Introduction
In this chapter, a review of the literature on the returns from research and development (R&D) and the impact on international trade is discussed. A broad perspective of R&D benefits and productivity growth in the agricultural sector will be explained in the first section. In the next section, the effect of research lags and distributions of benefits from research will be explained. Some market distortion policies and the impacts on trade and research benefits will also be evaluated in the next section.
Agricultural R&D expenditure and productivity
Agricultural productivity growth is essential for the overall economic growth for developing as well as developed countries. Measuring the agricultural productivity has been a major focus for agricultural economists and policy makers. A huge number of empirical works have explored the impacts of R&D on productivity growth in developed countries (for example, Thirtle and Bottomley 1989; Huffman and Evenson 1992; Chavas and Cox 1992, Mullen and Cox 1995) and developing countries (Fan 2000; Coelli and Rao 2005; Nin et al. 2003; Alene 2010; Fulginiti and Perrin 1998, Lusigi and Thirtle 1997).
Before examining the relationship between agricultural R&D and productivity growth, it is important to understand the meaning of productivity. By definition, productivity is measured by the quantity of output per unit of input used. According to Hall and Scobie (2006), though, higher productivity can also be caused by other sources; the main source of productivity growth is increments in the stock of knowledge. Since the stock of knowledge is difficult to quantify, the aggregated R&D expenditures have usually been used as a proxy. Intuitively, knowledge is derived from research, thus an increase in R&D expenditures is usually assumed to increase the stock of knowledge.
34 A seminal contribution by Griliches (1958) on the measures of return to investments in agricultural R&D gave impetus to hundreds of studies in this particular area. As the pioneer, Griliches (1958) used the cost-benefit approach and showed internal rates of return of 35 to 40 per cent in corn research and 20 per cent return in hybrid sorghum. He estimated the loss in the net economic surplus if the hybrid corn were to “disappear” and assumed that the supply curve would shift upwards as in the case of a perfectly elastic supply (long run) or alternatively shift to the left as in a perfectly inelastic supply (shorter run). He found that the estimated losses were somewhat greater in the case of using the perfectly inelastic supply of corn assumption and so used the more conservative perfectly elastic supply case.
Furthermore, Griliches (1964) was one of the first to use regression analysis and the Cobb-Douglas production function, and found that research expenditures had significant contributions to output and had similar rates of return as in his earlier studies in 1958. Following Griliches (1958, 1964), Peterson (1967) employed an index-number approach to measure the percentage decrease in the supply function and thus relaxed the supply elasticities assumptions of Griliches. He used both cost- benefit and production functions to analyse the rates of return to R&D in the poultry market and found a 20 to 30 per cent return from the investment. Much of the earlier work had focused on partial measures of productivity in terms of labour and land productivity. The econometric and non-econometric approaches used to evaluate the benefits of research will be discussed in detail in the next chapter.
Recent studies have also found significant contributions of agricultural R&D research to productivity growth (Thirtle and Bottomley 1989; Lusigi and Thirtle 1997; Mullen and Cox 1995; Mullen 2007; Hall and Scobie 2006; Sheng et al. 2011, Thirtle et al. 2008). Alene (2010) found that R&D was a profitable investment in African agriculture with a rate of return of 33 per cent per year while Mullen et al. (2008) found 15 to 20 per cent returns to investment in New Zealand and Australia.
From previous literature, it is obvious that the returns to agricultural R&D investment are significant and continue to increase over time as reported in Alston et al. (2000). They have compiled a list of 292 studies published from 1953 to 1997 and reported 1,886 estimates of rates of return to agricultural R&D. Since the average rates of
35 return measure leads to some misinterpretation, the median was used in their compilation. The median return to investment estimates for research and extension were 48 per cent and 69.2 per cent respectively.
Apparently, only 21 per cent out of the 1,886 estimates of the rates of return to agricultural research fell within the conventional range of 40 to 60 per cent per year (Alston et al. 2000). Additionally, they found a wide spread of rates of return around the average and significant positive skewness in the distribution. To understand the variation in the measured rates of return, Alston et al. (2000, p. ix) listed various factors such as:
Characteristics of the measures (e.g., ex-post or ex-ante, average or marginal, private or social, nominal or real).
Characteristics of the analyst (e.g., differences in the method and approaches used by individuals or groups of researchers or differences in precision of the measures attributable to the person or group who generated the estimate or the differences between research conducted by different groups (university, government, research institute or private sector).
Characteristics of the research being evaluated (e.g. type of technology, field of science, commodity class, time period, geographical location, institutional scope of research).
Characteristics of the research evaluation (methodologies used to evaluate the rates of return, e.g. lag structure (gamma, trapezoid or inverted “V”, polynomial) or lag length (short lags or long lags) or market distortion and spillovers taken into consideration when evaluating the research).
Besides these characteristics, the size and distribution of the research benefits depended on the R&D induced supply shifts. In the next section, the induced supply shifts will be elaborated in more detail.
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Supply shifts and research benefits
There has been debate on the functional forms and the nature of the supply shifts when evaluating R&D. In the market model, the supply curves were typically assumed to be linear or non-linear and the R&D induced supply shifts to be either parallel or non-parallel. In empirical work on evaluating research benefits, assumptions that have been used were nonlinear and pivotal supply shifts (Peterson 1967; Akino and Hayami 1975; Ayer and Schuh 1972; Wise 1984), linear and parallel shift (Griliches 1958, 1964; Hertford and Schmitz 1977; Rose 1980; Edwards and Freebairn 1984), linear and pivotal supply shift (Linder and Jarrett 1978; Rose 1980; Norton et al. 1987) and a proportional shift (Peterson 1967). Producers tend to obtain negative returns with a divergent pivotal shift when demand is inelastic (Duncan and Tisdell 1971). Voon and Edwards (1991) showed that the gross annual research benefits calculated for the non-linear supply curve with a pivotal shift differ from the linear supply curve.
However, Rose (1980, p.837) suggested that assuming a parallel shift for a supply curve is the most realistic strategy. He argued that for most innovations, the only available information will be the cost-reduction estimate for a single point on the supply curve, thus the knowledge pertaining to the shape of the supply curve or the position for single point estimates will not be available. It was suggested by Alston and Wohlgenant (1990) and agreed by Alston et al. (1995, p.64) that “under the assumption of a vertically parallel, research-induced supply shift, the functional forms of supply and demand are unimportant...”. Furthermore, in Zhao et al. (1997), they found that if the proportional supply shift is assumed, significant errors in surplus changes are possible. Thus, parallel shift of the supply curve seems to be plausible, particularly when the innovation involves land-augmenting technology, as in Martin and Alston (1997). Hence, in this study, linear demand and supply functions and a parallel shift have been used.
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Quality enhanced research
As discussed above, not only production improvement results from research, but also quality enhancement. Unlike productivity, the improvement in quality due to research shifts the demand curve outward (e.g. Unnevehr 1986; Lemieux and Wohlgenant 1989; Voon and Edward 1992). Unnevehr (1986) and Kim and Sumner (2005) have estimated the research benefits in rice grain quality improvement in Asian countries. Voon and Edwards (1992) assessed the research payoff for producers in Australian wheat with a one per cent point increase in the protein content. They found that 90 per cent of the net benefits accrued to the wheat producers. Other research pertaining to quality improvements have been based on the valuation of product characteristics (Dalton 2003; Ara 2003; Hurley and Kleibenstein 2005; Harris 1997). However, in this study, due to unavailability of disaggregate data on rice, it has been assumed that R&D activities are focused on quantity improvement.
Public and private investment in agriculture
Most of the agricultural research on productivity improvement has been carried out by the public sector until the Green Revolution and the Gene Revolution. Since then, participation of the private sector has started to increase. Moreover, in recent years, the private investment in agricultural research in developing countries has increased twofold compared with the public sector and the private expenditures account for almost half of the total R&D expenditures (Hareau 2006; Beintema and Elliot 2011; Alston et al. 2010). However, this scenario is not the same with the developing nations. In most developing countries, private sector underinvestment in agricultural R&D is due to a weaker definition of intellectual property rights on innovations (Alston et al. 1998; Pray 2001). Furthermore, there are weaker linkages between private-public sectors as there tends to be a lack of significant research programs in the private sector (Pray 2001).
Krishna and Qaim (2007) suggested that both the public and private sector benefits if technology transfers were properly designed and managed. They found that private- public technology transfer gives an added advantage to the private sector in which the agreement facilitates regulatory procedures and lessens the public calls for
38 government price intervention. Moreover, Pingali and Traxler (2002) have proposed that the public sector should focus on the areas of underinvestment by the private sector such as self-pollinating crops (rice and wheat) to help the poor farmers. They also recommended the need for collaboration between public, private and multinational sectors.
Though most of the studies focused on the public R&D, only some have focused on the relationship between public and private R&D and the difference in the rates of return. Chavas and Cox (1992) estimated internal rates of return of 28 per cent and 17 per cent for public and private R&D respectively using a non-parametric approach for the period of 1950-1982. Yet, Huffman and Evenson (1992) for the same time period, reported a higher rate of 41 for public and 46 per cent for private R&D. Using an error correction model, Makki et al. (1999) estimated 27 per cent and 6 per cent returns to public and private R&D respectively. However, Thirtle et al. (2004) suggested that the relationship between public and private R&D is complementary rather than a substitute. In this study, both the public and private R&D was combined as total R&D and separate effects were not evaluated. In the next section, the potential outcomes from agricultural biotechnology will be reviewed.
Ex-ante studies on agricultural biotechnology
In assessing the impact of agricultural biotechnology, many researchers have used both ex-ante and ex-post evaluations. One of the ways, as suggested by Mamaril (2002), was to evaluate the potential impact pathways for rice biotechnology R&D output like Bt rice and to use this information in research prioritization and development of effective product deployment strategies. Huang et al. (2004) examined the cost and benefits of R&D of the genetically modified (GM) crops, cotton and rice, in China and found that the gains from the GM were substantial. However, they could not find any significant impact on global trade, despite higher productivity that had resulted from the R&D. In a recent study by Anderson and Nielsen (2004), it was pointed out that import bans on GM technology had a large adverse effect in Western Europe, on those who practised the import bans, but the global economic welfare from the new biotechnology was not influenced by the distorted policies.
39 In the case of transgenic rice16, Annou et al. (2005) have found that an increase in production due to research leads to a decrease in price and expanded rice consumption that benefits consumers, while the producer welfare gains are small or negative. But the implication is limited in their studies as no trade regulations were assumed to be imposed on transgenic rice.
In this section, R&D and its impact on productivity and producer and consumer benefits have been reviewed. However, the size and distribution of benefits from R&D depend upon the lag effects of R&D, taking into consideration the gestation lags and how long the R&D continues to affect production. In the next section, the importance of lag length and shapes when evaluating research benefits will be discussed.
Research lags and structures
As explained in the earlier part of this chapter, agricultural productivity was assumed to be measured based on the stock of knowledge, which was modelled as a function of a distributed lag of past research expenditures. In an econometric estimation, productivity was regressed against the weighted average of past research expenditures. But questions emerged in estimating weighted research expenditures: how long will it take the research to affect the production? Then, how long will it last, 10 years, 15 years or forever? What would be the shape of the lag, polynomial or exponential? Despite a huge number of studies conducted to determine the returns to agricultural research, choosing an appropriate lag profile and the length of past research expenditures still remain questions among researchers.
Some researchers have used finite lags, while others have opted for infinite lags. Even though the stock of knowledge derived from the research depreciates over time, but to some extent, the effects of research will still persist (Alston et al. 2008). For practical reasons, many studies used finite lags to formulate the relationship with aggregated R&D expenditures. Alston et al. (2000) compiled a comprehensive meta-analysis of 1,886 observations from 292 publications. They found that 28 per cent of the
16
40 estimates were from studies with research lag lengths in the range of 11 years to 20 years.
It was common for researchers to limit the lag length up to 20 years in the 1980s but as more time series data became available, researchers used lag lengths up to 35 years (Mullen and Cox 1995; Huffman and Evenson 2006; Thirtle et al. 2008; Sheng et al. 2011; Alston et al. 2010). The study by Alston et al. (2008) tested for longer lags of 35 and 50 years for the U.S. agricultural research. In a recent study by Sheng et al. (2011), 16 and 35 years for R&D lags were used and found that the 35-year lag periods captured the effects of past R&D expenditures in the Australian broadacre agriculture better than the 16-year lags. However, in developing countries, the available time series data are usually not sufficient for testing longer lags. As an example, in Africa, Alene (2010) used only a 16-year lag and Schimmelpfenning et al. (2000) employed a lag of nine years between R&D expenditures and productivity. The reason these studies have used shorter lags in African studies was also due to the generally adaptive nature of the R&D.
Besides lag length, the R&D lag shape also affects the rates of return. Various types of lag shapes, designed to represent the stock of knowledge, have been used by many researchers. According to Alston et al. (2000), there were 5 types of lag shapes that have been used, namely, inverted-V, polynomial (second-order), polynomial (higher- order), trapezoid and free-form. They found that the most common types used were inverted-V or known as de Leeuw (Evenson 1967; Mullen and Cox 1995; Kim and Summer 2005), trapezoid (Huffman and Evenson 1989, 1992, 2006; Mullen and Cox 1995; Sheng et al. 2011) and lower order polynomial lags (Davis 1980, Knutson and Tweetan 1979; White and Havlicek 1982; Hastings 1981; Thirtle et al. 2008; Alene 2010). Studies by Alston et al. (1998), Alston et al. (2010), Schimmelpfenning et al. (2000), Sheng et al. (2011) and Bervejillo et al. (2011) had used gamma lag models which have more advantages compared with other shapes. Even in some studies, the geometric distribution, which was commonly used in industrial R&D, has been employed (Shank and Zheng 2006; Alston et al. 2010; Sheng et al. 2011). Also, free- form lag shapes have been used by some researchers to determine the impacts of research (Chavas and Cox 1992; Pardey and Craig 1989).
41 Based on Alston et al. (2000) and their meta-analysis, in the 1950sto the 1970s, most studies used the inverted-V shape or de Leeuw shape and from the 1970s, there has been a movement in the R&D lag shape to trapezoid and polynomial (second order). However, Thirtle et al. (2008) found that the beta distribution was most preferred compared with the most common lag shape, namely the trapezoid and polynomial, since both did not fit the data well, though both yielded significant elasticities.
It is essential to choose an appropriate length and shape for the lag profile so as to estimate the rates of return to R&D. Restrictions imposed on the lag length and lag shape could lead to direction and magnitude biases in the rates of return (Alston et al. 2008). Furthermore, the rates of return to public R&D are sensitive to lag shape and length (Thirtle et al. 2008) and could lead to under or over estimates of returns. According to Alston et al. (2000), in econometric studies, most studies with shorter lags (within some forms of lag) are likely to have larger average rates of return. Lag shapes also have an important role in determining the rates of return, where the polynomial and free-form lags lead to lower rates of return compared with the common lags, trapezoid and inverted-V. Thus, in this study the inverted-V, trapezoid and gamma distributions will be employed and lag lengths of 16 and 35 years used due to the time span of available data. More detail on the R&D lags will be given in Chapter 7.
Market Distortion Policies and Benefits from Research
The agricultural sector is often a highly distorted sector with various government interventions both in the developed and developing countries. Price distortions in the agricultural sector are debated among various interest groups such as producers, consumers, governments, international competitors and environmentalist (Sadoulet and de Janvry 1995). In this section, the impact of various forms of market distortions in evaluating research benefits will be considered. As discussed in Chapter 2, Malaysia practises many of the distortionary policies. Thus, it is important to review some of the related studies.
Measuring the size and distribution of research benefits assuming a free market when in fact, there are restrictions could lead to over or under-estimates of rates of return. It
42 is essential to investigate the benefits of research under market distortions including price controls, tariffs, subsidies and taxes. Furthermore, examining whether the country is small or large, exporting or importing could significantly impact the distribution of research benefits.
According to Alston et al. (1988), there is a strong relationship between distorted and undistorted research benefits. Alston and Martin (1995) also agreed with this statement, as they found that the research benefits that accrue in a distorted market were equal to the benefits in the undistorted market less the deadweight losses from the distortion. One of the earlier studies on the distribution of research benefits under market distortions, Alston et al. (1988) explored the benefits of research under a range