Abstract:work is licensed under a Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (https://creativecommons. org/licenses/by-nc-nd/3.0/igo/legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than fo… Show more
“…Also, there is no evidence that obstacles affect our dependent variable. These results are similar to Mohan, Stroble, and Watson (2017) for Caribbean firms. Notes: ***, **, and * indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.…”
Section: Results Using the Balanced Panelsupporting
confidence: 83%
“…They find that financial barriers are quantitatively important, especially for firms operating in the services sector. Mohan, Stroble, and Watson (2017) measure the effects of obstacles on innovation propensity, intensity, innovation outcomes, and labor productivity using a sample of Caribbean firms. They find that cost, knowledge, market, and policy obstacles hamper engagement in innovation activities, innovation investment, and innovation outcomes.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The methodology builds largely on Crespi and Zuñiga (2012) and Mohan, Stroble, and Watson (2017). Given that our goal is to measure the effect of obstacles to innovation on innovation propensity, innovation intensity, innovation outputs, and labor productivity, we need a model that captures the complex relationships between these variables.…”
Section: Econometric Model and Empirical Implementationmentioning
This paper analyzes the effects of financial and nonfinancial obstacles to innovation on Uruguayan firms. We contribute to the literature by including the role of systemic and institutional factors affecting the different stages of the innovation process. The empirical analysis is based on four waves of national innovation surveys covering firms in the industry and services sector. In line with recent studies, we confine our analysis to the relevant sample of potentially innovative firms. Our results show that market, financial, knowledge, and context obstacles are the most important factors reducing innovation propensity and the amount invested in innovation activities. The effects are similar for firms in the industry and services sectors. We do not find evidence that institutional factors hamper innovation. Investment in equipment and investment in R&D and other intangible activities are affected differently by obstacles. On the other hand, innovation outcomes are affected mainly by financial and marketrelated barriers. We do not find evidence that obstacles to innovation have a significant impact on labor productivity.
“…Also, there is no evidence that obstacles affect our dependent variable. These results are similar to Mohan, Stroble, and Watson (2017) for Caribbean firms. Notes: ***, **, and * indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.…”
Section: Results Using the Balanced Panelsupporting
confidence: 83%
“…They find that financial barriers are quantitatively important, especially for firms operating in the services sector. Mohan, Stroble, and Watson (2017) measure the effects of obstacles on innovation propensity, intensity, innovation outcomes, and labor productivity using a sample of Caribbean firms. They find that cost, knowledge, market, and policy obstacles hamper engagement in innovation activities, innovation investment, and innovation outcomes.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The methodology builds largely on Crespi and Zuñiga (2012) and Mohan, Stroble, and Watson (2017). Given that our goal is to measure the effect of obstacles to innovation on innovation propensity, innovation intensity, innovation outputs, and labor productivity, we need a model that captures the complex relationships between these variables.…”
Section: Econometric Model and Empirical Implementationmentioning
This paper analyzes the effects of financial and nonfinancial obstacles to innovation on Uruguayan firms. We contribute to the literature by including the role of systemic and institutional factors affecting the different stages of the innovation process. The empirical analysis is based on four waves of national innovation surveys covering firms in the industry and services sector. In line with recent studies, we confine our analysis to the relevant sample of potentially innovative firms. Our results show that market, financial, knowledge, and context obstacles are the most important factors reducing innovation propensity and the amount invested in innovation activities. The effects are similar for firms in the industry and services sectors. We do not find evidence that institutional factors hamper innovation. Investment in equipment and investment in R&D and other intangible activities are affected differently by obstacles. On the other hand, innovation outcomes are affected mainly by financial and marketrelated barriers. We do not find evidence that obstacles to innovation have a significant impact on labor productivity.
“…The World Bank Enterprise Survey (WBES) and the Productivity, Technology, and Innovation Survey (PROTEqIN) attenuate part of this problem. These surveys provide firm-level data applying the same questionnaire in different LAC countries, allowing the production of new evidence on firm behavior in the region (Dohnert, Crespi, and Maffioli, 2017;Grazzi and Pietrobelli, 2016). However, since the scope of these surveys is more comprehensive than just innovation, and country sample sizes are relatively small, the usefulness to the study of innovation in firms is limited.…”
To create and promote comprehensive regional innovation policy, it is important to have valid, comparable, and standardized innovation survey data from different countries in Latin America. The Harmonized Latin American Innovation Surveys Database (LAIS) contains nearly 690 variables and 119,900 observations at the firm level. Data are from 30 national innovation surveys conducted between 2007 and 2017 in 10 Latin American countries. The dataset increases the number of countries of the region with publicly available microdata about innovation at the firm level. The corresponding IDB technical note describes how criteria were applied to identify and select variables, whose data measure the same underlying concept, from substantially diverse innovation survey methods and questionnaires used in different Latin American countries. The availability of these data will allow more scholars to research innovation in Latin American firms and address long-standing unanswered questions about the relative importance a variety of factors driving innovation decisions in Latin American firms.
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