Universidades têm funções semelhantes na maioria dos sistemas de inovação em países industrializados e em industrialização, porém a relevância do seu papel se altera substancialmente em cada economia. Neste trabalho, identificamos que a alocação de recursos financeiros destinados à pesquisa apresenta especial concentração em hard sciences, entretanto a alocação de recursos humano concentra-se preferencialmente nas áreas de Ciências Humanas, Sociais Aplicadas e Linguística, Letras e Artes (soft sciences ). Esta distorção entre alocação de recursos financeiros e recursos humanos afeta diretamente a capacidade inovativa do país. É possível que o Brasil não esteja formando recursos humanos suficientes para competir em mercados avançados em ciência e tecnologia. Universities have broadly similar functions in the innovation systems of most industrial and industrializing countries; however the relevance of their role alters substantially. In Brazil, universities are the most important knowledge producers. We demonstrate that even though the largest allocation on financial resources goes to 'hard sciences', there is a large allocation of human resources within humanities, applied social sciences and linguistics, arts and literature (soft sciences). This distortion in the distribution of researchers with a relative high concentration in humanities and applied social science may directly affect Brazil's capacity of innovation. Brazil may not be producing the quantity of pertinent human resources expected to compete in the world's technological advanced markets
RESUMO O que pode explicar o fato de ter havido uma entrada maciça de IDE no Brasil e ter-se mantido uma inercial estrutural na base produtiva nacional? Existe uma série de fatos macroeconômicos que podem ajudar a explicar essa inércia, como a baixa taxa de investimento e a falta de ordenação dos preços-chave da economia (como taxas de juros e taxa de câmbio), mas a proposição aqui feita é que existe um problema microeconômico estrutural relevante: falta de aptidão tecnológica das empresas industriais brasileiras. Para analisar tal proposição parte-se da separação entre os tipos de IDE (greenfield, fusão e aquisição e 'em carteira') e os tipos de canais de transferência de tecnologia (horizontal e vertical). A partir de elementos empíricos e históricos constrói-se o argumento de que a falta de aptidão tecnológica é elemento-chave no entendimento da perda de dinamismo da indústria nacional.
Tem-se falado demasiadamente sobre a distribuição espacial dopobre. Quanto mais desagregado o mapa, mais perfeita é a sua visualização, maior a evidência da heterogeneidade da pobreza e melhor o entendimento da maneira de que ela é formada e como pode ser combatida a partir de políticas públicas localmente específicas. O Ceará apresenta a pobreza distribuída de forma heterogênea por todo o território cearense (medida pelaproporção de pobres e pelo índice de pobreza humana municipal), o que é corroborado a partir dos mapas de pobreza apresentados. As hipóteses de que há clusterização da miséria no Ceará e de que alguns municípios cearenses passaram por um processo de difusão de contágios entre 1991 e 2000 sãoconfirmados para os dados fornecidos pelo IPEADATA para o período 1991- 2000. A constatação foi possível através do instrumental econométrico espacial.
Purpose
The purpose of this paper is to investigate the academic side of university–firm linkages, reporting the results of research (called the “BR Survey”, a primary database) conducted in Brazil with leaders of research groups that interacted with firms. The authors analysed the answers from 662 research groups (from both universities and research institutes) to investigate whether the intensity of private funds affects the results of the interactions. The main intent is to answer the following question: Is there a difference between funding sources and the type of results achieved by research groups when interacting with firms?
Design/methodology/approach
To verify the impact of some variables on the perception of the main results of university–firm interactions, highlighting the impact of funding sources, the authors present a Logit Model defined with binary dependent variables. The null value is categorized as a “scientific result” (new scientific discoveries and research projects; publications, theses and dissertations; human resources’ and students’ education) and the value 1 is classified as an “innovative/technological result” (new products, artefacts and processes; improvement of industrial products and processes; patents, software, design and spin-off firms).
Findings
The authors found that the modes of interaction (relationship types) and some knowledge transfer channels, besides the number of interactions with firms, have statistically significant coefficients, so their values present different impacts on the results of the interaction. The results suggest that the Brazilian innovation policy towards a more active and entrepreneurial role of universities is fostering innovative/technological results from university–firm interactions.
Originality/value
The originality of the study lies on the results found that given the fact that private funding sources do not affect the conventional mission of Brazilian universities – teaching and research – university research groups should be even more incentivized to search for private funds to carry out their research. This may be a solution to the public fund scarcity and may help in reducing the historical distance between universities and firms in Brazil.
The growing U.S. R&D internationalization has historically been concentrated in developed countries. However, in the past few decades, the internationalization has moved toward less‐developed countries (LDCs), particularly Brazil, China, and India. What location factors are making some LDCs more “inviting” for U.S. R&D offshore? To answer this first question, we constructed a panel data using secondary data from the U.S. Bureau of Economic Analysis regarding the R&D investment made by the majority‐owned foreign affiliates of U.S. parent companies in 71 countries. We then applied a Heckman two‐step correction for selection bias test. The results highlight some important differences between developed countries’ and LDCs’ attractiveness. Based on these initial results, we conducted a detailed analysis of the determinants of U.S. R&D investments in Brazil, China, and India, which revealed that China’s determinants mostly match those found in more developed countries.
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