Four Essays on Firm Offshoring and Innovation Behavior

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RAMADA SARASOLA, Magdalena, 2009. Four Essays on Firm Offshoring and Innovation Behavior

@phdthesis{Ramada Sarasola2009Essay-15610, title={Four Essays on Firm Offshoring and Innovation Behavior}, year={2009}, author={Ramada Sarasola, Magdalena}, address={Konstanz}, school={Universität Konstanz} }

deposit-license Ramada Sarasola, Magdalena 2009 Four Essays on Firm Offshoring and Innovation Behavior eng 2011-09-06T10:24:50Z This dissertation deals with two major topics. The first topic looks at the behavior of multinational companies (MNCs) in terms of their offshoring behavior. Chapters<br />1 and 2 are contributions to this area, focussing on the choice of entry-mode made<br />by MNCs when going offshore. The second field of interest is devoted to firm’s innovation behavior. Chapters 3 and 4 analyze those issues in the context of Uruguayan firms.<br /><br />Chapter 1 reviews and criticizes in depth the literature on foreign establishment<br />mode choices by MNCs providing a motivation to perform a robustness analysis,that is then performed in Chapter 2. Through a review of the theoretical approaches behind each study, as well as by comparing the way theoretical constructs are operationalized, Chapter 1 pools different studies that are part of empirical literature in the field of foreign entry-mode decisions into different classes, comparing their results. The literature’s main results are then compared within those classes, finding inconsistencies in the significance and sign found for almost every explanatory variable studied in the context of entry-mode decisions. An explanation on the reasons behind those inconsistencies is also provided, yielding a strong motivation for<br />pursuing model misspecification checks and robustness analysis for those variables.<br /><br />Motivated by Chapter 1, in Chapter 2 a robustness analysis is performed on the<br />variables usually found in literature to be determinants for entry-mode choices. I<br />perform a so-called Extreme Bound Analysis (EBA) to determine which of almost 60<br />explanatory variables are robust to different model specifications. I do so by looking<br />at the entire distribution of the estimator for each explanatory variable’s coefficient,<br />following the methodology introduced by Sala-i-Martin (1997a) in a multinomial logit framework. To perform this analysis I build a unique dataset, that accounts for more than 4000 offshoring events of the largest 50 Multinational Companies (MNCs) worldwide in the last 15 years. In Chapter 2 I use 640 of those events, them being entries into foreign countries done by the largest 22 financial MNCs in the last 15 years. Based on over eight million regressions, my results are able to<br />isolate 15 variables that seem robustly related to an MNC’s entry-mode decision.<br />Multinational firms’ size and its international experience increase the likelihood of Greenfield Investments (GI) over choosing Merger & Acquisitions (M&A) or any<br />type of entry-mode involving a partner. Conversely, its host-country experience as<br />well as the larger its experience differential in M&A over GI, make greenfields less<br />probable. In terms of the host-country’s characteristics, more cultural distance between<br />home and host-country, a better developed financial sector (or local credit<br />market) in the host-country, a more regulated environment for obtaining licenses and more macroeconomic sustainability increase the chances of GI, while a worse local<br />infrastructure, higher ITC costs and more difficulties in registering property and<br />employing workers decrease the odds of greenfields. Joint Ventures are more likely<br />if the MNC is larger and if the host-country has more macroeconomic sustainability<br />and/or if it has a worse investment environment. A small and expensive talent pool,<br />make Joint Ventures (JV) less likely if compared to M&A. In addition, I find that<br />the size, expensiveness and quality of the talent pool determine the likelihood of full<br />GI due to the latter’s character of being generated from scratch, rather than because<br />it is done in a full ownership mode or without interacting with another company, be<br />it a target or a partner. The same applies for a host-country’s political stability.<br />Chapter 3 is the result of joint work with Adriana Cassoni and was developed under<br />the sponsorship of the IADB, for the Innovation Network. We there analyze the<br />innovation behavior of Uruguayan firms using a microeconomic dataset, stemming<br />from Uruguayan Innovation Surveys (IS) which we matched to Uruguayan Economic<br />Activity Surveys (EAS). We there review the theoretical and applied literature on<br />the innovative behavior of firms and its impact on productivity, so as to depict the<br />setting within which the models estimated for Uruguay are specified. We describe<br />the main characteristics of Uruguayan data, thus setting a benchmark for a better<br />understanding of the descriptive analysis afterwards summarized. Finally, we estimate<br />a model using a panel approach and correcting for survey design and sample<br />bias, following in general lines the specification of Cr´epon et al. (1998), although with several modifications and making use of new innovation and output indicators<br />proposed by us. Our results depict an innovation behavior that heavily relies on<br />innovation in processes and whose main impact on firm productivity occurs through<br />the improvement of firm internal efficiency, as opposed to product innovation.<br /><br />In the context of the paper prepared for the IADB we encountered that the empirical<br />literature dealing with the innovation behavior has difficulties to empirically<br />implement the theoretical model derived by Cr´epon et al. (1998). Most variables<br />and proxies used to operationalize the mentioned model are mediocre in terms of<br />replicating the underlying theoretical concepts and are not able to capture the complexity of innovation behavior when it comes to model innovation inputs and the<br />value of the innovation output obtained. In Chapter 4 we therefore suggest and construct<br />alternative measures for capturing those dimensions. Two of our indicators<br />can be applied using standard IS data, while the remaining three indicators make use<br />of EAS data. We analyze their comparative performance in a productivity equation<br />and conclude that our specification based on value added per employee as a proxy<br />of productivity and using our indicators for innovation output value significantly<br />improves the measuring of the impact of innovation on productivity growth yielding<br />more accurate and detailed results. 2011-09-06T10:24:50Z Ramada Sarasola, Magdalena

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