the contribution from research investments to productivity growth. There have been three enquiries by the Productivity Commission since 1976 and most public providers of research services to agriculture have been engaged in a process of  evolution that seems to have been accelerating in recent decades. Notable institutional changes include the Research and Development Corportation (RDC) and Cooperative Research Centre (CRC) systems in Australia and the Crown Research Institutes in NZ. These new institutions address different aspects of problems for research management arising from elements of non-rivalry and non-excludability that often characterise the information generated by research. There are long lags between the generation of new information through research investments and efficiency gains in agriculture and hence it is difficult to monitor the performance of the public agricultural research sector. Benefit cost analysis has been applied at a project level both ex post, as a measure of accountability, and ex ante, to assist in resource allocation. At a sector level, trends in productivity growth and in research investment are often monitored as proxies for knowledge about their causal relationship which has proved difficult to empirically estimate with precision. Much of the original econometric research in Australia was conducted in the early 90s by Mullen and various co-authors. They estimated that the returns from public investment in agricultural research between 1953 and 1994 was in the order of 15  40 percent. Productivity had been growing at about 2.5 percent per annum and public investment in R&D had stabilised after a period of strong growth in the 50s  70s. Highlighting the difficulties of this empirical work, Mullen and Strappazzon (1996) found that the models they were working with had poor time series properties, raising doubts about the existence of a stable long-term cointegrating relationship between research and productivity growth. It is now opportune to revisit this work. Changes in the agricultural research sector both domestically and internationally have seen renewed interest in productivity growth and in its relationship with R&D. In an international context there are concerns that both productivity growth and investment in agricultural R&D are falling, particularly in developed economies, with implications for food security in developing countries reliant on technology  spillovers whose populations will continue to increase for several decades. In Australia there is concern by governments to more closely align the large public investment in agricultural research with community goals and concern by the RDCs to earn adequate returns to farmers from the funds they invest. Except for organisations like ACIAR, technology spillover to developing countries is not a high priority. In addition, it was hoped that a longer dataset may allow a more thorough and rewarding econometric analysis. The primary objective here is to review empirically recent trends in productivity growth and in investment in research, and the rates of return to publicly funded agricultural research in Australia (and less thoroughly, in NZ). In particular I have been interested to assess any evidence that there has been a slowdown in productivity growth given the stagnation in public investment in research in recent decades, and by implication, an increase in the rate of returns from research. In the next section of the paper, the trend in productivity growth in broadacre agriculture in Australia relative to other sectors of the economy and to agricultural productivity in other countries is reviewed. Then follows a review of trends in the funding of agricultural R&D in Australia. Finally recent econometric analyses and productivity decomposition approaches are reviewed for evidence about the rates of returns to public investment in agricultural R&D Trends in Productivity in Australian Broadacre Agriculture Past studies of productivity growth in Australian agriculture based on ABARE farm survey data are reviewed in Mullen (2002) and Mullen and Crean (2006). Estimates from studies conducted in the 70s suggested that productivity growth in broadacre Australian agriculture ranged from 0.6 to 1.7% p.a. The first study using a  modern measure of total factor productivity (TFP) was that by Lawrence and McKay (1980). They used a Tornqvist-Thiel TFP index. Studies using these techniques are summarised in Table 1 where the observation period is in calendar year form but refers to the last half of the preceding financial year. For much of this period, productivity in broadacre agriculture has grown at a rate greater than 2.5% p.a. These studies are based on time series data which generally means that changes in technical efficiency and scale economies cannot be isolated from the contribution of technical change to productivity growth. Nor do these standard measures reflect current theory about how producers make decisions under risk, particularly climate risk (O Donnell et al. 2006). Table 1: Estimates of productivity growth in Australian broadacre agriculture. AuthorsPeriodAnnual Input Growth (%)Annual Output Growth (%)Annual Productivity Growth (%)Lawrence & McKay (1980)1953 19771.54.42.9Lawrence (1980)1960 19773.1Paul (1984)1968 19821.52.71.1Beck et al. (1985)1953 19831.34.02.7Males et al. (1990)1978 1989All agriculture2.0All Broadacre1.43.62.2Crops-1.83.75.5Sheep1.31.50.2Zeitsch & Lawrence (1993)1983 1994Sheep( 1.0Mullen & Cox (1995)1953 19940.12.62.5Knopke et al. (1995)1978 1994All broadacre0.22.92.7Crops0.45.04.6Sheep0.51.51.0Beef0.31.91.6Sheep-Beef-2.102.1Knopke et al. (2000)1978 1999Grains industry0.73.32.6Crops1.34.83.6Sheep0.61.20.6Beef0.32.42.1Sheep-Beef-0.90.41.4ABARE (2004)1989-2002All broadacreCrops1.8Beef2.1Beef - crops2.4ABARE (for Vic. DPI) (2006)1989 2004All broadacre-0.91.32.2Crops7.710.12.4Sheep-5.4-5.00.4Beef-0.32.22.5Kokic, Davidson and Rodriguez (2006)1989 - 2004Grains industry1.9Crops1.8 Has the rate of agricultural productivity growth changed? There is keen interest in how the rate of productivity growth has changed through time and, particularly now, in whether the rate of growth is falling, in line with expectations about growth in other countries with major agricultural sectors. Periods of atypical seasonal conditions and long investment cycles necessitate cautious interpretation of trends in productivity growth. Stoeckel and Miller (1982) argued that productivity growth in Australian agriculture increased after 1968 69  a  watershed year for agriculture. After this period, output continued to grow but inputs actually declined. Their study only extended as far as 1980 and inputs have grown since, but at a rate that has rarely exceeded 1% p.a. Mullen (2002) in his review of productivity growth in broadacre agriculture adopted the Stoeckel and Miller (1982) view, that 1969 was a  watershed year. Econometric analysis suggests that the growth rate from 1953 to 1968 was 2.0 percent and from 1969 to 2004, 2.5 percent. These estimates have been used in several papers by Mullen and coauthors to estimate the benefits from productivity gains in broadacre agriculture and are applied again below. However there is some evidence that productivity growth in the grains industry, if not in broadacre agriculture as a whole, may be drifting down while that for livestock specialists has been increasing. Males et al. (1990) reported productivity growth of 5.5% p.a. for specialist crop farmers for the period 1978 to 1989. Since then the two studies by Knopke et al. (1995; 2000) suggested that productivity growth for crop specialists slowed to 4.6% p.a. for the period from 1978 94 and to 3.6% p.a. for the period from 1978 99, while productivity growth in broadacre agriculture remained unchanged at 2.6% p.a. Knopke et al. (2000) speculated that this decline may be associated with increased expenditure on capital inputs as wheat prices recovered during this latter period. A study undertaken by ABARE for the Victorian DPI (ABARE) found that productivity growth in broadacre agriculture had declined to 2.2% p.a.. However, the analysis was conducted over a relatively short period from 1989 to 2004 and drought was a major influence in latter years of this period. Most recently Kokic et al. (2006) found that for the 1989-2004 period, productivity growth in the grains industry averaged 1.9 % and for specialist croppers, averaged 1.8%. The rate of growth for the grains industry increased to 2.6 % when adjusted for the poor seasonal conditions over this period. Productivity growth in the sheep industry, at least as estimated using ABARE survey data, has always been disappointing, at 1% p.a. or less in recent decades. Martin et al. (2004) estimated that productivity growth for specialist wool producers had been 0.9% p.a. over the period from 1977 78 to 2001 02 and had been 1.2% p.a. since 1988 89. Villano et al. (2006) found that specialist wool growers achieved rates of growth as high as 5.2% but warned that this finding might be related to the characteristics of the population from which the sample was drawn (i.e. all participants were involved in the Farm Monitor Project). The productivity of beef specialists has been better than that of sheep specialists but less than that of those predominantly involved in crop production. The estimates from Table 1 suggest that productivity in the beef sector has been increasing. Productivity grew at the rate of 1.8% p.a. from 1978 to 2002 but the growth rate was 2.1% p.a. for the 1989 to 2002 period and 2.5 % to 2004 for specialist beef producers. Mixed beef and crop producers enjoyed a growth rate of 2.4% p.a. to 2002. Northern beef producers experienced higher productivity growth than their southern counterparts. Productivity grew at the rate of 3.3% p.a. from 1989 to 2002. As for other industries ABARE (2004) found evidence that larger beef producers enjoyed higher productivity growth than smaller producers. It is somewhat puzzling that productivity on crop farms has consistently exceeded that on livestock farms and the issue is not explored in any depth in the literature. Some studies suggest that this might arise from a more rapid development of cropping technologies in recent decades (minimum tillage, crop varieties, improvements to fertilisers and pesticides etc) compared to livestock technologies, the longer breeding cycles for livestock, and perhaps the labour intensive nature of some livestock handling operations. As noted later, expenditure on plant research in Australia has grown more rapidly than expenditure on livestock research in recent years. Whether this is a cause of, or a response to, higher rates of productivity growth in the crop sector is unclear. What is also puzzling is the apparent ambiguity as to whether or not there are gains in productivity available from economies of scope. It would seem that there are gains for specialist livestock producers to diversify towards more cropping but the rate of productivity growth of specialist croppers is higher than that of mixed farms. Hailu and Islam (2004) using a multilateral measure and ABARE farm survey data from 1977 to 1999 found that while the level of productivity growth in agriculture was still higher in NSW (2.07% p.a.) and Victoria (1.45% p.a.) than the other states, the gap has narrowed considerably because WA (3.73% p.a.) and SA (3.19% p.a.) in particular, have enjoyed higher rates of productivity growth in recent years, perhaps because of their greater reliance on cropping. Recent Analysis by Chang Tao Wang Mullen and coauthors in their analysis of TFP used a dataset extending from 1953 to 1988 and then to 1994 derived from ABARE s broadacre agriculture survey (see Mullen and Cox (1996) for more detail). For analysis of the relationship with research investment, a series as long as possible helps. Wang (2006)  spliced an ABARE dataset for the years 1978 to 2003 to the original Mullen and Cox dataset. Small differences between the two datasets were observed for the overlapping years 1978-1994. These can largely be explained because the survey populations changed from being a population in which all farmers had sheep (1953-1994) to one in which crop and beef specialists (having no sheep) were allowed. Wang used the original dataset to 1979 and the more recent dataset from then. EDIT FOR YEARS Wang s estimate of the Fisher TFP index in Australian broadacre agriculture is shown in Figure 1 and Table 2. The Fisher TFP index rose almost 3.5 times from 100 in year 1953 to 343 in 2002 before declining to 261 in 2003, reflecting the drought in that year. The index is highly variable, falling in 18 of the 50 years, reflecting seasonal conditions. The average annual rate of growth over the entire period was 2.5 percent. Figure 1: Productivity in Australian Broadacre Agriculture as measured by a Fisher ideal index and the Terms of Trade facing Farmers  Source: Derived from ABARE data Table 2: Average Growth in Productivity (Fisher Index) for Broadacre Agriculture period 1953-2003 1953-1968 1969-1984 1985-1994 1995-2002 Productivity growth (%) 2.5 1.9 1.8 2.5 2.7Source: Wang (2006) Relative to other sectors of the Australian economy The Productivity Commission has been estimating productivity growth in major sectors of the Australian economy such as agriculture (Table 3 adapted from Parham (2004)). These estimates are based on ABS sectoral data from the National Accounts using a value added approach to estimating productivity. The last row in Table 3 displays the ratio of productivity growth in agriculture to the the average for all sectors (market economy). Table 3: Productivity growth in sectors of the Australian economy: 1974 99. 1975 821982 851985 891989 941994-99Agriculture1.61.11.42.64.3Mining-1.70.52.62.51.2Manufacturing2.11.81.71.61.3Electricity, gas & water2.03.24.23.71.8Construction1.40.4-0.3-0.20.4Wholesale trade-0.7-0.9-0.51.23.2Retail trade1.00.6-0.20.11.0Accom., cafes & restaurants-0.9-1.3-1.9-1.6-0.3Transport & storage2.21.21.01.41.9Communication services6.54.94.84.93.7Finance & insurance-2.0 -1.00.20.70.8Comm. Rec. Services-1.4-2.2-2.9-3.1-3.3Market Economy1.10.80.40.71.8Agriculture/Market economy TFP1.41.43.53.72.4Source: adapted from Parham 2004 Parham (2004) estimated that productivity growth in the Australian economy during the 1990s (1993 99) was 1.8% p.a., a percentage point higher than previously estimated. This placed Australia in a favourable position relative to other OECD countries. He estimated that the growth in agricultural productivity during the 1990s has been 4.3% p.a., higher than all other sectors and higher than for previous decades. The growth rate in the wholesale trade sector was 3.2% p.a., much improved on previous periods, and 3.7% p.a. in the communications services sector  down on previous periods. Up to 1994, productivity growth in the electricity, gas and water sector and in the communications sector generally exceeded that in agriculture and so did the growth rate in manufacturing although to a lesser extent. Parham found that productivity growth in agriculture in the 1970s and 1980s was subdued (Table 3). Undoubtedly, the severe drought in the early 1980s was a factor in this result. The surge in productivity growth in the 1990s coincided with a surge in productivity growth for the Australian economy generally. Factors thought to contribute to this surge include the greater openness of the economy to trade and investment, the continuing deregulation of markets and institutions, and efficiency gains from the computer, telecommunications and transport sectors. Research is regarded as having made a significant ongoing contribution to productivity growth both in agriculture and the economy generally. Parham (2004) tentatively speculated that the relative contributions of greater openness, R&D, and the adoption of ICT to an apparent increase of about 1% p.a. in the rate of productivity growth in the 1990s may have been in the order of 0.5, 0.3 and 0.2% p.a. It is interesting to note from Table 3 that productivity growth rates in other sectors of the economy seem as variable as that of agriculture and are often negative. While the question of fluctuations in productivity growth was rarely addressed in the ABARE studies, there is less evidence of the variation in growth rates than is apparent in the ABS based sectoral analyses. The ABARE based estimates generally fluctuate within the 2 3 % p.a. range while the ABS estimates fluctuate in a broader band of 1 4% p.a.. Relative to other agricultural sectors International comparisons of productivity are difficult to make because of differences in methods, data, and observation periods and the contribution of productivity differences to international competitiveness is difficult to separate from other factors. International comparisons are reviewed in Mullen and Crean (2007). Australia s recent performance compares favourably with other countries. For example, Rao et al. (2004) for the 1980 2000 period found that Australian agriculture achieved a TFP growth rate of 2.6% p.a.  higher than their estimate of 2.0% p.a. for the period 1970 2001 and higher than estimates from earlier studies. This rate of growth is similar to that achieved by the US but a little slower than that in Canada and Denmark and well above average for the group of OECD countries (Figure 2). This reported acceleration in productivity growth is consistent with recent studies of productivity growth in Australian agriculture in the 90s as described above. Figure 2: Productivity growth rates  selected OECD countries.  SHAPE \* MERGEFORMAT  Source: Rao et al. 2004; Coelli & Rao 2003 The lack of international farm survey data comparable to that collected by ABARE appears to be the main reason for the lack of comparative international studies of particular agricultural industries. However, there appears to be some evidence to suggest that Australia s rate of crop productivity growth of 3.6% p.a. compares favourably with other countries which ranged from 1.4 2.8% p.a. Livestock productivity appears to be low relative to other livestock sectors although productivity in the Australian beef industry has risen. Implications for Competitiveness Most commonly productivity growth has been compared with the terms of trade as a partial indicator of whether Australian agriculture is becoming more competitive. The conventional wisdom has been that the terms of trade for Australian agriculture have been declining inexorably. However, while the trend in the terms of trade did decline for about 40 years from 1953 (Figure 1), since the early 1990s, the rate of decline has been much slower, at least for the sector as a whole. Using econometric techniques, it was found that the terms of trade declined at the rate of 2.3% p.a. over the whole period 1953 2004 but, that the rate of decline was 2.7% p.a. from 1953 1990, similar to the rates of productivity growth in broadacre agriculture. Perhaps in the 1950s the terms of trade were declining at a faster rate than productivity gains. From 1991to 2006, the rate of decline in the terms of trade was only 0.9%. However, productivity continued to grow. Over the entire period from 1953 it can be seen from Figure 1 that TFP index grew from 100 to almost 350 while the terms of trade declined from about 320 to 100. From Figure 6 it can be seen that only in recent years has the real value of agricultural output in Australia consistently exceeded $30 billion and perhaps these favourable trends in TFP and the terms of trade have contributed. There is a wide but inconclusive literature about how productivity measures might be adjusted for terms of trade effects (Diewert & Morrison 1986). The Productivity Commission (Zheng 2005) has decided not to adjust TFP measures for terms of trade effects. Given that there has been little real trend in the terms of trade facing farmers since the early 90s, the bias from price changes in estimates of TFP over this time may be small. An important indicator of the agricultural sector s competitiveness is the rate of its productivity growth relative to that achieved by other sectors of the economy. Shane et al. (1998) argued:  the assessment of changes in comparative advantage in two countries entails a comparison of the ratio of growth in agriculture to growth in the rest of the economy . Agricultural productivity growth in Australia has been up to four times higher than the average productivity growth for the economy as a whole (Table 3). The Australian findings in respect to relative sector performance do not seem unique. Bernard and Jones (1996) in an analysis of 14 OECD countries over the period 1970 87 found that average productivity growth in agriculture grew at the rate of 2.6% p.a. as compared to 1.2% p.a. for industry (Table 4). In respect to the Australian findings, many countries are reported to have higher agricultural sector TFP growth for the 1970 87 period. However, Australia performs well on the basis of its ratio of agricultural TFP growth to non-agricultural TFP compared to other countries. Australia s ratio of 3.6 was significantly higher than the 2.17 average reported for the group and was only behind two other countries, the US and the United Kingdom. This implies that Australia s ability to compete on world markets may have improved over the period. Table 4: Productivity growth rates  agriculture versus other industries (selected OECD countries): 1970 87. CountryAgriculture Average TFP growthTotal Industry Average TFP growthRatio of Agriculture TFP to Non-Agriculture TFP RatioUnited States1.500.305.00Canada0.900.402.25Japan-0.201.50-0.13Germany4.301.303.31France4.001.702.35Italy2.001.002.00United Kingdom3.600.904.00Australia1.800.503.60Netherlands4.401.303.38Belgium3.701.602.31Denmark4.101.402.93Norway2.101.501.40Sweden2.001.201.67Finland2.201.701.29Average2.601.202.17 Trends in Expenditure on R&D for Australia There are two important sources of data on public investment in agricultural R&D in Australia. The first of these is the dataset assembled by Mullen et al.  ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Mullen</Author><Year>1996</Year><RecNum>340</RecNum><record><rec-number>340</rec-number><ref-type name="Report">27</ref-type><contributors><authors><author>Mullen, J.D.</author><author>Lee, K. </author><author>Wrigley, S. </author></authors></contributors><titles><title>Agricultural production research expenditure in Australia: 1953-1994</title><secondary-title>NSW Agriculture, Agricultural Economics Bulletin 14</secondary-title></titles><dates><year>1996</year></dates><pub-location>Orange</pub-location><urls></urls></record></Cite></EndNote>(1996a) on public investment in research and extension in Australia from 1953 94. The second source is that collected in a biannual survey by the ABS which extends back in some form to 1968 69 where total public expenditure on agricultural R&D was estimated by the sum of expenditure on R&D in the plant and animal socioeconomic objective classes (from which expenditure on fisheries and forestry was deducted for the purposes here). The most recently reported ABS survey year was 2002 03. Mullen et al.  ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Mullen</Author><Year>1996</Year><RecNum>340</RecNum><record><rec-number>340</rec-number><ref-type name="Report">27</ref-type><contributors><authors><author>Mullen, J.D.</author><author>Lee, K. </author><author>Wrigley, S. </author></authors></contributors><titles><title>Agricultural production research expenditure in Australia: 1953-1994</title><secondary-title>NSW Agriculture, Agricultural Economics Bulletin 14</secondary-title></titles><dates><year>1996</year></dates><pub-location>Orange</pub-location><urls></urls></record></Cite></EndNote>(1996a) advised that in view of the availability of ABS data, it was no longer sensible to update their series. Instead the two series have been  spliced to give a perspective on investment in R&D from 1953 to the present. In 1991, the two series differed by only 2.5% and from this year ABS data were used. Generally the two series differed by about 10% giving some degree of confidence in this splicing procedure. However, in 1987 and in 1989, the ABS estimates of expenditure exceeded the Mullen et al. (1996a) estimates by 20% or more. The rise and then fall in R&D estimates over this period reported by ABS were not evident in the published financial reports of large R&D institutions used by Mullen et al. (1996) and hence ABS data were not used until 1991. Since the ABS census is only conducted every second year an estimate of R&D expenditure was linearly interpolated for the intervening years. In this paper all expenditure data have been expressed in 2004 dollars using a GDP deflator. Total public expenditure on agricultural R&D has grown from A$115 million in 1953 to almost A$730 million in 2003 (in 2004 dollars). Figure 3 shows that growth was strong to the mid 1970s. There has been little growth since, although in recent years there is some evidence of renewed growth. As a percentage of total expenditure on R&D, expenditure on agricultural R&D in 2003 was 8%. It has declined steadily from 20% in 1982. Expenditure on environmental research has never exceeded 10% of total expenditure and was 6.5% in 2003. In Australia, the public sector has always been the dominant provider of research services to the agricultural sector (Figure 4). The business sector has generally been responsible for less than 10% of total agricultural R&D although its share in 2003 was 14%. This contrasts sharply with other developed countries where agricultural R&D is roughly shared between public and private sectors (Pardey & Beintema 2001). From ABS data, state organisations, presumably dominated by the state departments of agriculture or their equivalents, have been responsible for about half of all agricultural R&D in Australia, with the Commonwealth responsible for a quarter and universities, about 15%. From the 2003 ABS survey, there was evidence that more research is being undertaken by universities and the business sector and less by state and Federal organisations. The share undertaken by states was down to 44%. The focus of this paper is on publicly funded agricultural research.  Figure 3: Real public expenditure on agricultural R&D in Australia (in 2004 dollars): 1953:2003.  Figure 4: Expenditure shares of agricultural R&D in Australia by providers of research services (2002 03). Research intensity is a measure of investment in research relative to the size of the agricultural sector. In Figure 5 two measures of public research intensity are presented, one relative to agricultural GVP and one relative to agricultural GDP. The latter measure is the one most frequently used in international comparisons. In research intensity terms, public funding for agricultural research has been drifting down from about 5% p.a. of GDP in the period from 1978 86 to just over 3% p.a. in 2003. Intensity grew strongly in the 1950s and 1960s.  Figure 5: Public agricultural research intensity in Australia (R&D expenditure as proportion of agricultural GDP or GVP). For most of the 1990s, expenditure on plant and animal research was similar but by 2003 expenditure on plant research was half as much again as that on animal research. Perhaps this partly reflects the growing importance of the Grains Research and Development Corporation (GRDC) as a source of funds. During the 1980s, the share of the GRDC in total RDC funding was under 20% but by 2001 it had risen to 30% before declining to 27% in 2003 04. The leading states for the location of public agricultural R&D in 2003 were Victoria and Queensland (similar amounts), followed by NSW. The most recent international review of agricultural R&D is that by Pardey and Beintema (2001) based on 1995 data although further insights will soon be available in a study edited by Pardey et al. (forthcoming) . At that time, investment in agricultural research worldwide was still growing but in developed countries, the rate of growth had slowed from 2.2% p.a. in the 1980s to 0.2% p.a. From Pardey et al. (forthcoming), public investment in agricultural research in real dollars (2000 international dollars) had only risen from A$15.2 billion in 1981 to about A$23 billion in 2000. In  dollar terms, expenditure on agricultural research in 1995 in developing countries exceeded that in developed countries with China, India and Brazil emerging as major investors. Public research intensity in developed countries was 2.6% p.a. and total agricultural research intensity was about 5.5% p.a. Research intensity in less developed countries was often very low such that average public research intensity in the countries Pardey and Beintema (2001) reviewed was just over 1% p.a. By 2000, about a third of all agricultural R&D was undertaken in the private sector, but little of this was in developing countries. The world s poorest countries are still dependent on technology spillovers from rich countries both individually, through the CGIAR system and through organisations such as Australia s ACIAR. A feature of the agricultural research sector in Australia has been the prominent role played by the RDCs. In 2003, total expenditure by the RDCs was A$461 million (nominal) which is approaching half the total public expenditure on agricultural R&D, although it probably overstates RDC funding for agricultural production research because some of these funds were used to fund research of a non-production nature such as research in processing or environmental areas. Recall also that less than half of RDC funds are raised from producers (because of the predominant Federal funding of Land and Water Australia for example). In the 1980s, RDC funding only amounted to about 15% of total public expenditure on agricultural R&D. The Contribution of R&D to Growth in Agricultural Productivity Empirical research to estimate the returns from investment in R&D has generally fallen into two classes  econometric analyses at a sector level and benefit cost analysis of particular research investments. Reported here are some recent econometric analyses updating that originally conducted by Mullen and Cox (1995) and some benefit cost analyses based on scenarios about the potential contribution of domestic research to productivity growth in Australian agriculture. The findings of Mullen and co-authors can be summarised as follows: In an alternative to the econometric approach, Scobie et al. (1991) synthesised a production function linking expenditure on research with productivity growth in the Australian wool industry. Gauging public investment in wool production R&D to have been about A$40 million in 1985, they estimated that the average internal rate of return to Australia might have been in the order of 9.5% and the internal rate of return to woolgrowers might be in the order of 25%. Mullen and Cox (1995) estimated that the returns to research in broadacre agriculture in Australia may be in the order of 15 40% over the 1953-1988 period. The low rate was associated with a 35-year research profile and the high rate with a 16-year research profile. Mullen and Strappazzon (1996) using a dataset extended to 1994 estimated that the rate of return to pubic investment in broadacre research was between 18 and 39% for models in which research was defined over 35- and 16-year periods. However they also found that for the Australian dataset, there was no strong evidence of a stable long run equilibrium relationship between expenditure on research and productivity. The lack of a constant long run relationship does not imply that research has no effect on productivity. Rather it suggests that the impact of research may vary through time in response say, to changes in research management or to research opportunities. Mullen et al. (1996) attempted to incorporate research in a translog cost model of broadacre agriculture in Australia. While their preferred model did not satisfy all the conditions of a well behaved cost model, they found that research did have a significant impact in reducing costs in broadacre agriculture and that the rate of return to research was as high as 86%. Cox et al. (1997) using nonparametric methods estimated the marginal internal rates of returns to research and extension expenditures in the order of 12 20% over the 1953 94 period. They found nonparametric evidence of lagged research and extension impacts on productivity in Australian broadacre agriculture out as far as 30 years. Alston et al. (2000) conducted a meta-analysis of 292 studies of the rates of return to agricultural research, about one third of which were published in scientific journals. Their main findings included: there was no evidence that returns from research investments were declining the returns from research appear higher in developed countries estimated rates of return from research were lower for enterprises with longer production cycles. According to Alston et al. (2000) the median rate of return reported in the Mullen papers was 25%. Mullen was one of four analysts whose median rate of return was 25% or less. More recently the Productivity Commission (Shanks & Zheng 2006) undertook a comprehensive analysis of the relationship between investment in research, particularly by the business sector, and productivity growth in the Australian economy. They estimated that the return to business investment at the level of the entire market economy was 50%. Their preferred estimate of the rate of return to public investment in agriculture was 24%, relatively precisely estimated in a range of from 1 46%. The estimated rate of return in the manufacturing sector was 50% and the returns to the mining and wholesale and retail sectors were 159% and 438% with very wide confidence intervals. Recent Analysis Following Chang Tao Wang Wang (2006) and Wang et al. (2007), using Mullen and Cox s dataset extended to 2003, examined whether the relationship between research investment and productivity growth in Australian broadacre agriculture has changed over the 15 years since the original Mullen and Cox analysis. The focus of econometric analysis reported here is not so much on change from Mullen and Cox and from 1988, as on modelling the relationship between productivity and research over the entire period for which data are available, 1953-2003. Either explicitly or implicitly, empirical analyses of the link between research investment and productivity growth are based on a structural model where the product of investment in research is a lagged increase in the stock of technology or knowledge-in-use by producers. This yields a flow of benefits to producers and consumers over many years. Other explanatory variables include the level of education of farmers, the terms of trade, seasonal conditions and investment in extension, the set of variables used by Mullen and Cox. Additionally TFP in agriculture is likely to be influenced by  spillovers of technology from other countries and by improvements in public infrastructure in the form of communications and transport within Australia. There are real difficulties in assembling suitable proxies for these variables and little attempt has been made yet to do this for the latter two variables. A further difficulty in econometric research in this area is the high degree of collinearity between explanatory variables making precise estimation of all coefficients unlikely. Common practice is to introduce a time trend to pick up the influence of omitted variables but Mullen and Cox found that even for their reduced set of variables, the inclusion of a time trend meant that several coefficients including the research coefficient could not be precisely estimated. Following Mullen and Cox closely, attention was confined to the 16 and 35 year lag models including a weather index, farmers terms of trade and farmers education as explanatory variables and estimated in double log form. The knowledge stock variables were assembled as weighted sums of past investments in research and extension over 16 and 35 year lag lengths using a procedure more fully described in the original Mullen and Cox paper. Some of the estimated models can be found in Table 5, as well as Mullen and Cox s original models. Initially, both models were estimated over the 1953-2003 period. Neither model estimated using OLS performed well. For the 16 year model, the research coefficient was not significant and both the Durbin-Watson and RESET statistics suggested evidence of misspecification. For the 35 year model all coefficients were significant but the specification diagnostics were similar to the 16 year model. Plots of the CUSUM statistic show a marked departure by the statistic from zero from the early 80s for both models and it crossed the upper bound in the early 90s (a bit later for the 35 year model), further evidence of misspecification or structural change over this long period. Various techniques were used to address these specification problems including the addition of a time trend, correction for serial correlation and the use of dummy variables both as an intercept and interactively with research. The RESET test provides some guidance as to whether quadratic or interaction terms are missing from the model. Adding a quadratic knowledge stock term led to a marked improvement in the properties of both models as can be seen in Table 5. Models including a variable allowing interaction between the knowledge stock and farmers education also performed well. There was some evidence from non-nested testing that the introduction of a quadratic term added to the explanatory powers of the models more than the interaction term and on this basis attention focussed on the 16 and 35 year models with quadratic knowledge stock terms. Adding a trend term to either of these models to isolate the contribution of omitted variables noted above proved unsatisfactory. The econometric properties of both the 16 and 35 year quadratic models are strong. All coefficients are precisely estimated and have the expected sign (expectations about the signs on the knowledge stock variables are discussed further below). For the 35 year model, the D-W and RESET statistics and the plot of the CUSUM values all suggest few problems with the specification of this model. These same specification statistics for the 16 year model have values suggesting that specification might remain a problem. Non-nested testing of these two models provided clear evidence in favour of the 35 year model and supported concerns about the specification of the 16 year model. The equations below represent the total and marginal impact of the knowledge stock, KS, on TFP where the other explanatory variables evaluated at their means are subsumed in the constant term. The implication of a quadratic knowledge stock is that the impact on TFP of a change in the knowledge through research investment say, is not a constant as in a linear model but depends on the level of research investment. Our expectation is that as investment in research continues to increase, holding other explanatory variables constant, eventually the changes in TFP will become smaller. For this to happen, typically the coefficient on the linear term is positive and that on the quadratic term is negative. As can be seen from Table 5, the signs are reversed here, suggesting that over the range of research investment experienced in the 1953- 2003 period, the marginal impact of increments to research investment is still increasing.  At its average level for 1953-2003, the marginal impact of a change in the knowledge stock (in logs) was 0.18 and 0.22 for the 16 and 35 year models. Using the same procedure as Mullen and Cox (1995) these marginal impacts translate into internal rates of return (IRRs) of 23 and 15 percent for the 16 and 35 year models. These internal rates of return are for a once only unit ($1000) increase in the knowledge stock variable evaluated at the average level of TFP, research investment and output price and scaled up from farm level by the ratio of the value of broadacre agriculture in Australia to the value of output from the farm survey data. These results were obtained from econometric models which maintain strong assumptions about how investments in research and extension translate into changes in TFP and from which some variables expected to influence TFP have been omitted . Nor is there much information available about the opportunity cost of alternative uses of public funds. Hence some caution in interpreting the results is warranted. Nevertheless they indicate that investment in agricultural research, at least over the range in investment levels experienced from 1953 to 2003 has earned good rates of return. There is no evidence that the returns from public research are declining, a finding consistent with Alston et al. (2000). In fact these result that the marginal impact of research is increasing lend some support to a view recurring in the literature that there is underinvestment in agricultural research. In response to the possibility of structural change, the models were estimated from 1969, Stoeckel and Miller s  watershed year. From Table 5, it can be seen that the quadratic version of the 16 and 35 year models still had superior properties to the linear models. There was little change in the 35 year model. However the IRR from the 16 year model, 39 percent, is much larger than the IRR from the 35 year model, 16 percent. This difference reflects the change in marginal impacts. The marginal impact of a change in knowledge stock for the 35 year model increased from 0.22 to 0.33 for the period since 1969 whereas the marginal impact for the 16 year model increased from 0.18 to 0.56, reversing their relative magnitudes. . Non-nested testing was again unable to discriminate between the 16 and 35 year alternatives, both models displaying little evidence of misspecification. Because of its assumption that the impacts of research are experienced over 16 years rather than 35 years, the 16 year model is gives greater weight to the more recent changes in research investment. In particular the slowdown in research investment over recent decades is fully reflected in this model. Perhaps this explains the higher marginal impact and IRR associated with this model estimated since 1969. Perhaps the focus of research institutions such as the RDCs on applied research and practice change by farmers is shortening the lag profile is having some success. Perhaps there have been efficiency gains by the sharing of human and physical capital between research institutions. If however research lags do extend over 35 years then perhaps the consequences of this stagnation in research funding are yet to be fully reflected in productivity trends and IRRs. Scenarios about Sources of Productivity Growth and Returns from Research In this section, some scenarios are developed about sources of productivity growth in agriculture and estimates are made of the rates of return from domestic R&D that these scenarios imply using standard benefit-cost techniques. These estimated rates of return are not statistically based  results but rather the rates of return implied by a set of what are considered plausible assumptions which are subject to sensitivity analysis. If research does cause productivity growth then the relationship is likely to be between some long-term underlying rate of TFP and a long-term rate of research investment. The short-term variations observed in TFP are unlikely to be responses to short-term fluctuations in R&D given the long lags involved in changing the stock-of-knowledge in use. The long-term trend in productivity, which is possibly in the vicinity of 2.5% p.a. for broadacre agriculture in Australia, reflects the influence of slow moving factors like research-induced technical change, the education levels of farmers, and the state of public infrastructure in the form of transport and communications. Another slow moving variable is farm size. Acknowledging its speculative nature, some assessment can be made of how this underlying rate of productivity growth may be decomposed. Perhaps up to 0.5% p.a. can be attributed to factors such as public infrastructure and education levels of farms. Scobie et al. (1991) suggested that, in the absence of technical change, the underlying rate of productivity growth in the Australian wool industry might be 1% p.a. Since Scobie et al. s (1991) research, rates of productivity growth of less than 1% p.a. have been observed for specialist livestock producers, particularly wool producers, and technical regression over long periods seems unlikely, hence providing a rationale for halving the underlying rate to 0.5%. The literature suggests that a large component of TFP can be attributed to technical change. Technical change, accounting for 2% p.a., arises from public and private investments in research and extension where a significant component of both activities is related to the adaptation of foreign knowledge spillins. This scenario attributes none of the productivity growth to scale economies or gains in technical efficiency. MANSON AND BLACK??? The contribution of foreign knowledge to productivity growth means that rates of return to research, which only include Australian investment in research, will be overestimated to some extent, while acknowledging that Australian R&D resources are required to adapt foreign knowledge to Australian conditions. Alston (2002) has argued that, certainly between states, but even between nations, foreign research may be as important as domestic research. The extent of spillovers depends on similarities in climate and land resources and on the nature of agriculture. Australia s climate and its dependence on broadacre agriculture, particularly a unique wool industry, suggests that foreign spillovers while still important to Australia, consider chemicals and machinery for example, may be less important than domestic knowledge. It has been assumed that for Australian broadacre agriculture, domestic R&D activities may be responsible for productivity growth in the order of 1.2% p.a. and foreign spillins for 0.8% p.a.  a 60:40 split. Two scenarios for investment analysis relate Australian R&D investment first, to productivity growth at the rate of 2.0% p.a. and second, to productivity growth at the rate of 1.2% p.a. These scenarios  bracket the potential benefits from domestic research. Under assumptions used in this report, domestic research generates productivity gains of at least 1.2% but some productivity gains, 0.8%, are picked up from foreign sources without any domestic mediation. It is more likely the case that some domestic research is required to capture the benefits from foreign spillovers. At the extreme, domestic research is required to capture any of these foreign benefits, hence domestic R&D can lay claim to the whole 2.0% gain. Based on these assumptions, Figure 6 decomposes the value of all productivity gains in Australian agriculture since 1953 into those attributable to domestic R&D and those attributable to other sources of productivity including foreign knowledge and domestic sources such as public infrastructure and farmers education. It has been assumed that prior to 1969, productivity grew at 2.0% (80% of its current rate).  Figure 6: Sources of productivity growth in Australian agriculture (in 2004 dollars): 1953 2003. Holding technology at its 1953 state, less than 30% of the value of output in 2004 can be accounted for by conventional inputs (represented by the blue bars). Seventy percent of the value of farm output arises from the various sources of productivity growth such as improvements in infrastructure and communications, higher quality inputs, and new technologies from research and extension activities. Almost half the value of output in 2003 can be attributed to new technology generated by domestic research since 1953 (represented by the aqua bars). At a real rate of interest of 4%, the compound value of the stream of benefits from domestic research from 1953 2003 is A$878 billion (in 2004 dollars). As pointed out in Mullen  ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Mullen</Author><Year>2002</Year><RecNum>331</RecNum><record><rec-number>331</rec-number><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mullen, J.D.</author></authors></contributors><titles><title>Farm management in the 21st century</title><secondary-title>Australian Agribusiness Review</secondary-title></titles><volume>10</volume><dates><year>2002</year></dates><urls><related-urls><url>http://www.agribusiness.asn.au/Review/2002v10/Index.htm</url></related-urls></urls></record></Cite></EndNote>(2002) the benefits of new technology in Australian agriculture are shared with producers, processors and consumers who are non-residents of Australia. On the basis of previous research into the distribution of the benefits from research, he estimated that perhaps Australian producers, processors and consumers retain 80% of benefits or about A$700 billion in this case. The data series on public research investment presented above was backcast to 1918 to allow the estimation of models with research lags of 35 years. The compound value of public investment in research between 1953 and 2003 has been A$64.5 billion and the estimated total back to 1918 has been A$77.4 billion (in 2004 dollars). Mullen  ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Mullen</Author><Year>2002</Year><RecNum>331</RecNum><record><rec-number>331</rec-number><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mullen, J.D.</author></authors></contributors><titles><title>Farm management in the 21st century</title><secondary-title>Australian Agribusiness Review</secondary-title></titles><volume>10</volume><dates><year>2002</year></dates><urls><related-urls><url>http://www.agribusiness.asn.au/Review/2002v10/Index.htm</url></related-urls></urls></record></Cite></EndNote>(2002) estimated that private R&D in Australia and public extension expenditure might add a further 40% to domestic R&D investment, giving a total of A$90.3 billion since 1953 and A$108 billion since 1918 (in 2004 dollars). These benefit cost data allow an indicative assessment of returns from research in Australian agriculture under a number of scenarios (Table 6). Note that for these benefit cost scenarios, only benefits between 1953 and 2003 were recognised, a conservative approach particularly with respect to the flow of future benefits. Costs between 1918 and 2003 were recognised. Results are sensitive to this assumption. Table 6: Rates of return to research in Australian agriculture. Scenario:Benefit-Cost RatioIRRProductivity growth @ 2.0%:(a) Public research onlyR&D from 191817.017%R&D from 195320.5(b) Public + private research + extensionR&D from 191812.216%R&D from 195314.6(c) (b) + Gains to Australians only (80%)R&D from 19189.715%R&D from 195311.7Productivity growth @ 1.2%:(a) Public research onlyR&D from 191811.316%R&D from 195313.6(b) Public + private research + extensionR&D from 19188.115%R&D from 19539.7(c) (b) + Gains to Australians only (80%)R&D from 19186.514%R&D from 19537.8 Under the most optimistic scenario where all productivity gains at the rate of 2.0% are attributed to domestic research investments made since 1918, the internal rate of return (IRR) is 17% and the benefit-cost ratio is 17.0:1. If it is assumed that productivity gains from domestic public and private research and extension result in productivity gains of 1.2% then the IRR is 15% and the benefit-cost ratio is 8.1:1. In both these scenarios, benefits from this stream of research investments accruing after 2003 for potentially many more years are ignored. These IRRs, based on gross benefits, are most comparable to those normally reported in the literature. Table 6 also reports IRRs and benefit-cost ratios for scenarios in which the leakage of benefits to non-residents of Australia is recognised and which are a little lower. All estimated IRRs are within the range first suggested by Mullen and Cox (1995), although at the lower end of this range. Conclusions While there is some evidence that productivity growth in the cropping sector has declined in the past decade, for Australian broadacre agriculture as a whole, productivity growth has remained at around 2.5 percent per year. It has been high relative to other sectors of the Australian economy and relative to the agricultural sectors in other OECD countries. In particular, the ratio of productivity growth in Australian agriculture to productivity growth in the Australian economy as a whole has been high relative to other countries. Over the past decade, the terms of trade facing Australian farmers has declined at a much slower rate. Hence it is likely that productivity growth has improved the competitive position of Australian agriculture. Despite a series of poor seasons, the real value of output from Australian agriculture has remained consistently above $30 billion for the first time since the early 60s. While productivity growth has remained high, public investment in agricultural research in Australia has been static ($700m in 2004 $s) for two decades and has declined in research intensity terms while the research sector has continued to evolve both in terms of where investments are made and how they are managed. The ABS statistics reveal a shift in research resources to plant industries from animal industries which may underpin average productivity growth given the observed higher rates of productivity growth in the cropping industries. The increasing importance of funding through RDCs and CRCs may well mean that a greater proportion of research investment is of an applied nature, boosting productivity growth in the short run but perhaps at the expense on growth in the longer term. The pursuit of environmental outcomes through agricultural research is a more dominant influence in the management of public research than previously. While investment in research in ABS Socio-Economic Objective Classification categories related to agriculture has grown little, it is likely that investments in research by traditional agricultural research agencies now classified as having environmental objectives has grown. Within agricultural research institutions, much of this environmental research is focused on developing technologies to ameliorate or accommodate degradation in a manner profitable to farmers. Hence, some investment in environmental research is likely to be underpinning continued productivity growth in agriculture. The joint nature of agricultural and environmental outcomes and the inadequate accounting for environmental outcomes is a source of bias in the measurement of productivity, research investment and returns to research, particularly from society s perspective. A common view has been that traditional measures overstate productivity growth because they ignore resource degradation. The focus on improved environmental outcomes from agricultural technologies, still unmeasured, means that this bias is at least smaller and in some agricultural systems may be negative. The share of research conducted in the public sector funded by RDCs now approaches fifty percent and the growth of the CRC system has fostered greater cooperation and sharing of both human and physical capital by public research institutions not just though CRC partnerships. The role of the public sector in funding agricultural research remans under scrutiny. Given the long lags noted above, the impacts of neither the decline in investment nor the offsetting changes in the research portfolio and its management are likely to be exhausted yet, hence even though productivity growth has remained healthy, concern about current rates of investment in research are understandable. Both econometric and productivity decomposition techniques were applied to assess the likely rates of return from public investment in research in broadacre agriculture. The least that can be said is that the returns on investment are likely to have remained within the 15- 40 percent range estimated by Mullen and Cox (1995). Again the lower returns are associated with the 35 year lag model and the higher returns with a 16 year lag model estimated for the period since 1969. The substantive change from Mullen and Cox s work was the finding that for the extended dataset, a linear-in-logs specification was no longer adequate in explaining the relationship between research and TFP but that introducing a quadratic term associated with the knowledge stock variable restored good econometric properties. More strongly the results presented here suggest no evidence of a decline in the returns from research. In fact the marginal impact of research, now related to the level of research investment (through the quadratic term), increases with research over the range of investment levels experienced from 1953 to 200, a finding which lends support to the view that there is underinvestment in agricultural research. These results were obtained from econometric models which maintain strong assumptions about how investments in research and extension translate into changes in TFP and from which some variables expected to influence TFP have been omitted . Nor is there much information available about the opportunity cost of alternative uses of public funds. Hence some caution in interpreting the results is warranted. References ABARE 2004, 'Australian Beef Industry: Productivity', ABARE research report 04.2. ABARE 2006, Data commissioned by DPI Victoria. Alexander, F. and Kokic, P. 2005,'Productivity in the Australian Grains Industry.' Report prepared for the Grains Research and Development Corporation, Canberra. Alston, J. M. 2002, 'Spillovers', Australian Journal of Agricultural and Resource Economics, 46, 315-346. Alston, J.M., Chan-Kang, C., Marra, M.C., Pardey, P.G. and Wyatt, T.J. 2000,'A Meta-Analysis of Rates of Return to Agricultural R&D', IFPRI Research Report 113, IFPRI, Washington, D.C., Beck, A.C., Moir, B., Fraser, L. and Paul, P. 1985, 'Productivity Change in the Australian Sheep Industry - a Zonal Analysis: 1952-53 to 1982-83', 55th ANZAAS Congress, Monash University, Melbourne. Bernard, Andrew B. and Jones, Charles I. 1996, 'Productivity across Industries and Countries: Time', Review of Economics & Statistics, 78, 135. Brown, R.L., Durbin, J. and Evans, J.M. (1975),  Techniques for testing the constancy of regression relationships over time , Journal of the Royal Statistical Society, Series B, 1975 (2): 149- 192. Chambers, R. 2005, unpublished, Stochastic Productivity Measurement, University of Maryland. Coelli, T. and Rao, P. 2003, 'Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries,1980-2000', CEPA Working Paper 022003. Coelli, T.J., Rao, D.S.P., O'Donnell, C.J. and Battese, G. 2005, An Introduction to Efficiency and Productivity Analysis, Springer. Cox, T., Mullen, J. and Hu, W. S. 1997, 'Nonparametric Measures of the Impact of Public Research Expenditures on Australian Broadacre Agriculture', Australian Journal Of Agricultural And Resource Economics, 41, 333-360. Gopinath, Munisamy, Arnade, Carlos, Shane, Mathew and Roe, Terry 1997, 'Agricultural Competitiveness: The Case of the United States and Major Eu Countries', Agricultural Economics, 16, 99-109. Hailu, A. and Islam, N. 2004, 'Multilateral Productivity Levels and Growth Rates for Australian Broadacre Agriculture', 48th Annual Conference of the Australian Agricultural and Resource Economics Society, Melbourne. Hall, J and Scobie, G. 2006,'The Role of R&D in Productivity Growth: The Case of Agriculture in New Zealand: 1926-27 to 2000-01', Working Paper 06/01 Industry Commission 1997,'Assessing Australia's Productivity Performance', AGPS, Canberra. Knopke, P. , O Donnell, V. and Shepherd, A. 2000,'Productivity Gains in the Australian Grains Industry', ABARE Research Report, 2000.1, ABARE, Canberra. Knopke, P., Strappazzon, L. and Mullen, J. D. 1995,  Productivity Growth: Total Factor Productivity on Australian Broadacre Farms , Australian Commodities, Vol. 2, pp. 486-497. Kokic, P., Davidson, A. and Boero Rodriguez, V., (2006),  Australia s grains industry: Factors influencing productivity growth , ABARE Research Report 06.22 prepared for the GRDC, Canberra. Krugman, P. 1996, Peddling Prosperity: Economic Sense and Non-Sense in the Age of Diminished Expectations, Norton, New York. Lawrence, D. 1980, 'Productivity Change in Zones in the Australian Sheep Industry, 1959-60 to 1976-77', Quarterly Review of the Rural Economy, 2, 202-206. Lawrence, D. and McKay, L. 1980, 'Inputs, Outputs and Productivity Change in the Australian Sheep Industry', Australian Journal of Agricultural Economics, 24, 46-59. Males, W.P., Davidson, H., Knopke, P., Loncar, T. and Roarty, M.J. 1990,'Productivity Growth and Developments in Australia's Primary Industries', Discussion paper 90.8, ABARE, Canberra. Martin, P, King, J. and Shafron, W. 2004,'Australian Wool Industry 2004', ABARE eReport 04.20 prepared for Australian Wool Innovation, Canberra. Mullen, J. D. and Cox, T. L. 1995, 'The Returns from Research in Australian Broadacre Agriculture', Australian Journal of Agricultural Economics, 39, 105-128. Mullen, J. D. and Cox, T. L. 1996, 'Measuring Productivity Growth in Australian Broadacre Agriculture', Australian Journal of Agricultural Economics, 40, 189-210. Mullen, J. D. and Strappazzon, L. 1996, 'The Relationship between Research Investment and Productivity Growth: Australian Broadacre Agriculture 1953-1994', Global Agricultural Science Policy for the Twenty-first Century, Melbourne. Mullen, J.D. 2002, 'Farm Management in the 21st Century', Australian Agribusiness Review, 10. Mullen, J.D. and Crean, J. 2006, 'Strong Agricultural Productivity Growth Despite Weaker Public R&D Investment: Does This Make Sense?' Farm Policy Journal, 3, 17-29. Mullen, J.D. and Crean, J. (forthcoming, 2007),  Productivity growth in Australian agriculture: Trends, sources, performance, Australian Farm Institute Mullen, J.D., Lee, K. and Wrigley, S. 1996a,'Agricultural Production Research Expenditure in Australia: 1953-1994', NSW Agriculture, Agricultural Economics Bulletin 14, Orange. Mullen, J.D., Morrisson, C.J. and Strappazzon, L. 1996b, 'Modelling Technical Change in Australian Broadacre Agriculture Using a Translog Cost Model', Georgia Productivity Workshop II, University of Georgia. O Donnell, C., Chambers, R. and Quiggin, J. (2006),  Efficiency analysis in the presence of uncertainty , unpublished manuscript. Pardey, P.G,, Alston, J.M. and Piggott, R., Editors, (forthcoming) Agricultural Development in the Developing World: Too Little, Too Late?, IFPRI, Washington. Pardey, P.G., Alston, J.M. and Beintema, N.M. 2006,  Agricultural R&D at a critical crossroads , Farm Policy Journal, 3(1): 1-9. Pardey, P. G. and Beintema, N.M. 2001,'Slow Magic: Agricultural R&D a Century of Progress', International Food Policy Research Institute, Washington, D.C. Parham, D. 2004, 'Sources of Australia's Productivity Revival', Economic Record, 80, 239-257. Paul, P.B. 1984, 'The Measurement of Productivity Change in Australian Agriculture', Bureau of Labour Market Research Workshop on Measures and Experiences of Productivity Changes, Canberra. Productivity Commission 2005,'Trends in Australian Agriculture', Research Paper, Canberra. Rao, P., Coelli, T. and Alauddin, M. 2004,'Agricultural Productivity Growth, Employment and Poverty in Developing Countries: 1970-2000.' Employment Strategy Papers, Centre for Efficiency and Productivity Analysis (CEPA), School of Economics, University of Queensland., Brisbane. Ruttan, V. W. 2002, 'Productivity Growth in World Agriculture: Sources and Constraints', Journal of Economic Perspectives, 16, 161-184. Scobie, G. M., Mullen, J. D. and Alston, J. M. 1991, 'The Returns to Investment in Research on Australian Wool Production', Australian Journal Of Agricultural Economics, 35, 179-195. Shanks, S. and Zheng, S. 2006,'Econometric Modelling of R&D and Australia's Productivity', Productivity Commission Staff Working Paper, Canberra. Stoeckel, A. and Miller, G. 1982,'Agriculture in the Economy', In Agriculture in the Australian Economy, Williams, D. B. Sydney University Press, pp. 166 - 185. Villano, R., Fleming, E., Farrrell, T. and Fleming, P. 2006, 'Productivity Change in the Australian Sheep Industry Revisited ', 50th Annual Conference of the Australian Agricultural and Resource Economics Society, Manly. Zeitsch, J and Lawrence, D. 1993, 'The Productivity of Specialist Woolgrowing in Australia', Georgia Productivity Workshop, University of Georgia, Athens,. Acquaye, A.K.A., J.M. Alston, and P.G. Pardey.  Post-War Productivity Patterns in U.S. Agriculture: Influences of Aggregation Procedures in a State-Level Analysis. American Journal of Agricultural Economics 85(1)(February 2003): 59-80. Ball V.E., J.C. Bureau, R. Nehring, and A. Somwaru.  Agricultural Productivity Revisited. American Journal of Agricultural Economics, 79(4)(November 1997): 1045-63. Ball V.E., F.M. Gollop, A. Kelly-Hawke, and G.P. Swinand.  Patterns of State Productivity Growth in the U.S. Farm Sector: Linking State and Aggregate Models. American Journal of Agricultural Economics, 81(1)(February 1999): 164-179. Jin, S., S. Rozelle, J.M. Alston, J. Huang.  Economies of Scale and Scope and the Economic Efficiency of China s Agricultural Research System. International Economic Review 46(3)(August 2005): 1033-1057. Pardey, P.G., J.M. Alston, and N.E. Beintema.  Agricultural R&D Spending at a Critical Crossroads. Farm Industry Journal 3(1)(February 2006): 1-10. Pardey, P.G., J.M. Alston, and R.R. Piggott eds. Agricultural R&D Policy in the Developing World: Too Little, Too Late? Washington DC: International Food Policy Research Institute, 2006. Pardey, P., N. Beintema, S. Dehmer, and S. Wood. Agriculture Research and Development: A Growing Global Divide? IFPRI Food Policy Report, Washington, DC: International Food Policy Research Institute, 2006. Table 5: Econometric Results and IRRs from the 35 and 16 year lag models   Little, perhaps less, is known about returns from other areas of public sector investment.  Modern in the sense of being based on a locally flexible functional form that can be interpreted as giving a second order approximation to an arbitrary production function.  This remained the standard approach in Australia until Mullen and Cox (1996), following Diewert s (1992) recommendation, used the Fisher index to measure productivity.  The tenor of a symposium at the 2006 IAAE Conference titled  Global Agricultural Productivity Slowdown Measurement, Trends and Forces was that growth was likely to fall in some countries but empirical evidence confirming this is yet to be published.  Most of the studies referred in this section are based on ABARE broadacre or grazing industry survey data from farms engaged in various combinations of extensive crop and livestock production. Results for broadacre agriculture refer to the whole sample. Results for specialist croppers on livestock producer are based on sub-samples.  Some ABARE reports are prepared from the perspective of broadacre agriculture while the more recent reports take the perspective of the grains industry and hence it is not always clear that the same classifications are being used over time.  Estimated as the coefficient on a time trend in a regression against the log of TFP and a constant.  The average annual growth rate for 1995-2003 was 0.1 percent  Parham (2004) estimated productivity growth between  the same points in successive productivity cycles, from productivity peak to productivity peak .  For the period 1991  2004 (used in Figure 1) there was no trend in the terms of trade.  An improvement in the terms of trade is similar in effect to an improvement in TFP because from the same resource base more output is available on the domestic market while the trade balance is unchanged.  The data here are based on total expenditure by research providers from all sources. Important sources of funds to public research providers have been the RDCs.  Both GDP and GVP were expressed as five year moving averages to remove trends purely related to seasonal conditions.  Fisheries and Forestry have not been included in either R&D expenditure or in GVP and GDP.  RDC levies are generally based on the value of production.  Pardey (pers. comm.) expects to complete a revision of this paper in 2006.  These rates of return are low relative to past studies but they accounted for the leakage of research benefits to non-residents of Australia and the excess burden of raising taxes to fund research.  Forty three estimates were reported ranging from 2.5 562% with a mean of 87.3.  The other three were Alston et al. (19.1%), Pardey (22.3%) and Scobie (22.6%).  There was a problem of double counting capital and labour in estimating the returns to the other industry sectors. This does not appear to be a problem for the agricultural sector where the control variable was public rather than business investment in research. They estimated that without this seemingly unnecessary adjustment the rate of return to public investment in agriculture was 32%.  The sum of normalised recursive residuals estimated by adding observations in a forward direction (Brown, Durbin and Evans, 1975).  As suggested by Garry Griffith.  The t-statistic for the introduced variable associated with the interaction term was larger for both the 16 and 35 year models.  The t-statistic on the introduced variable in the 16 year model associated with the 35 year model was over 3 for both the J  and JA  tests and the corresponding variable in the 35 year model was not significantly different from zero.  Again using the same procedure as Mullen and Cox, the implied IRRs for extension are 7 and 13 percent per annum for the 16 and 35 year models.  They will be understated from a global perspective because the benefits other nations received from Australian R&D are not recognised.  Using a methodology developed by Alston et al. (1994).  Published ABS data on environmental research do not identify where the research is undertaken.       PAGE 19 PAGE 29  TFP growth (%) 2.00 2.20 4.00 0.80 1.30 2.30 0.30 3.20 2.60 3.30 2.00 0.40 1.40 2.60 0.90 3.20 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Australia Canada France New Zealand United Kingdom United States Italy Denmark OECD Country Rao et al. (2004) - period 1970-2001 Coelli and Rao (2003) - period 1980-2000 JL????" 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I"ITIhIpIxI;0000 ?$IfgdS!?kd$$If杔謗?\ H?<%?  ?  ?  T  ?   ???t%?????4?4? la?p?ytS!xI€I侷?,! ?$IfgdS!?kdd$$If杔謗?\ H?<%? ? ? T ?  ???t%?????4?4? la?p?ytS!剶?$If]剶gdS!禝窱篒翴腎????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!腎躀餓鳬JJ????? ?$IfgdS!J J0JDJLJTJ;0000 ?$IfgdS!?kd$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!TJ\J^J咼欽淛?0????kdt$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!淛濲燡??0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!翵腏艼菾蠮????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!蠮襃頙;  & F ??勑剺??$If^勑`剺dS!?kd $$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!頙餔鳭KK K????0?kdv $$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS! KKK"K*K2K????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!2K4K@K;  & F ??勑剺??$If^勑`剺dS!?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!@KBKJKRKZK\K????0?kd" $$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!\K怟↘狵????? ?$IfgdS!狵琄窴;  & F ??勑剺??$If^勑`剺dS!?kdx $$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!窴篕糑綤蔏蘇????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!蘇鬕LLL L????? ?$IfgdS! L"LLL`LbLdL;0000 ?$IfgdS!?kd$$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!dLfLhL?0?kdz$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!hL凩哃嶭朙濴????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!濴燣琇;  & F ??勑剺??$If^勑`剺dS!?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!琇甃禠綥芁萀????0?kd&$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!萀訪諰轑鍸頛????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!頛餖鶯;  & F ??勑剺??$If^勑`剺dS!?kd|$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!鶯麹M MMM????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!M,M.M8MN????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!>NTNVN`NhNpN????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!pNrN孨燦;0000 ?$IfgdS!?kd, $$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!∟?0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!∟腘芅萅蔔蘊????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!蘊蜰贜;  & F ??勑剺??$If^勑`剺dS!?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!贜躈轓郚鐽闚????0?kd.$$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!闚鬘鯪鳱鶱O????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!OOO;  & F ??勑剺??$If^勑`剺dS!?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!O O"O$O,O.O????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!.OfOzO|O~O€O????? ?$IfgdS!€O侽濷;  & F ??勑剺??$If^勑`剺dS!?kd0($$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!濷燨狾睴篛糘????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!糘萇蔕襉躉銸????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!銸鍻騉;  & F ??勑剺??$If^勑`剺dS!?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!騉鬙﨩PPP????0?kd2,$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!PPP(P0P8P????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!8P:P凱淧濸燩;0000 ?$IfgdS!?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!燩?0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!腜芇萈蔖襊????? ?$IfgdS! & F ??勑剺??$If^勑`剺dS!襊訮郟;  & F ??勑剺??$If^勑`剺dS!?kd40$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!郟釶銹鍼頟餚????0?kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS! ?$IfgdS!餚騊鬚鯬鳳鶳????? ?$IfgdS!刪?$If^刪gdS!鶳黀tQvQ;61+1$gdAVgdAVgd靏??kd?$$If杔謗?\ H?<%???T? ???t%?????4?4? la?p?ytS!vQ`SbSW W瞆碯N\P\俖衊襚ccxeze`jbj榩歱╲獀JyLy陓靯0}??????????????????????????gd?Sgd?@gdAVgd??gdS\?1$gdAV0}2}Z?\?|?~?軉迒鑷陣顕饑0?2?4?貓趫軋陥靾??????????????????????????? ???1$$Ifgd剘? 1$$Ifgd剘?gdmta 劆刞鷁劆`刞鷊dR}gd剘?gd?S?,?.?B?D?Z?????? 1$$Ifgd剘? ???1$$Ifgd剘?Z?\?^?悏;0 ???1$$Ifgd剘? 1$$Ifgd剘??kd郕$$If杺?斁謭 ?!?6?€ €?€?€?€?€??????????4?4? 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