Abstract<\/span><\/p>\n\nThe idea that demand might be a major source of innovation dates back to the contribution of
\nSchmookler(1962, 1966) and Kaldor(1966). Despite the slowdown in the study of this relation due to
\nthe critics by Mowery and Rosenberg(1979), the demand side approach has recently regained attention.
\nWith the resurrection of the demand side also the debate on the role of public demand has been re-
\nvitalized. Innovative public procurement has been increasingly considered as a form of public support
\nto private innovation activities both from innovation scholars and policy makers (Edler and Georghiou
\n2007), grounding the need for demand oriented innovation policy.
\nEconomic historians suggested an even more fundamental role for public procurement in setting the
\npace of technological change, acknowledging government demand as a crucial factor in developing some
\nof the most in uential technologies of the 20th century. In particular Ruttan (2006) and Mowery (2008,
\n2011) report how U.S. military and aerospace related procurement had a major impact for the emergence
\nand di usion of so called general purpose technologies (GPT) such as semiconductors, computing and
\nthe internet. GPTs are usually considered as engines of economic growth, fostering widespread produc-
\ntivity gains through massive relocation and reorganization of the economic activity. According to the
\nGPT literature this potential growth is achieved only if the virtuous cycle of innovation complementar-
\nities is triggered between the sector that unveils the new technology (upstream sector) and the sectors
\napplying the new technology (downstream). Technological levels of the upstream and downstream sec-
\ntors are hence strategic complements and widespread di usion stems from the coordination of beliefs
\nbetween GPT producer and application sectors (Bresnahan and Trajtenberg, 1995). Coordination fail-
\nures and larger uncertainty tied to drastic innovations may therefore provide little market incentives for
\nadoption in the downstream sectors, potentially leaving an economy locked-in on inferior technological
\ntrajectories . Bresnahan and Trajtenberg (1995) already suggested that public procurement may play a
\nvery important role to overcome this potential market failure, injecting the virtuous cycle of innovation
\ncomplementarities.<\/p>\n
\nDespite the economic historians\u2019 contributions and Bresnahan and Trajtenberg's suggestion, no em-
\npirical work has so far tried to nd evidence of the link between public procurement and technological
\ngenerality. This paper tries to ll this gap. Following the intuition provided by Bresnahan and Trajten-
\nberg (1995) and conceiving the arrival of a GPT \u00abas a process unfolding in time rather than a single
\nhomogeneous shock\u00bb (Cantner and Vannuccini, 2012), I surmise that procurement might represent one of
\nthe most important element in creating the right soil to \u00ab cultivate \u00bb a technology that may (or may not)
\nhave the potential to reach high levels of pervasiveness. I will hence hypothesize that public procurement
\ncan raise the degree of generality of upstream technologies, triggering innovation complementarities in
\nthe downstream sectors for which the market is not providing su cient incentives.<\/p>\n
To empirically test this hypothesis I make use of patent data and in particular of patent citations.
\nCitations can be considered as \u2018paper trail\u2019 of the linkages between an innovation and its technological
\n\u2018antecedents\u2019 and \u2018descendants\u2019 (Trajtenberg et al. 1997). This feature allows to: i) use patent citations
\nto identify the connection between innovations related to public procurement and their antecedents; ii)
\nmeasure generality of patents looking at the extent to which the follow-up technical advances are spread
\nacross di erent technological elds through a Generality Index (Trajtenberg et al. 1997; Hall, 2002) 1.
\nOn the basis of this 2 consideration the hypothesis stated above can be expressed in a more formal way,
\nI will in fact hypothesize that receiving a citation from a patent related to public procurement raises the
\ngenerality level of the cited patent.<\/p>\n
\nTo perform the analysis we exploit data from 3 di erent sources: i) NBER patent data project that
\ncollects data for patents granted by the USPTO in the period 1976-20062, together with citations data; ii)
\nFederal Procurement Data System (FPDS),3 which includes several pieces of information for each Federal contract
\nawarded from 2000 onwards; iii) the Compustat North America Database which gathers nancial and
\nmarket information on public companies in the U.S. .
\nI hence design a quasi experiment in which we compare the change in the generality level (measured
\nthrough the Generality Index) at two di erent points in time, 1999 and 2006, between treated and
\na control patents, whose application date falls in the period 1993-1997 and who received at least 10
\nforward citations in 2006. Public procurement is the treatment variable and, in particular, a patent is
\nput into the treatment group if it receives a citation from a patent related to public procurement in scal
\nyear 2000.
\nIn order to deem a patent as \u2018related to public procurement\u2019 I rely on 2 considerations: i) U.S.
\nlaws: the regulation states that a patent for an invention made by a contractor in the performance of
\nwork under a government contract should include a \u2018Government interest statement\u2019. ii) I Aggregate
\nprocurement contracts data for scal year 2000 at the rm level and then match them through entities
\nnames with USPTO patents whose priority date falls in year 1999-2000 (NBER- patent data project).
\nA patent is then considered as \u2018related to public procurement\u2019 if it belongs to a rm who won at least a
\nprocurement contract in scal year 2000 and if it includes the \u2018Government interest statement\u2019.
\nTaking simple di erence in averages of the change in the Generality Index among 1999 and 2006
\nbetween the treated and control group of patents would lead to biased results due to multiple endogeneity
\nissues (mainly selection bias). To mitigate the bias we hence use as control patents only patents that
\nare similar to the ones in the treated group along several dimensions (as it was done by Czarnitki et.
\nal., 2011 and Fier and Pyka 2012). I therefore adopt the conditional di erence-in-di erences approach
\n(Heckman et al., 1998). As a rst step I use propensity score matching method to tackle the selection
\non observables problem, estimating the propensity score through a probit regression on several patents\u2019
\nand assignees\u2019 characteristics. Once that the matching is implemented I use di erence-in-di erence to
\neliminate potential biases due to selection on unobservables.<\/p>\n
\nPreliminary results of the average treatment e ect provided by the CDiD approach suggest a positive
\nand signi cant impact of innovative public procurement upon the generality of a patent. In particular,
\non average receiving a citation by a patent related to public procurement raises the Generality Index
\nof a 4%, con rming the initial hypothesis. Public demand seems hence to be of crucial importance
\nin increasing the pervasiveness of a technology, calling for the need of Schumpeterian demand policies
\n(Antonelli, 2009).<\/p>","protected":false},"excerpt":{"rendered":"
Article de Emilio Raiteri. Travail qui sera pr\u00e9sent\u00e9 au sein du groupe de travail R&D et Innovation d\u00e9fense, le jeudi 13 novembre 2014. Abstract The idea that demand might be a major source of innovation dates back to the contribution of Schmookler(1962, 1966) and Kaldor(1966). Despite the slowdown in the study of this relation […]<\/p>\n","protected":false},"author":1,"featured_media":564,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[360],"tags":[],"class_list":["post-865","post","type-post","status-publish","format-standard","has-post-thumbnail","category-actualites-du-reseau"],"_links":{"self":[{"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/posts\/865","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/comments?post=865"}],"version-history":[{"count":1,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/posts\/865\/revisions"}],"predecessor-version":[{"id":1236,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/posts\/865\/revisions\/1236"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/media\/564"}],"wp:attachment":[{"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/media?parent=865"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/categories?post=865"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ecodef-ihedn.fr\/en\/wp-json\/wp\/v2\/tags?post=865"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}