Firms’ research and development (R&D) efforts are known to generate spillover effects on other firms’ outcomes, e.g., innovation and productivity. Policy recommendations that ignore spillover effects may not be optimal from a social perspective whence the importance of accounting for spillover effects. Quantifying R&D spillover effects typically requires a spatial matrix that characterises the structure of interaction between firms. In practice, the spatial matrix is often unknown due to factors that include multiplicity of forms of connectivity and unclear guidance from economic theory. Estimates can be biased if the spatial matrix is misspecified, and they can also be sensitive to the choice of spatial matrix. This dissertation develops robust techniques that estimate the spatial matrix alongside other parameters from data using a two-pronged approach: (1) model elements of the spatial matrix using spatial covariates (e.g., geographic and product market proximity) and a parameter vector of finite length and (2) estimate the spatial matrix as a set of parameters from panel data. Approaches (1) and (2) address two identification challenges - uncertainty over relevant forms of connectivity and high-dimensionality of the design matrix - in single-index models. In this three-chapter dissertation, the first approach is applied in the first and third chapters, while the second approach is applied in the third chapter. Chapter 1 proposes a parsimonious approach to estimating the spatial matrix and parameters from panel data when the spatial matrix is partly or fully unknown. By controlling for several forms of connectivity between firms, the approach is made robust to misspecification of the spatial matrix. Also, the flexibility of the approach allows data to determine the degrees of sparsity and asymmetry of the spatial matrix. The chapter establishes consistency and asymptotic normality of the MLE under conditional independence and conditional strong-mixing assumptions on the outcome variable. The empirical results confirm positive spillover and private effects of R&D on firm innovation. There is evidence of time-variation and asymmetry in the interaction structure between firms. Geographic proximity and product market proximity are confirmed as relevant forms of connectivity between firms. Moreover, connectivity between firms is not limited to often-assumed notions of proximity; it is also linked to past R&D and patenting behaviour of firms. Single-index models suffer non-identification due to rank deficiency when the design matrix is high-dimensional. Chapter 2 proposes an estimator that projects a high-dimensional parameter vector into a reduced consistently estimable one. This estimator generalises the assumption of sparsity which is required for shrinkage methods such as the Lasso, and it applies even if the high-dimensional parameter vector’s support is bounded away from zero. Monte Carlo simulations demonstrate high approximating ability, improved precision, and reduced bias of the estimator. The estimator is used to estimate the network structure between firms in order to quantify the spillover effects of R&D on productivity using panel data. The empirical results show that firms on average generate positive R&D spillovers on firm productivity. The spatial autoregressive (SAR) model has wide applicability in economics and social networks. It is used to estimate, for example, equilibrium and peer effects models. The SAR model, like other spatial econometric models, is not immune to challenges associated with misspecification or uncertainty over the spatial matrix. Chapter 3 applies the approach developed in Chapter 1 to estimate the spatial matrix in the SAR model with autoregressive disturbances in a parsimonious yet flexible way using GMM. The asymptotic properties of the GMM estimator are established, and Monte Carlo simulations show good small sample performance. / Economics

The research performed throughout this dissertation aims at implementing quantitative methods in order to assess economic and technological performances of firms, i.e. it tries to assess the impacts of the determinants of technological activity on the results of this activity. For this purpose, a representative sample of the most important R&D firms in the world is constituted. The micro-economic nature of the analysis, as well as its international dimension are two main features of this research at the empirical level. The second chapter illustrates the importance of R&D investments, patenting activities and other measures of technological activities performed by firms over the last 10 years. The third chapter describes the main features as well as the construction of the database. The raw data sample consists of comparable detailed micro-level data on 2676 large manufacturing firms from several countries. These firms have reported important R&D expenditures over the period 1980-1994. The fourth chapter explores the dynamic structure of the patent-R&D relationship by considering the number of patent applications as a function of present and lagged levels of R&D expenditures. R&D spillovers as well as technological and geographical opportunities are taken into account as additional determinants in order to explain patenting behaviours. The estimates are based on recently developed econometric techniques that deal with the discrete non-negative nature of the dependent patent variable as well as the simultaneity that can arise between the R&D decisions and patenting. The results show evidence of a rather contemporaneous impact of R&D activities on patenting. As far as R&D spillovers are concerned, these externalities have a significantly higher impact on patenting than own R&D. Furthermore, these effects appear to take more time, three years on average, to show up in patents. The fifth chapter explores the contribution of own stock of R&D capital to productivity performance of firms. To this end the usual productivity residual methodology is implemented. The empirical section presents a first set of results which replicate the analysis of previous studies and tries to assess the robustness of the findings with regard to the above issues. Then, further results, based on different sub samples of the data set, investigate to what extent the R&D contribution on productivity differs across firms of different industries and geographic areas or between small and large firms and low and high-tech firms. The last section explores more carefully the simultaneity issue. On the whole, the estimates indicate that R&D has a positive impact on productivity performances. Yet, this contribution is far from being homogeneous across the different dimensions of data or according to the various assumptions retained in the productivity model. The last empirical chapter goes deeper into the analysis of firms' productivity increases, by considering besides own R&D activities the impact of technological spillovers. The chapter begins by surveying the alternative ways proposed in the literature in order to asses the effect of R&D spillovers on productivity. The main findings reported by some studies at the micro level are then outlined. Then, the framework to formalize technological externalities and other technological determinants is exposed. This framework is based on a positioning of firms into a technological space using their patent distribution across technological fields. The question of whether the externalities generated by the technological and geographic neighbours are different on the recipient's productivity is also addressed by splitting the spillover variable into a local and national component. Then, alternative measures of technological proximity are examined. Some interesting observations emerge from the empirical results. First, the impact of spillovers on productivity increases is positive and much more important than the contribution of own R&D. Second, spillover effects are not the same according to whether they emanate from firms specialized in similar technological fields or firms more distant in the technological space. Finally, the magnitude and direction of these effects are radically different within and between the pillars of the Triad. While European firms do not appear to particularly benefit from both national and international sources of spillovers, US firms are mainly receptive to their national stock and Japanese firms take advantage from the international stock.

This thesis explores three important factors that have been central to the pursuit of economic growth, particularly in the developing and emerging economies. These are Outward Foreign Direct Investment, Reverse Technology Spillovers, and Total Factor Productivity. Chapter 2 examines whether India’s Outward Foreign Direct Investment (OFDI) pattern is consistent with Dunning’s Investment Development Path (IDP) sequence using macro data over the period 1980-2010. It tests whether the level of development - proxied by GDP per capita - is the main factor explaining OFDI, and augments the IDP by studying other major determinants such as exports, Inward FDI, human capital, and R&D using the Cointegration and Error Correction Model techniques. The results support the main proposition of the IDP, but also highlight the importance of other factors. We also find that OFDI granger-causes R&D, suggesting a possibility of reverse technology spillover. Chapter 3 analyses the ‘feedback effect’ of Foreign Direct Investment (FDI) on Total Factor Productivity (TFP) growth of emerging economies via technology spillovers across borders. We study the effect of R&D spillovers resulting from Outward FDI flows from 18 emerging economies into 34 OECD countries over the 1990-2010 period, comparing the impact with that of spillovers resulting from Inward FDI flows. The result confirms that FDI enhances productivity growth in the home country; however the impact is much larger when R&D-intensive developed countries invest in the emerging economies than the other way round. The country-specific bilateral elasticities also support this outcome. Finally, Chapter 4 studies twofold stages of OFDI – determinants and effects – at a disaggregated level, using data on OFDI undertaken by 34 countries in 10 major sectors of US during 1990-2010. The main aim of this essay is to provide micro evidence in support of outcomes of Chapter 2 & 3. The first stage concentrates on the driving forces of OFDI to understand its macroeconomic determinants, by distinguishing the factors into 3 broad categories: country specific, sector specific and time specific variables. In the second stage, we then study how the home countries benefit from the OFDI that they undertake in the US, in terms of the impact of induced reverse technology spillovers. This stage entails the creation of a foreign R&D capital term as the weighted average of R&D intensity of US with the OFDI undertaken by the home countries into US. It investigates both direct and interaction effects of such R&D spillovers on the growth of home country’s TFP. The analysis also considers a lag structure to allow for a time lag in the transfer and effect of foreign R&D capital. Results for both the stages confirm the set hypotheses.

京都大学 / 新制・課程博士 / 博士(経済学) / 甲第25079号 / 経博第686号 / 新制||経||306(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 神事 直人, 准教授 高野 久紀, 教授 森 知也 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

R&D plays a dual role: First, it generates new knowledge and second, it develops a firm's absorptive capacity. Most of the existing strategic investment game models neglect, however, the second role of R&D. The aim of this paper is to incorporate the absorptive capacity hypothesis in such a model by endogenizing the spillover. A two-stage game is established and subsequently solved, looking for the subgame perfect Nash equilibria. Considering the comparative static properties of the model as well as the simulation results, a new effect appears: The "free-rider effect" of the models with exogenous spillover, which deteriorates the higher the spillover becomes, is now counteracted by the "absorptive capacity effect". It is found that firms will invest more in R&D to strengthen absorptive capacity when the spillover parameter is higher. (author's abstract) / Series: Department of Economics Working Paper Series

The research performed throughout this dissertation aims at implementing quantitative methods in order to assess economic and technological performances of firms, i.e. it tries to assess the impacts of the determinants of technological activity on the results of this activity. For this purpose, a representative sample of the most important R&D firms in the world is constituted. The micro-economic nature of the analysis, as well as its international dimension are two main features of this research at the empirical level.<p><p>The second chapter illustrates the importance of R&D investments, patenting activities and other measures of technological activities performed by firms over the last 10 years.<p><p>The third chapter describes the main features as well as the construction of the database. The raw data sample consists of comparable detailed micro-level data on 2676 large manufacturing firms from several countries. These firms have reported important R&D expenditures over the period 1980-1994.<p><p>The fourth chapter explores the dynamic structure of the patent-R&D relationship by considering the number of patent applications as a function of present and lagged levels of R&D expenditures. R&D spillovers as well as technological and geographical opportunities are taken into account as additional determinants in order to explain patenting behaviours. The estimates are based on recently developed econometric techniques that deal with the discrete non-negative nature of the dependent patent variable as well as the simultaneity that can arise between the R&D decisions and patenting. The results show evidence of a rather contemporaneous impact of R&D activities on patenting. As far as R&D spillovers are concerned, these externalities have a significantly higher impact on patenting than own R&D. Furthermore, these effects appear to take more time, three years on average, to show up in patents.<p><p>The fifth chapter explores the contribution of own stock of R&D capital to productivity performance of firms. To this end the usual productivity residual methodology is implemented. The empirical section presents a first set of results which replicate the analysis of previous studies and tries to assess the robustness of the findings with regard to the above issues. Then, further results, based on different sub samples of the data set, investigate to what extent the R&D contribution on productivity differs across firms of different industries and geographic areas or between small and large firms and low and high-tech firms. The last section explores more carefully the simultaneity issue. On the whole, the estimates indicate that R&D has a positive impact on productivity performances. Yet, this contribution is far from being homogeneous across the different dimensions of data or according to the various assumptions retained in the productivity model.<p><p>The last empirical chapter goes deeper into the analysis of firms' productivity increases, by considering besides own R&D activities the impact of technological spillovers. The chapter begins by surveying the alternative ways proposed in the literature in order to asses the effect of R&D spillovers on productivity. The main findings reported by some studies at the micro level are then outlined. Then, the framework to formalize technological externalities and other technological determinants is exposed. This framework is based on a positioning of firms into a technological space using their patent distribution across technological fields. The question of whether the externalities generated by the technological and geographic neighbours are different on the recipient's productivity is also addressed by splitting the spillover variable into a local and national component. Then, alternative measures of technological proximity are examined. Some interesting observations emerge from the empirical results. First, the impact of spillovers on productivity increases is positive and much more important than the contribution of own R&D. Second, spillover effects are not the same according to whether they emanate from firms specialized in similar technological fields or firms more distant in the technological space. Finally, the magnitude and direction of these effects are radically different within and between the pillars of the Triad. While European firms do not appear to particularly benefit from both national and international sources of spillovers, US firms are mainly receptive to their national stock and Japanese firms take advantage from the international stock.<p> / Doctorat en sciences économiques, Orientation économie / info:eu-repo/semantics/nonPublished