At home and abroad: An empirical analysis of innovation and diffusion in energy technologies

Journal of Environmental Economics and Management 61 (2011) 119–134

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At home and abroad: An empirical analysis of innovation and diffusion in energy technologies Elena Verdolini a,, Marzio Galeotti b a b

Catholic University of Milan and Fondazione Eni Enrico Mattei, Corso Magenta 63, 20123 Milano, Italy University of Milan and IEFE-Bocconi, Italy

a r t i c l e in f o

abstract

Article history: Received 4 November 2009 Available online 16 December 2010

This paper contributes to the induced innovation literature by extending the analysis of supply and demand determinants of innovation in energy technologies to account for international knowledge flows and spillovers. We select a sample of 38 innovating countries and study how knowledge related to energy-efficient and environmentally friendly technologies flows across geographical and technological space. We demonstrate that higher geographical and technological distances are associated with lower probabilities of knowledge flow. Next, we use previous estimates to construct internal and external knowledge stocks for a panel of 17 countries. We then present an econometric analysis of the supply and demand determinants of innovation accounting for international knowledge spillovers. Our results confirm the role of demand-pull effects, proxied by energy prices, and of technological opportunity, proxied by the knowledge stocks. Our results show that spillovers between countries have a significant positive impact on further innovation in energy-efficient and environmentally friendly technologies. & 2010 Elsevier Inc. All rights reserved.

Keywords: Innovation Technology diffusion Knowledge spillovers Energy technologies

1. Introduction Energy efficiency, conservation and renewable energy are repeatedly cited as prominent options for achieving both climate and energy security goals. Energy is a fundamental ingredient of economic growth, which can however be hindered by the negative externality associated with emissions from fossil fuel combustion. While renewable energy sources can potentially replace fossil fuels, both their costs and the technological barriers to wide scale deployment remain high. Researchers and policy makers recognize that technology could potentially change the dynamics that characterize climate and energy systems [67]. Understanding technological change (TC henceforth) and the response of technology to economic incentives is crucial to design the appropriate energy and environmental policies. In the field of energy economics the potential of TC is related to concerns for energy supply and to the complexity of energy systems. Lessening the dependence from imported fossil fuels and mitigating the effects of rising energy prices are critical issues both for developed and developing countries. Given the crucial role of technology in easing this dependence and in providing alternative sources of energy (i.e. renewable), the focus of the debate is the interplay between TC and energy and environmental policy. Moreover, the inherent complexity of energy systems implies substantial and irreversible investments that have intertemporal and international effects, raising concerns for lock-in effects.1

 Corresponding author. Fax: +39 0252036946.

E-mail address: [email protected] (E. Verdolini). Technological lock-in refers to forces creating barriers to the diffusion of environmentally superior (higher efficiency) technologies in energy systems. Such barriers are for example sunk costs and long-lived capital stock. The deployment of both new and existing technology is an important requirement for TC. 1

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TC is expected to play a major role in reducing greenhouse gases emissions. The IEA’s Energy Technology Perspectives Report [30] suggests that energy efficiency improvements in buildings, appliances, transport, industry and power generation represent the largest and least costly options to reduce CO2 emissions. In particular, fuel and electricity efficiency is expected to contribute for more than 38% to the reductions needed to meet IEA’s BLUE scenario, namely a 50% decrease in CO2 emissions by 2050. Renewable energy sources also represent important options: collectively, the IEA foresees that renewables can account for 17% of emission reductions in the BLUE scenario. These considerations are coupled with the claim that the rate and direction of TC can be induced by policy intervention [32]. Over the longer term, complex and less immediate technological solutions promoting renewables and energy efficiency are very relevant, thus worthy of close investigation. Given the global nature of environmental and energy issues, a particularly important role is played by the diffusion of innovation at the international level. Pizer and Popp [52] identify two potential ways in which foreign knowledge can influence the domestic economy. A first effect results from the flow of knowledge across borders: foreign inventions can increase the productivity of domestic Research and Development (R&D) investments by inspiring additional domestic innovation. In this case, increases in productivity are the result of a positive externality, namely knowledge ‘‘spillovers’’. On the other hand, foreign technologies might be simply ‘‘transferred’’, i.e. introduced in foreign markets and adopted. In this paper, we focus on the first channel: international knowledge flows and spillovers. The majority of R&D activities, both general and energy-related, are carried out in a few developed countries, most significantly the United States, Japan and Germany. If technological change is to play a role in addressing global issues, it is crucial to identify the channels through which knowledge diffuses at the international level and to assess what its spillovers are. While some studies focus on technological diffusion across countries [39], to our knowledge no study has yet addressed the issues of knowledge flow and spillovers specifically with respect to energy and environmental technologies. Our results are relevant for two main reasons. First, they can help to design appropriate policies aimed at stimulating innovation and at reducing the cost of mitigation options. Second, the analysis we present responds to the need for better empirical understanding of knowledge flows and spillovers voiced by the modelers’ community [52]. The induced innovation hypothesis, first proposed by Hicks [27], posits that both increased demand and increased technological opportunity in a given country affect the production of additional knowledge. Popp [54] confirms this insight in his analysis of inducement in energy efficient technologies for the United States. There are however two important issues that need to be addressed in this regard. First, as mentioned above, it is necessary to account for international knowledge flows and spillovers: to what extent is the innovation carried out outside national borders available in a given country and how does it affect the production of knowledge? Second, the analysis has to be extended to other innovating countries to assess the validity of the conclusions reached for the USA. This paper contributes to the literature by addressing precisely these two issues and focusing on innovation in the critical fields of energy-efficient and environmentally friendly technologies. Using data on patent citations the paper also investigates the empirical determinants of knowledge diffusion. Drawing on work by Peri [50] we study the geographical and technological channels through which innovation in the above-mentioned fields becomes known in and available to countries other than the innovating one. Such an analysis allows us to construct weights to proxy for knowledge flow between countries. These weights are used, together with data on patents, to construct measures of both internal and external knowledge stocks in selected energy technologies. The final aim of this analysis is to assess how the process of innovation responds to changes in demand (as proxied by energy prices, but also by indexes of energy-related policies and value added in the economy) and in technological opportunity (measured using both knowledge stock proxies), fully accounting for knowledge spillovers. As a consequence, this paper is of relevance both for the general literature on technological change as well as for the literature that studies environmental and energy-efficient inducement.

2. Technical change, knowledge spillovers, and environmental economics In his induced innovation hypothesis, Hicks [27] first emphasized the role of relative factor prices in spurring invention aimed at saving on relatively more expensive production inputs. The link between factor prices and the process of innovation was formalized initially by Ahmad [3], Kamien and Schwartz [37] and Binswanger [5]. In these frameworks, price changes affect a firm’s decision regarding R&D investment and efforts, thus influencing the rate and direction of innovation and resulting in biased technological change. In a different vein, Schumpeter [64] viewed innovation and R&D investment as the outcome of profit maximizing economic agents within the economy as an endogenous response to profit incentives. He suggested that at the heart of modern capitalism was the process of ‘‘creative destruction’’: innovators, attracted by the prospects of a temporary market power, introduce in the market successful products which grant them excess profits for a certain period and which are subsequently displaced by other innovations. Following Hicks and Schumpeter, a number of theoretical and empirical analyses tried to discern the determinants of TC and their effects [21,24,58,59,62,63]. In these analyses, growth is modeled as a process driven by the endogenous creation and

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diffusion of new technologies. In general, research on endogenous TC tends to focus on aggregate R&D expenditure and neutral technological change [32].2 Recent contributions by Acemoglu [1,2] address specifically the issue of the direction and bias of TC, which often benefits some factor of production rather than others. In particular, market size and price effects play a central and conflicting role with respect to the direction of technical change. On one hand, the market size effect encourages innovation that increases the use of the more abundant input. On the other hand, the price effect encourages innovation directed at the scarcer factors. The elasticity of substitution between factors determines how powerful these effects are and, therefore, determines how TC and factor prices respond to changes in relative supply. Of particular relevance for the theory of TC is the debate on the importance of demand versus supply determinants of innovation [7,60,61,63]. In the general literature on technological change the role of international knowledge flows and the effect of knowledge spillovers on economic growth have received much attention. At a more micro-level, the analysis mostly focuses on knowledge diffusion within a given country or a given sector of the economy. Studies like [31,34] show that the flow of knowledge is geographically localized and that technological similarities between innovating and receiving entities favor diffusion. At the macro-level, theoretical studies in the trade-growth literature emphasize the role of international knowledge flows as a channel for growth. Rivera-Batiz and Romer [57], for instance, show that, under certain assumptions, allowing for flows of ideas results in a permanently higher growth rate. Feenstra [18] also concludes that trade and international diffusion of knowledge have to occur simultaneously to obtain convergence in the growth rates of different countries. The empirical trade-growth literature has however devoted little attention to identifying better proxies for measuring the flow of knowledge across countries. The studies confirming strong R&D externalities between countries make assumptions about the availability of ideas across space and mostly use trade information to proxy for knowledge flows. Coe and Helpman [17], for example, explore the effects of domestic and foreign knowledge stocks on a country’s productivity. To this end, they construct a measure of domestic knowledge stock using own R&D expenditures and a measure of foreign knowledge stock using information on R&D expenditures of trading partners. Peri [50] combines both the micro-economic approach on knowledge flows and the macroeconomic analysis of spillovers. He studies knowledge flows across the regions of Europe and North America and uses this information and data on R&D investments to build proxies for internal and external knowledge stocks. He shows that both stocks have a positive impact on aggregate innovation. All these considerations on TC, knowledge flows and spillovers have increasingly made their way to the economics of climate change. Indeed, it is now widely acknowledged that technological change can substantially reduce the costs of stabilizing atmospheric concentrations of greenhouse gases (GHGs). While early studies typically assumed exogenous TC [44,46], more recent contributions have allowed for endogenous TC. Depending on the structure of the climate-economy model (top-down versus bottom-up), different strategies have been adopted, from accounting for R&D efforts to modelling learning-by-doing.3 The importance of knowledge spillovers as one among the sources of TC in formal models of energy and the environment has been extensively discussed [15,16,67]. In this respect, Buonanno et al. [10] appear to have been the first to incorporate international knowledge spillovers in an applied climate-economy model. In light of the scarcity of empirical results that can guide the modelling of knowledge creation, diffusion and spillovers, Bosetti et al. [6] and Carraro et al. [12] explore the sensitivity of predicted mitigation costs to different assumptions on knowledge dynamics. In particular, Bosetti et al. [6] focus on how international knowledge flows affect the dynamics of the R&D sector and the main economic and environmental variables. They demonstrate that model results are sensitive not only to the modelling of international spillovers, but also to the calibration of the parameters. Given the strong dependence of model results on both modelling choices and parameter calibration, it is a matter of concern that the magnitude of induced TC is still uncertain. Empirical studies that have tested the induced innovation hypothesis specifically with respect to environmental inducement are often limited to specific sectors or relative to a single country, thus making it hard to generalize their conclusions [19,56]. Lanjouw and Mody [40] find a strong correlation between pollution abatement expenditures and rate of patenting for several countries, though no econometric analysis is conducted. Jaffe and Palmer [33] use R&D expenditures and patents application as measures of innovative activity and data on regulatory compliance costs to study whether changes in regulatory stringency are associated with more or less innovative activity by US regulated industries. They find that lagged environmental compliance expenditures have a significant positive association with R&D expenditures, but no relationship between compliance costs and inventive output (as measured by successful applications). Newell et al. [43] consider the effect of both energy prices and energy-efficiency standards on the average efficiency of a group of energy-using consumer durables, namely room air conditioners, central air conditioners, and gas water heaters. They show that over time, changes in energy prices induce both the production and commercialization of new models and the elimination of old models from the USA market. On the other hand, the imposition of environmental standards determines a drop of those products which are energy-inefficient. 2 TC is neutral if a change in production technology does not alter the ratio of the inputs’ marginal products, namely it does not affect their relative productivity. Conversely, biased TC favors one input of production over the other(s). Skill-biased TC, for example, is a shift in the production technology that favors skilled over unskilled labor by increasing its relative productivity and, therefore, its relative demand. 3 ¨ See, e.g., [10,13,20,25,45]. Loschel [41] discusses modeling TC in climate models.

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Finally, Popp [54] takes a broader view and analyzes the inducement effect of changing energy prices and technological availability on energy-efficient and environmentally friendly innovations. Using data on US patents and patent citations for the period 1970–1994 he estimates productivity parameters capturing the usefulness (or productivity) of energy patents in a specific technology for a given year. These parameters are then used to construct a stock of knowledge for each energy technology group. Using this knowledge stock to proxy for the supply-push determinant of innovation and energy prices as proxy for demand-pull determinant, he empirically proves that both demand-side and supply-side factors play an important role in the inducement of innovation. This influential analysis is focused on innovation activity within the USA, a single top-innovator country. A legitimate question to ask is therefore whether these results are also confirmed for other top innovator countries as well as for less innovative countries. Even more importantly, being limited to a single country, Popp’s [54] analysis does not account for the international diffusion of knowledge and the consequent spillover effects. As shown by the literature on innovation economics, firms, regions and countries benefit significantly from innovation carried out in other firms, regions and countries, although the magnitude of this benefit is not certain. The role of spillovers is crucial, given that the majority of R&D effort, and subsequent innovation, is carried out in a limited number of developed countries. Finally, the study of knowledge spillovers in energy-efficient and environmentally friendly technologies is important for assessing the true potential of TC with respect to environmental issues, namely lowering the costs associated with GHGs reductions. To specifically address these two issues, we present in the following section a general framework to think about innovative activity and its determinants. 3. Modelling innovative activity Innovation activity is affected by both demand and supply factors. According to Griliches [22], the demand-side determinants of innovation are those macro-shifts (such as shifts in aggregate demand or population) that make inventive activity more (or less) profitable at a given level of scientific information. On the other hand, changes in technological opportunity include scientific and technological advancements that make additional innovation more profitable or less costly at a fixed aggregate or industry level of demand. Formally: IAt ¼ hðZtD ,ZtS Þ

ð1Þ

where IAt denotes innovation activity and Zt is the vector of either demand (D) or supply (S) determinants. The latter in particular is typically taken to be represented by technological opportunities, TOt, which enhance innovation at an unchanged level of demand and are typically proxied by knowledge, a concept which is more amenable to measurement. Knowledge accumulates over time but is also subject to obsolescence. Moreover, knowledge originates from many places, sectors, countries, especially in an era of globalization. There can therefore be important spillover effects from the knowledge formed in country i to innovation activity taking place in country j. We can capture this idea as follows: int ext TOt ¼ gðKt1 ,Kt1 Þ

ð2Þ

where Kt  1 denotes the end-of-period stock of either internal (int) or external knowledge (ext). Using (2) into (1) yields: int ext ,Kt1 Þ IAt ¼ lðZtD ,Kt1

ð3Þ

What are the factors affecting innovation from the demand side? We can think of three elements, all in expected terms. One is E energy prices pt which signal the expected cost of fossil fuel-based technologies: innovation in energy-efficient or no-fossil technologies can be spurred by a high cost of oil/gas/coal because existing technologies become more expensive to operate. Innovation can also be spurred by a high price of electricity suggesting—at unchanged environmental regulation—that it is convenient to invest in new technologies. A second driver of demand is likely to be given by the state of the economy, which can be captured by economy-wide or sectoral value added, VAt. A third and final component likely to be important is the stringency of environmental and energy policy in a given country, EPt: ceteris paribus, a country characterized by the presence of regulation regarding energy efficiency and the environment is going to be a place where innovating on existing energy technologies is most relevant. We can summarize the above considerations in (3) as follows: int ext ,Kt1 Þ IAt ¼ f ðpEt ,VAt ,EPt ,Kt1

ð4Þ

where we expect all impacts to be positive. There are two issues which need to be addressed to make (4) operational: how to measure innovation activity and the stocks of knowledge. We now turn to these aspects.

4. Measuring innovation and knowledge stocks Empirical analyses of the innovation process face the inherently difficult task of finding proper proxies for the measurement of innovative activity and TC as well as for the flow of knowledge between different innovating entities. In this paper we follow the well-established literature that uses patent data to proxy for innovation and patent citations as a measure of knowledge flow between different innovating firms, regions or countries.

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Two indirect methods have been commonly used in the literature to proxy for innovation: R&D investments, which are a measure of the input in the innovation process, and patent data, which proxy for the output of innovative activity.4 Both are indirect measures of innovation, which shed light only on certain aspects of TC [4]. The assumption that patents reflect innovative activity is validated in a number of studies [22]. Among the first, Pakes and Griliches [48] show a strong relationship between R&D expenditures and the number of patents received at the cross-section level, across firms and industries. Griliches et al. [23] study the value of patents as indicator of economic activity and conclude that patent data represent a viable resource for the analysis of technological change. At the macro-level, Pavitt and Soete [49] use patent data to analyze the relative competitiveness of various countries: they construct a ‘‘revealed technology advantage’’ index that allows to compare and contrast the international location of inventive activity in different industries. Others, such as Sokoloff and Kahn [65] use patent data to study the regional patterns of economic growth and the externalities of population size and agglomeration. Even if useful, patents are an imperfect indicator of inventive activity. There are certain limitations in using patents as a proxy for innovation, namely that ‘‘not all inventions are patentable, not all inventions are patented and the inventions that are patented differ greatly in ‘‘quality’’, in the magnitude of inventive output associated with them’’ [22, p. 1669]. In addition to these general limitations, we notice that patent data can shed light only on the dynamics of embodied technological change, namely those innovations that are embedded in new machinery. Conversely, patents cannot provide any insight on disembodied technological change, such as the ‘‘learning-by-doing’’ which also increases productivity. Such issues are clearly left out of a study based on patent data. Keeping in mind the limitations outlined above, the use of patent data with the purpose of investigating technological change and spillovers within energy-efficient product innovation has several advantages. First, patent data are available at the disaggregate level for a number of countries, which allows the identification of both energy-related technologies and of the source country of each innovation. Using information on patents we can proxy for innovation and construct measures of internal and external knowledge stocks for each innovating country in the selected technologies under study. Moreover, in order to be adopted and deployed, energy-efficient products and alternative energy sources need to enter the market and to reach a wide number of potential users. For this reason, we believe that product innovation in this field is likely to be patented. If patents are a proxy for innovative activity, patent citations have been used to track the diffusion of innovations: granting a patent is a legal statement confirming that the innovation represents a useful improvement, over and above the previous state of knowledge. Citations serve the legal duty of delimiting the scope of the property right conveyed by the patent. They therefore represent ‘‘the paper trail’’ left by knowledge diffusion. Jaffe et al. [36] and Jaffe and Trajtenberg [34] among others use citations to study the extent of spillover localization both in the USA and abroad.5 The use of citations patterns has been almost exclusively limited to data relative to patents granted by the United States Patent and Trademark Office (USPTO). Only in the US, in fact, has an inventor the legal duty to declare and cite any previous knowledge on which his/her innovation was built. Jaffe et al. [35] estimate that about one fourth of citations included in a USPTO patent indicate a very important knowledge flow, one fourth of citations indicate an important knowledge flow, while the remaining citations do not give significant information as they have been mostly added for strategic or legal reasons. In other patenting offices, such as the European Patent Office (EPO), the French or German Patent Offices, citations do not indicate any knowledge flow since most of them are added by the examiner of the patent as well as by the lawyer of the innovator [47]. Because of this limitation, in the present analysis of knowledge flows and spillovers we chose to use data relative to patents granted to all innovating countries by the USPTO.6 The patent data used in this paper are extracted from the NBER patent dataset [26] which contains all utility patents granted between 1963 and 2002 by the United States Patent and Trademark Office (USPTO) for a total of more than 3.4 million patents. Information about each patent includes patent number, application date, grant date, technological classification of the patent, name of the applicants and of the inventor as well as information on their country of residence. Starting from 1975, the database includes also information on citations received by each patent in the sample. Using the USPTO patent classification system and following Popp [54] we select patents granted to a group of 38 countries in 12 technologies: seven energy-supply technologies (coal gasification, coal liquefaction, solar energy, batteries for storing solar energy, fuel cells, using waste as fuel, wind energy) and five demand technologies (recovery of waste heat for energy,

4 Patents are a set of territorial exclusionary rights granted by a state to a patentee for a fixed period of time (usually 20 years) in exchange for the disclosure of the details of the invention. Stated purpose of a patent system is to encourage invention and technical progress both by providing a temporary monopoly for the inventor and by forcing the early disclosure of the information necessary for the production of the new item or the operation of the new process [22]. To be eligible for a patent, an invention (device, process, etc.) needs to meet the patentability standards. First, the invention has to be new (not known before the application of the patent). Moreover, the invention should involve a non-obvious inventive step and should be useful or industrially applicable. 5 Note the distinction between ‘‘forward’’ and ‘‘backward’’ citations, depending on the point of view. Backward citations are the citations by patent z to previous patents whose knowledge helped to inspire the innovation included in z. Forward citations, on the other hand, are citations given to patent z by subsequent patents that were inspired by, or that improved upon, the innovations included in z. 6 We are aware of only one other study that uses information on citations in the five major patent offices (USPTO, Japan Patent Office, German Patent Office, French Patent Office and the UK Patent Office) in order to assess the productivity of foreign research on domestic innovation [51]. This study uses a methodology similar to Popp [54]. As we noted above, the main concern with this approach is that citations in countries other than the US are not good proxies for the flow of knowledge since they are mostly added by examiners.

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Table 1 Patents in energy-related technologies by innovating country, 1975–2000. Inventor country

Patents (no.)

% over sample

Avg citation/ patent

0 citation (%)

1–10 citation (%)

4 10 citation (%)

Cited patents (%)

United States Japan Germany France Canada Sweden United Kingdom Switzerland Italy Netherlands Austria Taiwan Australia Israel South Korea USSR/RU Finland Belgium Denmark Norway Hungary Spain South Africa Luxembourg New Zealand China Brazil Argentina Czechoslovakia Mexico Yugoslavia Greece India Singapore Romania Bulgaria Ireland Malaysia Total

11,940 3642 1937 855 491 416 371 366 211 183 169 168 165 142 130 81 68 55 51 43 30 29 23 15 15 11 10 8 7 7 6 5 5 4 3 2 2 2 21,668

55.10 16.81 8.94 3.95 2.27 1.92 1.71 1.69 0.97 0.84 0.78 0.78 0.76 0.66 0.60 0.37 0.31 0.25 0.24 0.20 0.14 0.13 0.11 0.07 0.07 0.05 0.05 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01 100

3.03 2.35 1.84 1.96 3.22 2.19 2.33 2.49 1.61 1.81 1.85 1.62 1.94 2.90 0.88 2.02 1.35 2.24 2.33 1.23 1.53 1.69 2.13 1.07 2.33 0.36 0.90 2.75 2.43 1.14 5.67 0.60 6.80 0.50 2.67 – 1.50 0.50 2.64

31.48 37.56 41.09 37.89 35.44 35.34 36.93 33.61 47.87 37.16 34.91 53.57 44.85 34.51 56.15 32.10 48.53 41.82 31.37 44.19 46.67 41.38 30.43 46.67 33.33 72.73 60.00 25.00 42.86 42.86 33.33 80.00 20.00 50.00 33.33 100.00 – 50.00 34.79

62.71 58.65 56.89 60.35 55.80 62.50 60.38 63.39 50.71 61.75 63.91 43.45 52.12 57.75 43.85 66.67 50.00 54.55 62.75 55.81 50.00 58.62 65.22 53.33 66.67 27.27 40.00 75.00 57.14 57.14 50.00 20.00 60.00 50.00 66.67 – 100.00 50.00 60.61

5.80 3.79 2.01 1.75 8.76 2.16 2.70 3.01 1.42 1.09 1.18 2.98 3.03 7.75 – 1.23 1.47 3.64 5.88 – 3.33 – 4.35 – – – – – – – 16.67 – 20.00 – – – – 4.60

68.52 62.44 58.91 62.11 64.56 64.66 63.07 66.39 52.13 62.84 65.09 46.43 55.15 65.49 43.85 67.90 51.47 58.18 68.63 55.81 53.33 58.62 69.57 53.33 66.67 27.27 40.00 75.00 57.14 57.14 66.67 20.00 80.00 50.00 66.67 100.00 50.00 65.21

Self-citations (citations to same assignee) are excluded.

heat exchange, heat pumps, Stirling engines, continuous casting processing of metal).7 We assign each patent to the country of residence of the inventor. The sample so selected is composed of 21,668 patents granted to American and foreign innovators between 1975 and 2000, as shown in Table 1.8 The US accounts for more than 50% of innovation in the sample, but a significant number of patents are also granted to other countries, with Japan and Germany being the second and third innovators. Table 1 also shows descriptives statistics relative to patent citations, namely the percentage of patents in each country which receive zero citations, between 1 and 10 citations and more than 10 citations. The value of the patents in our sample, as proxied by citations, is highly skewed: close to 35% of patents receive no citation over the period 1975–2000, suggesting that the innovation they represent has been of no value for future innovators. Furthermore, close to 61% of the patents in the sample receive between 1 and 10 citations. The remaining 4.6% obtains 11 or more citations. These last innovations are the ones that have been particularly important for future innovators to build on. Patent value is very different across countries. USA patents are among the most highly cited on average: around 68.52% of USA innovation has been cited by other patents. However, countries such as Canada, India and Yugoslavia have higher average citation per patent than the USA. This indicates that their fewer innovations are more valuable for future research than the

7 The technologies included in this study were chosen based on the list of 22 technologies initially included in Popp [54], note 5, and described in Popp [53]. We use the same technologies selected by Popp [54] since the temporal focus of our analysis is very similar to his. We selected 12 of the initial 22 technologies (while Popp [54] selects 11) and included in the analysis all technologies with at least 200 patents. 8 The estimations presented in this paper are limited to the period 1979–1998 to keep consistency between the estimation of knowledge flows and spillovers. We drop the last years of patent data to avoid truncation problems both in citations and in patent applications. In addition, the price variable used in the estimation of spillovers is available from 1978.

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Table 2 Patents in energy-related technologies by innovating country, 1975–2000. Total patents Non-assigned/individuals Assigned

21,668 4464 17,204

Number of assignees

3,922

Assignees with: 1 patent 2 patents 3–10 patents 11–20 patents 21–50 patents 51–100 patents More than 100 patents

60.35 14.23 18.21 3.26 2.58 0.89 0.48

average US innovation. Moreover, countries like Denmark, South Africa, India, Argentina and Ireland have a higher percentage of cited patents within total energy-related innovation. In addition, top innovators like Germany and France have lower-thanaverage citations per patent than Romania, which is among the bottom 5 innovating countries. Table 2 presents some descriptive statistics with respect to the assignees of the energy-related patents in our sample: over the period 1975–2000 period more than 60% of the innovators are granted only one patent and only 0.48% of the innovating firms and individuals are granted more than 100 patents. One important limitation of the dataset we use is the so-called ‘‘home bias’’ problem. This refers to the fact that any American innovator is likely to patent his innovation first in the US market, for this represents his/her home market. For innovators from all other countries, on the other hand, the US market is not necessarily the first natural outlet for patenting. If it is true that the US market represented the biggest market for technologies well into the 1990s, innovators coming from Europe (for example) are likely to patent their innovations first in their home country or at the EPO before exporting the innovation to the USA. As a consequence, when comparing USA patents with foreign patents, we are to a certain extent comparing ‘‘apples’’ with ‘‘oranges’’. The foreign innovations we observe are all intended for a foreign market (the USA) and therefore likely the most economically valuable patents in the sending country which are duplicated. American patents, conversely, include patents that are marketed only in the home country together with ‘‘international’’ patents. Keeping this difference in mind, we carry out sensitivity analysis to account for the differential economic value of patents and to make sure that results are robust to the exclusion of the USA from the sample. Turning now to the measurement of the internal and external knowledge stocks, we assume that the amount of external knowledge available to country i at the beginning of time t is the sum of the knowledge produced abroad that has crossed country i’s border. Formally: X ext int Ki,t1 ¼ fi,j Kj,t1 ð5Þ jai

where fi,j represents the probability that an idea generated in country j is accessible to country i and where stocks are measured end-of-period. Such a definition indicates that diffusion of knowledge across countries is not perfect: only a fraction fi,j of the knowledge produced in country j is accessible to country i at any time t. Because of the relevance of fi,j , in the following section we measure it using data on patent citations, which proxy for the ^ which we use to flow of ideas between two given countries, and study its determinants. This yields an estimated value f i,j build the external knowledge stock available in country i at time t as in Eq. (5) and we present estimates of Eq. (4). 5. Knowledge flows and the effect of geography and technological distance We model the probability that an idea generated in country j in time t0 becomes available in country i at time t as the combination of two exponential processes [11,50]:

fi,j ðlÞ ¼ ef ði,jÞ ð1ebðlÞ Þ

ð6Þ

where l =t t0 is the citation lag, the time elapsed from the grant date of the cited innovation (t0) and the time of citation, or year of application of the citing patent (t). The probability of citation fi,j ðlÞ is a function of bilateral characteristics of inventing and receiving countries and of the time elapsed since invention l. The term 1ebðlÞ indicates that the likelihood of innovation originating in country j being available in country i grows with the citation lag. The term ef(i,j), on the other hand, indicates that the probability that country i learns an idea coming from country j depends on a series of bilateral characteristics that influence the diffusion of ideas between different countries. This formulation assumes that the effects of the countries’ bilateral characteristics and of time act in a multiplicative way. As time goes by, more of the ideas produced in a region are available in any other country [50].

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Previous studies have shown that geography plays an important part in the diffusion process, as the probability of learning an idea is higher the smaller the geographical distance between the citing and cited entities. The main conclusion of these studies is that diffusion is geographically localized [36,34]. In addition, much research points to the contribution of trade to the international flow of ideas [17,39]. Cultural factors are also important: Keller [38] and Peri [50] demonstrate that a common language has a positive effect on the diffusion of knowledge. Finally, technological specialization affects diffusion [31,9]: the flow of knowledge is higher if the innovating firms, regions or countries are similar in their technological characteristics. We note that it is important to analyze jointly the effect of geography and of technological specialization on knowledge flow since technologically similar firms tend to also cluster geographically. As a consequence, only looking at the geographical determinants can over-estimate their contribution [36]. We study here the effect of geography and technological distance on the diffusion of knowledge. To this end, we assume that the knowledge flow across countries is time invariant and fix the same interval l for all countries, collect the constant terms and explicitly define the function f(i,j) as depending on N proxies of geographical and technological distance.9 Eq. (6) is thus transformed to " # N X fi,j ¼ Cl ef ði,jÞ ¼ exp a þ bn xni,j ð7Þ n¼1

where Cl ¼ 1ebðlÞ . The main results we present are obtained setting l= 10 in the empirical analysis. However, we carried out sensitivity analysis for different values of the citation lags. The empirical results presented in this section are robust to setting l = 5, 15 and 20. The N bilateral characteristics of the cited and citing countries which are used as explanatory variables in (7) are identified on the basis of the knowledge diffusion literature outlined above. They are defined as follows:

 x1i,j is a dummy equal to 1 if the citing and cited country are different, indicating that knowledge has crossed a national border;

 x2i,j is the geographical distance between citing and cited countries;  x3i,j is a dummy equal to 1 if the citing and cited country do not belong to the same trade area, indicating that knowledge crossed a trade block border;

 x4i,j is a dummy equal to 1 if the citing and cited countries have different official languages, indicating that knowledge crossed a linguistic border.

 x5i,j is a technological index adapted from Jaffe [31].10 This index uses information on the distribution of the patents of each couple of countries i and j to measure their distance in technological space. In particular, each country i is associated with a vector Shi = (shi,1,shi,2,y,shi,S) containing the patent shares it generated in each technological field s (shi,s) for the whole period under consideration. The uncentered correlation coefficient (angular distance) between these vectors for each pair of countries is calculated using the following formula: ðShui Shj Þ x5i,j ¼ 1 P P ½ s ðshi,s Þ2 s ðshj,s Þ2 1=2



ð8Þ

The value of the above index is between 0 and 1 and it is equal to 0 for countries which have the same distribution of patenting across the different technologies considered in the analysis. This index is expected to be negatively correlated with the probability of observing a citation (and therefore with the probability that knowledge flows between the two countries). The more similar the two countries are in technological space, the more likely they are to cite each other. x6i,j is a measure of distance in technological development of the citing country j from the cited country i. The index is adapted from MacGarvie [42] and uses information on average forward citations received by the patents of the cited and citing countries. It is calculated as the ratio of the average number of citations received by patents in citing country j (fj,s) to the average number of citations received by patents in cited country i within the same technological field s, averaged over the number of fields in which the citing country patents (Si), minus one. P ðfj,s =fi,s Þ x6i,j ¼ s 1 ð9Þ Sj The index equals zero if the patents granted in the citing country in the 12 technologies considered in the analysis are on average as cited, and therefore as important for future innovation, as those developed by the cited country. Similarly, this index is less than zero if the energy-related patents granted to the citing country are of lower importance (less cited) than those granted to the cited country and it is greater than zero if the patents granted to the citing country are of greater

9 Peri [50] uses a similar approach to study the flow of patented knowledge across different regions of North America and Europe. Three main reasons support the need for a similar study that focuses on innovation in energy-efficient technologies. First, Peri’s [50] analysis is based on patterns of diffusion in overall patenting activity and the conclusions he presents should be tested when considering energy innovations. Second, Peri [50] focuses mainly on analyzing the flow across geographical space and only briefly looks at the flow across technological space. Instead we are interested in a deeper analysis of the contribution of technological distance to knowledge flow due to the peculiar nature of energy innovations and their complexities. Finally, Peri’s [50] analysis is focused only on regions in North America and Europe, while our analysis looks at the flow of knowledge across countries, both more and less developed. 10 The two indexes of technological distance used in this paper are calculated using the patents in the 12 technologies we selected for the empirical analysis.

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Table 3 Geographical and technological channels of knowledge diffusion. Specification

I

II

III

IV

V

Countries

All

All

All

All

No USA

Citation lag Crossing country Border 1000 km further Crossing trade Border Crossing linguistic Border Technological Distance Vicinity of citing to Frontier of Cited

10 years  1.713nnn (0.240)  0.012 (0.014)  0.349nn (0.141)  0.295nnn (0.094) – – – –

10 years  1.333nnn (0.251)  0.011 (0.014)  0.345nn (0.138)  0.203nn (0.091)  1.759nnn (0.321) – –

10 years  1.145nnn (0.241)  0.011 (0.013)  0.347nnn (0.135)  0.200nn (0.087)  1.699nnn (0.311)  0.052nnn (0.019)

20 years  1.186nnn (0.242)  0.011 (0.013)  0.321nn (0.128)  0.162nn (0.081)  1.723nnn (0.298)  0.041nn (0.017)

10 years  1.222nnn (0.263)  0.012 (0.016)  0.420nn (0.183)  0.144 (0.112)  1.813nnn (0.342)  0.057nn (0.022)

Cited country FE Citing country FE

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Observations Log-likelihood

1444  1272.16 7964

1444  1256.54 8370

1444  1251.35 8620

1444  1327.35 9984

1369  962.23 20,118

w2

Dependent variable: all citations within 10 years from grant date of cited patent. Citations calculated omitting self-citations. Negative binomial estimation method, robust standard errors in parenthesis. n nn nnn , , indicate significance at respectively 10%, 5%, and 1% levels.

importance (more cited) than those granted to the cited country. This measure could be either positively or negatively correlated with the probability of observing a citation. In the first case, being a technological laggard negatively influences the probability of observing knowledge flow. In the second case, conversely, a negative correlation would indicate that technological laggards can learn more from a top innovator. The problem in estimating Eq. (7) is that the diffusion parameter fi,j is not observed, and so we use observable patent citations ci,j as a proxy for the diffusion of knowledge [50]: " # N X bn xni,j þ ui,j E½ci,j  ¼ exp ai þ aj þ ð10Þ n¼1

Eq. (10) includes ai and aj which represent citing country and cited country fixed effects controlling respectively for the different propensity to cite across countries and the different propensity to patent across countries. The dependent variable, ci,j, is the count of citations received by patents originating in country i by patents originating in country j within a given time from the grant date of the cited patent. Estimating the coefficients b1ybn in (10) allows us to study how geography and technological specialization affect knowledge flows between two countries and to subsequently calculate the diffusion ^ for each pair of countries by substituting the estimated coefficients in Eq. (7). parameter f i,j We construct the variable ci,j of Eq. (10) by counting the citations received between 1979 and 1998 by patents in country j coming from patents in country i within 10 years from the grant date.11 We exclude self-citations, as standard in the literature, and we provide sensitivity analysis based on different values of the citation lag, namely 5, 15 and 20. We then associate each ci,j with information about the geographical and technological characteristics explained in the previous section. Data on geographical distance come from the Distance Database of CEPII [14]. Eq. (10) is estimated using a negative binomial approach in order to account both for the count data nature of the dependent variable and for the over-dispersion in the data. In particular, the advantage of the negative binomial model with respect to other transformations of Eq. (10)—for example taking logs on both sides—is that it allows to include in the analysis also those observations for which ci,j is equal to zero over the sample period. In our case this is particularly important, since our sample includes not only top-innovating countries, which are likely to be highly cited by any other country, but also countries with a low number of patents over the period, and for many pairs of countries there is no citation link in the period under consideration. 11 This results in 38  38 ¼ 1444 observations The timeframe of the analysis of knowledge flows is consistent with the timeframe of the analysis of demand-pull and technology-push determinants of innovation presented in Section 6. This in turn is determined by data availability and limitations.

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Table 3 reports the results of the maximum likelihood estimation of the negative binomial specifications for Eq. (10). They partly confirm previous findings, but also shed light on the peculiarities of knowledge flows in energy-efficient and environmentally friendly technologies as compared to the generic innovation analyzed in other studies. Specification I includes only the geographical determinants of innovation, while in specifications II and III we add the indexes of technological distance, x5i,j and x6i,j respectively. As expected, the coefficients associated with crossing a country border have a negative impact on the probability of citation and therefore on the probability of knowledge flow between any couple of countries. This confirms that the majority of innovative ideas never crosses a country border. In particular, citation to a foreign patent is between 18.0% (e  1.713) and 31.8% (e  1.145) as likely as the probability of citation within the same country. In addition, note that moving across the different specifications and adding the technological indexes as explanatory variables, the coefficient associated with crossing a country border decreases in absolute value. This indicates that an analysis focusing only on flow of knowledge between different countries would provide biased results. Unlike previous evidence on this aspect, the coefficient of the variable indicating additional distance from citing to cited country (1000 km further) is not significant. It seems reasonable to conclude that once knowledge has crossed the country border, it is not the additional geographic distance but the technological distance that drives diffusion of energy-related innovations. In specification III, citation between countries speaking different languages is around 81.9% as likely as citations between countries with the same official language. Unlike Peri [50], in our analysis the coefficient associated with crossing a trade border is negative and significant and remains stable across all specifications. Specifically, the probability of citation between countries belonging to different trade blocks is 70.7% of the probability of citations within the same trade block. Turning to technology factors, technological distance as measured by x5i,j and x6i,j also has a negative effect on the expected probability of observing knowledge flow. For example, if the citing and cited country have completely different patenting patterns, so that the technological distance index is equal to one, the probability of citation drops to 18.3% of the initial level (specification III). Specifications IV and V represent sensitivity analysis for previous estimations. Specification IV tests the hypothesis that diffusion of knowledge is time-invariant by considering citations within 20 years (instead of 10) from the grant date of the cited patent. The estimated coefficients are in line with those of the other specifications presented so far and confirm the negative impact of both geographical and technological distance on the probability of citation.12 The assumption that the probability of citation is time-invariant, although restrictive, is thus supported by the data. Specification V on the other hand tests the robustness of results to the omission of the USA from the sample. This is particularly important given the different nature of USA and foreign patents. As already mentioned, the USA market is the first natural outlet for USA innovators, but is most likely the second choice for innovators from other countries. The results presented in specification V confirm the effect of geographical and technological resistance barriers. Unlike specifications I–III, however, the variable indicating crossing a linguistic border is not significant. Moreover, the estimated coefficients for all other variables are slightly higher than in the previous cases. 6. Demand and supply determinants of innovation In view of the empirical analysis of the determinants of innovation as represented in Eq. (4), the first step is to compute external and internal knowledge stocks. Using the estimated parameters from specification III in Table 3 we construct the ^ as in Eq. (7) to build a measure for the external knowledge stock available in any given country according to (5). To weights f i,j ^ ¼ 1. Table 4 presents the estimated weights for the external this end, we normalize a= 0 in (7) so that, by construction, f i,i

knowledge stock, with the sending country along the rows and the receiving countries across the columns. We observe that the percentage of knowledge flow goes from a minimum of 0.04% that the Spain receives from Switzerland to a maximum of 31% that the USA receives from Canada. While the flow of knowledge is higher between countries that are geographically close, such as for example the northern European countries or Canada and the USA, it is also true that geography does not tell the whole story: France, for example, receives a higher percentage of the knowledge produced in Japan (15.0%) than the one it receives from the much closer Spain (14.6%). Ideally, to construct the knowledge stock variables data on private R&D for the sector(s) of energy-related innovations should be used. Lacking these kind of data, we use patent data to proxy for internal and external knowledge in each country [8,54]. For each technology field s the stocks are constructed using the perpetual inventory method: Ki,s,t ¼ PAT i,s,t þ ð1dÞKi,s,t1

ð11Þ

The initial value of the stock Ki,s,t0 is calculated as follows: Ki,s,t0 ¼

PAT i,s,t0 ðg s þ dÞ

ð12Þ

where d ¼ 0:1 is the depreciation rate set at a level in line with the literature on innovation [38] and g s is the average rate of growth of patenting in the technology s for the period between t0 and t0  4. We use t0 = 1974 as the initial year to compute the 12

Additional results with citation lag set to 5 and 15 years confirm these findings and are available from the authors upon request.

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Table 4 Estimated diffusion parameters.

US JP DE FR GB CA SE CH IT NL AT AU FI BE DK NO ES

US

JP

DE

FR

GB

CA

SE

CH

IT

NL

AT

AU

FI

BE

DK

NO

ES

1 0.169 0.171 0.162 0.210 0.310 0.159 0.104 0.127 0.177 0.077 0.151 0.151 0.080 0.153 0.122 0.117

0.167 1 0.175 0.163 0.170 0.165 0.150 0.119 0.134 0.161 0.088 0.112 0.147 0.089 0.145 0.120 0.097

0.155 0.163 1 0.242 0.254 0.152 0.235 0.205 0.201 0.231 0.168 0.118 0.231 0.149 0.181 0.184 0.148

0.149 0.150 0.241 1 0.231 0.191 0.221 0.194 0.212 0.227 0.156 0.124 0.223 0.149 0.172 0.196 0.146

0.182 0.154 0.247 0.235 1 0.194 0.189 0.168 0.194 0.234 0.132 0.141 0.233 0.121 0.189 0.175 0.147

0.289 0.155 0.158 0.194 0.198 1 0.144 0.128 0.125 0.174 0.078 0.139 0.150 0.099 0.153 0.124 0.103

0.123 0.130 0.216 0.204 0.201 0.135 1 0.127 0.139 0.214 0.103 0.084 0.283 0.064 0.159 0.159 0.125

0.083 0.100 0.198 0.216 0.158 0.121 0.103 1 0.295 0.125 0.299 0.142 0.157 0.241 0.104 0.201 0.128

0.101 0.106 0.183 0.196 0.172 0.107 0.129 0.130 1 0.135 0.229 0.149 0.161 0.187 0.114 0.214 0.166

0.163 0.149 0.227 0.229 0.227 0.170 0.233 0.129 0.154 1 0.094 0.098 0.216 0.106 0.201 0.153 0.133

0.035 0.048 0.110 0.101 0.075 0.041 0.054 0.080 0.151 0.060 1 0.090 0.061 0.124 0.048 0.174 0.105

0.111 0.087 0.100 0.109 0.118 0.119 0.066 0.125 0.147 0.088 0.128 1 0.096 0.112 0.073 0.131 0.137

0.089 0.098 0.177 0.174 0.168 0.116 0.175 0.060 0.135 0.158 0.111 0.080 1 0.090 0.167 0.182 0.103

0.054 0.062 0.122 0.127 0.097 0.080 0.052 0.098 0.174 0.086 0.222 0.105 0.117 1 0.102 0.191 0.092

0.116 0.112 0.165 0.160 0.170 0.135 0.114 0.044 0.121 0.176 0.072 0.078 0.204 0.111 1 0.148 0.102

0.066 0.070 0.126 0.139 0.114 0.087 0.104 0.067 0.153 0.103 0.169 0.104 0.161 0.126 0.086 1 0.104

0.062 0.057 0.102 0.104 0.093 0.055 0.067 0.040 0.123 0.095 0.117 0.117 0.063 0.072 0.062 0.114 1

knowledge stock, while we beginning the analysis in 1979, as a way to minimize the impact of the way Ki,s,t0 has been calculated. We compute the external available stock of knowledge for country i as the sum of the knowledge stocks of all other ^ as in Eq. (5). innovating countries weighted by the respective estimated diffusion parameters f i,j Having constructed a measure of technological opportunity, representing the supply-side determinants of innovation, we can proceed to analyze their effect together with the demand-side determinants. We are especially interested in the role played by international technology spillovers. Our proxy for innovative activity is the number of patents granted to country i in technology field s at time t. The count data nature of this dependent variable suggests the use of a negative binomial model for the estimation of Eq. (4).13 The estimated model reads as follows: E int ext þ b2 VAi,t1 þ b3 EP i,t1 þ g1 Ki,s,t1 þ g2 Ki,s,t1 þ ei,s,t1  E½PAT i,s,t  ¼ exp½ais þ at þ b1 Pi,t1

ð13Þ

where E[PATi,s,t] stands for the expected number of patents in energy-related technologies in country i in technology field s in E year t, with t being the application year of the patent. Pi,t  1 is the price of energy in i at time t  1, which proxies for expected energy prices and indicates changes in the demand for innovation in energy-related technologies. Ideally, different prices of energy should be used for different technologies, but since detailed data are not available for all the technologies considered we use the IEA real index for end-use energy prices for industry [28].14 In order to proxy for the level of economic activity we include the ratio between the innovating country GDP and the GDP of the USA at time t-1 (expressed in percentage points) as a regressor (‘‘Value Added’’ in Tables 6 and 7). Such a measure is preferred to the simple level of GDP in a given country because we recognize that patents, while useful indicators of innovative activity, have shortcomings. In particular, the patents included in this analysis are those for which foreign countries require protection in the USA. Since, as already mentioned, foreign patents are most likely duplicate patents in the USA, we believe that considerations about the market size of the United States play a role in the decision to ask for protection of a duplicate. Moreover, the United States’ relative importance as a market for technology over time has decreased. Adding the ratio between innovating country GDP and USA GDP aims at controlling for these aspects. In addition to the above variables, we try to capture the policy environment of an innovating country in two different ways. On one hand, we include in the estimation the level of government expenditures in R&D targeting energy efficiency, fossil and renewable sources and storage technologies. These are taken from the IEA Energy Technology R&D Database [29]. Alternatively, we include a dummy variable equal to 1 if at least one policy targeting energy efficiency is in place in the innovating country in any year t [66]. Such an index is not very sophisticated; nonetheless, it is an indication of the presence of policy targeting energy efficiency and should be correlated with higher levels of innovation in the technologies under study. Control variables in our estimation include a set of individual fixed effects ais (country-technology dummies) as well as year dummies at .15 13 An alternative approach would be a log–log estimation in which the dependent variable is defined as the ratio between patents in technology s in country i at time t over total patents granted to country i at time t. This specification is more in line with the one used by Popp [54] but it suffers from the problem that all observations with zero patents cannot be used due to the log transformation of the data. For this reason, we prefer the negative binomial estimation. The analysis was also carried out using this alternative specification. The results confirm the importance of technological opportunity as a determinant of innovation and are available from the authors upon request. 14 The data used to compile the index have been chosen as the most relevant price statistics for which comparable data across countries are available. The resulting index represents a homogenous series with long coverage. A lot of effort was made in order to ensure that the data are internationally comparable across all the countries considered. The index is normalized to 100 in year 2000. 15 The estimation was also carried out including separate dummy variables for time, country and technology effects. The results are in line with the ones presented in this section and are available from the authors upon request.

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Table 5 Descriptive statistics. Variable

Observations

Mean

Std. Dev.

Min

Max

Patents Patents (weighted) Own stock Own stock (weighted) Foreign stock Foreign stock (weighted) Price R&D (billions of 2007 USD and ppp) Value added (GDP/GDPUSA)  100

4080 4080 4080 4080 4080 4080 4080 3720 4080

3.72 2.35 29.89 22.76 61.87 46.90 98.59 0.25 15.38

14.89 9.97 124.59 93.09 90.55 65.24 18.95 0.48 23.38

0 0 0 0 1.21 1.01 52.57 0.01 1.30

210 172 1584.38 1077.94 662.48 442.52 159.93 4.21 100

Table 6 Supply and demand determinants of innovation: patents counts. Specification

I

II

III

IV

V

VI

Dependent variable Countries Technologies Supply determinants Own stock

Patent counts All All

Patent counts All All

Patent counts All All

Patent counts All All

Patent counts All Supply

Patent counts All Demand

1.047nnn (0.007) – –

1.031nnn (0.007) 1.096nnn (0.011)

1.025nnn (0.007) 1.106nnn (0.011)

1.030nnn (0.007) 1.097nnn (0.011)

1.027nnn (0.007) 1.117nnn (0.012)

1.028nnn (0.009) 0.982 (0.019)

1.004n (0.002) – – – – – –

1.006nnn (0.002) – – – – – –

1.004n (0.002) – – 1.279nnn (0.065) – –

1.006nn (0.002) 1.049n (0.028) – – 1.394nnn (0.116)

1.006n (0.004) 1.085n (0.046) – – 1.652nnn (0.253)

1.003 (0.003) 1.007 (0.027) – – 1.240nn (0.109)

Year FE Countryntechnology FE

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Observations Log-likelihood

4080  4365 327,132

4080  4305 321,377

3720  4037 284,879

4080  4296 310,346

2380  2161 151,309

1700  2066 122,315

Foreign stock Demand determinants Energy price Value added Public R&D Policy index

w2

Negative binomial estimation, exponentiated coefficients, robust SE in parenthesis. , , indicate significance at respectively 10%, 5%, and 1% levels. Null hypothesis is that each coefficient is different from 1. n nn nnn

Finally, to better interpret the estimated parameters associated with knowledge stocks we normalize the internal and external stocks so that a one unit change in the normalized variable is equivalent to a 10% change from the mean value for each country/technology group.16 The data available allow us to build a sample of 17 countries (USA, Japan, Germany, France, UK, Canada, Sweden, Switzerland, Italy, Netherlands, Austria, Australia, Finland, Belgium, Denmark, Norway and Spain) for which we pool observations for all technologies over the period 1979–1998. Table 5 reports descriptive statistics while Table 6 presents the results of the estimation of Eq. (13). The coefficients are presented as incidence rate ratios (namely as eb ) and should be interpreted as increasing the expected probability of patenting in country i in field s at time t. Specification I presents results when accounting only for the effect of price and own knowledge stock, similar to Popp [54]. We confirm that both price and own knowledge stock are positively and significantly correlated with the level of innovation in the selected countries and technologies. Specifically, a 10% increase from the mean of the own knowledge stock is associated with an innovation level 4.7% higher. On the other hand, a 1% increase in the price index is associated with an increase of approximately 0.4% in the probability of innovation. Specification II accounts for the role of external knowledge stock on innovation. The coefficients associated with the own knowledge stock and price variables are similar to the ones obtained in specification I: a 10% increase from the mean of the

16 nor The normalization is as follows: Ki,s,t ¼ ððKi,s,t =K i,s Þ10Þ10. The resulting variables have a mean value of 0, with a deviation of 1 unit from the mean equivalent to a 10% increase or decrease from the mean value of the original variable [55].

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131

own knowledge stock is associated with an innovation level 3% higher, while a 1% increase in the price index translates to a probability of citation that is 0.6% higher. In addition, the coefficient associated with the external knowledge stock is highly significant and higher in magnitude than the one associated with the own knowledge stock of any given country, indicating that greater availability of ideas generated outside the country borders is associated with higher levels of domestic innovation. In particular, a 10% increase from the average foreign knowledge stock value increases by 9.6% the probability of domestic innovation. Specification III also includes the level of R&D expenditures, while specification IV includes the proxy for overall value added on the economy and the index of energy-efficient policy. The results show that a 1% increase in the ratio of GDP over USA GDP increases by 5% the probability of patenting. In addition, both policy expenditures as well as the presence of policy targeting energy efficiency have a positive and significant effect on innovation: the probability of innovating is higher for those countries whose government is committed to improving energy efficiency either by spending public money or by passing regulation that targets efficiency. As pointed out above, we recognize that the policy index we propose is not totally satisfactory, and we consider this result as a preliminary one worth of further investigation. Specifications V and VI repeat the estimation separately for supply (S) and demand (D) technologies and show that the effect of demand and supply determinants of innovation is different for the two sub-groups. The coefficient for the price variable is insignificant in both cases. Since the estimate is very similar to the one presented for the joint analysis, the insignificance derives most likely from the smaller sample sizes of the two sub-groups. Turning to the supply determinants of innovation, the effect of the own knowledge stock is similar and significant in both estimations. On the other hand, the effect of foreign knowledge stock is higher and significant for supply technologies, but not significant for demand technologies. A possible explanation may have to do with the different nature of supply and demand technologies. Supply technologies represent possible ways to reduce the dependence from fossil fuel-based inputs: they are often frontier technologies far from market maturity, where research is more basic and learning-by-researching occurs at fast rates and is not yet affected by possible diminishing returns. These technologies often involve much higher investments than demand technologies (for example, in the case of renewable energy). In this sense, they might benefit more from the availability of both domestic and international knowledge. On the other hand, demand-technologies include processes that are more mature but where research can improve energy efficiency. Learning-by-researching rates are less subject to the positive effect of external knowledge stock because innovation in this field is based on incremental improvement of already (locally) developed technologies. This conclusions are however preliminary and need to be validated by further research. The results presented so far confirm the expectation that higher demand for energy-related technologies and higher knowledge stocks are associated with higher levels of innovative activity. In particular, the estimated coefficients indicate that a 10% increase in the external knowledge stock is associated with a higher level of innovative activity than a corresponding increase in the own knowledge stock. When interpreting these results, we need to keep in mind the nature of the patent data used in this study. In fact, we should not conclude that knowledge spillovers from other countries have a higher effect on innovation than investing in own knowledge at home because, for all countries but the USA, the patents we observe represent high value innovation exported to a foreign market. This means that own knowledge stocks represent

Table 7 Supply and demand determinants of innovation: sensitivity analysis. Specification

I

II

III

IV

V

Dependent variable Countries Technologies Supply determinants Own stock

Weighted counts All All

Patent counts All All

Patent counts No USA All

Patent counts Top 5 All

Patent counts No top 5 All

1.030nnn (0.007) 1.148nnn (0.013)

1.036nnn (0.006) 1.095nnn (0.010)

1.019nnn (0.005) 1.119nnn (0.013)

1.076nnn (0.008) 1.069nnn (0.011)

1.014nn (0.006) 1.115nnn (0.017)

1.009nnn (0.003) 1.075nn (0.033) 1.371nnn (0.152)

1.010nnn (0.002) 1.059nn (0.029) 1.280nnn (0.118)

1.001 (0.002) 1.098nnn (0.030) 1.226nn (0.106)

1.003 (0.006) 1.031 (0.030) 2.007nnn (0.224)

1.004 (0.005) 1.018 (0.117) 0.879 (0.114)

Time FE Countryntechnology FE

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Observations Log-likelihood

4080  3312 395,068

3876  4035 288,684

3840  3424 272,336

1200  2491 19,236

2880  1762 180,259

Foreign stock Demand determinants Energy price Value added Policy index

w2 Notes: see notes to Table 6.

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domestic innovations previously exported to a foreign market, while external knowledge stocks represents other countries’ innovations exported to a foreign market. The results we present indicate therefore that knowledge spillovers have a higher impact than own stock of knowledge on those innovations that are valuable on a more global scale. In this sense, our study does shed light on properly defined domestic innovation, as we observe only a (highly valuable) subset of all the patents applied for in any given country. To test the validity of the results presented so far, Table 7 provides sensitivity analysis for previous estimations. Specification I addresses the issue of the value of patented innovation.17 Using simple patent counts to proxy for innovation and to construct the knowledge stocks implies that all patents have the same innovative content and are equally valuable. However, the distribution of patent value is highly skewed and attributing the same weight to all patents would not necessarily provide correct results. For this reason, we repeat the estimation defining PATi,s,t as the number of patents in energy technologies that received at least one citation. This approach ensures that we drop from the analysis all those patents that never served as the basis for additional innovation. Incidentally, we note that such an approach also partly corrects for the home-country bias present in the dataset, as those US patents that are not important for future innovation are in this way dropped from the analysis, since they receive no citations. The knowledge stocks are calculated accordingly. Specification I shows a coefficient for the own knowledge stock which is identical in magnitude and standard error to the one of Specification IV in Table 6. Conversely, the coefficient associated with the external knowledge stock is higher in magnitude. This indicates that not accounting for the value of patented innovation provides a correct estimate of the impact of the own knowledge stock, but underestimates the contribution of the foreign knowledge stock. These results indicate on the other hand that foreign knowledge stock has a higher impact on the production of more useful/valuable innovation. The effect of price is also higher than what previously estimated. Specification II considers the fact that, since the USA market is the natural outlet for USA innovators, the application date on US patent will (almost always) be the priority date, namely the first filing date. In contrast, most foreign innovators will have first filed an application in their home country, which most likely would happen around a year before application in the USA (and perhaps more in the case of a Patent Cooperation Treaty filing).18 For this reason, we repeat the estimation subtracting 1 to the application year of foreign patents, while leaving the application year of USA patents unchanged. Also in this case, previous results are confirmed. Specification III does not include data from the USA. Dropping observations relative to the USA allows us to fully interpret the estimated coefficients as affecting the probability of patenting an innovation in a foreign country. In this case, the estimated coefficients for the own and external knowledge stock are respectively lower and higher than previous estimates. Once again, this indicates that increases in foreign stock have a higher impact on the probability of domestic innovation than comparable increases in the own knowledge stock. As mentioned above, the external knowledge stock has a higher impact than the own knowledge stock on the probability of being a leader in a foreign innovation market. One important caveat to keep in mind is that for all countries but the USA the patents included in the analysis represent innovations targeting a foreign market. In contrast, some US innovations included may only be targeting the local market. As a result, these results indicate that foreign knowledge might be more important for global innovations. The contribution of own and foreign knowledge stocks to further innovation is not equal for top innovators and less innovative countries. Specifications IV and V show separate results for the top five innovators in our sample (USA, Japan, Germany, France and the UK) and all other countries, respectively. For top innovating countries a 10% increase in either own or foreign knowledge translates in approximately the same increase in the probability of innovation, with the own stock having a slightly higher impact (respectively, 7.6% and 6.9%). Conversely, for less innovative countries, the impact of a 10% increase in foreign knowledge stock is much higher than the impact of a 10% increase in domestic knowledge stock on the probability of innovation (respectively, 11.5% and 1.4%). The empirical results presented in this section clearly indicate that knowledge spillovers are a powerful source of inducement. Analyses not accounting for the positive inducement effect of this supply-side determinant of innovation are likely to result in omitted variable bias. This is true both for top innovators and for less innovative countries. These results also confirm the need to account for knowledge spillovers in climate economy models.

7. Conclusions This paper contributes to the induced innovation literature by extending the analysis of supply and demand determinants of innovation in energy technologies, accounting in particular for the role of international knowledge flows and knowledge spillovers. 17 Besides the sensitivity analyses presented in Table 7, we also considered the potential endogeneity of the public R&D variable. We explored a twostage procedure in which the R&D variable is instrumented. In the first stage, energy R&D is regressed on all other exogenous variable and on total government R&D as instruments. The predicted value from the R&D equation is then used as regressor in a second stage. Results are very similar to the ones presented. However, the procedure for bootstrapping the standard errors does not converge. An alternative specification with bootstrapped standard errors, but where the stock variables are not normalized, also confirms these findings. Results are available from the authors upon request. 18 The Patent Cooperation Treaty is an international patent law treaty which provides a unified procedure for filing patent applications to protect inventions in any of its contracting states.

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We have first identified and studied the channels through which knowledge flows between countries. Our empirical analysis shows that higher geographical distance is associated with a lower probability of knowledge flows. A detailed analysis of the role of distance in technological space indicates that the more similar any two given countries, the more likely the flow of knowledge between the two. We also confirm the importance of linguistic similarities and trade block relations between sending and receiving countries. Next, we built measures of internal and external available knowledge stocks and presented an empirical analysis of the supply and demand determinants of innovation. With respect to demand-side inducement effects, we show that increases in both energy prices and in the value added of each country positively affect the probability of innovation. The commitment of each state to increasing energy efficiency and providing research investment for energy-related technologies is associated with a higher probability of innovation. This paper’s main contribution is however to the crucial issue of knowledge spillovers. We first confirm previous results indicating that innovators ‘‘stand on the shoulders of domestic giants’’: an increase in the stock of domestic knowledge translates into a higher probability of innovation. In addition, we also show that the ‘‘foreign’’ giants might allow innovators to ‘‘stand taller’’. An increase in foreign knowledge stock translates in a higher probability of innovation with respect to comparable increases in own domestic stocks for the sample as a whole. However, the analysis of the inducement effect for lead innovators and less innovating countries shows that the foreign knowledge stock is important for both groups, but with different magnitudes. In the case of top innovators, the effect of the external knowledge stock is comparable to that of the own knowledge stock. In the case of less innovative countries, the effect of the external knowledge stock is much higher than that of the internal knowledge stock. In relation to the general literature on TC, the paper confirms that the ability of a country to absorb knowledge from abroad is not merely a function of geographical distance, but that technological specialization contributes a great amount to increasing absorption capacity. In relation to the literature on inducement, the results presented in this study provide some guidance to the modelling of knowledge dynamics in climate-economy models and show how including international knowledge spillovers could substantially improve the ability to mimic innovation in integrated assessment models. Of course, the analysis presented here could be fruitfully improved in a few directions. Firstly, it would be useful to relax the assumption made in the first part of the paper that the rate of knowledge diffusion between countries is time-invariant. Secondly, the availability of better proxies for the demand determinants of innovation would further strengthen the econometric results in the second part of the paper. In particular, on the one hand, reliable energy price series would allow the extension of the study to non-OECD countries. On the other hand, more satisfactory measures of effectiveness of energy and environmental policy would more effectively pin down the role of policy for innovation activity. Our current research focuses on these aspects.

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