The Productive Efficiency of Science and Technology Worldwide: A Frontier Analysis

Abstract

We are interested in how codified knowledge is produced around the globe (which inputs are used to produce scientific articles and patented inventions) and the efficiency of the process (how do the best performers produce more with the same inputs or produce the same with less inputs). Using a Data Envelopment Analysis (DEA) efficiency frontier approach, we aim to determine which countries are more efficient at producing codified knowledge. We proxy knowledge production by publications and patents, obtained through human (researchers) and non-human (R&D expenditure) resources. We built a 15-year database with more than 800 observations of these and other variables. Our findings enable us to distinguish efficiency by country, geographical region, and income area. We run four different specifications and correlate the results with partial productivity indexes seeking consistency. Under constant returns to scale, the most traditional producers of knowledge are not fully efficient. Instead, small countries with limited resources appear to be efficient. When we add environmental conditions, both sets of countries are efficient producers of knowledge outputs. High-income regions, on the one hand, and East Asia, North America, and Europe and Central Asia, on the other, are the most efficient regions at producing knowledge.

Efficiency Assessment on Codified Knowledge Products: An SFA Approach

“…The sample of Carrillo ( 2019) comprises 33 countries with significant involvement in R&D activities (above 1 percent of the World's activity), to which efficiency scores were obtained with an output-oriented VRS DEA model. Also, Ferro and Romero (2021), using a Data Envelopment Analysis (DEA) efficiency frontier approach, study which countries are more efficient at producing scientific articles and patents. They find efficient countries that are both small and not traditional knowledge producers.…”

A State-Level Analysis of Mexican Education and Its Impact on Regional, Economic, and Social Development: Two-Stage Network DEA Approach

“…Data Envelopment Analysis (DEA) is a non-parametric technique used to evaluate efficiency and productivity with a comprehensive record of successful applications in numerous sectors (Emrouznejad and Yang, 2018;Liu et al, 2013;Mahmoudi et al, 2020). For example, Avilés-Sacoto et al (2020) used DEA methodology to investigate the regional efficiency of innovation systems; Ferro and Romero (2021) constructed a DEA model to determine countries' efficiency in producing codified knowledge. Flegl and Hernández Gress (2023) applied DEA to evaluate the efficiency of public security in Mexico; Moghaddas et al (2022) assessed a resource allocation in a sustainable supply chain based on DEA modeling; Wu and Lin (2022) applied a DEA model to measure the performance of cultural tourism of several Asian tourist destinations.…”

Education Performance of Czech Public Higher Education Institutions Using Data Envelopment and Panel Regression Analysis

“…HEIs produce a highly skilled workforce, fostering productivity gains and technological advancements that stimulate economic growth. Research has consistently shown that countries with more college graduates experience higher per capita income, increased labour market participation and reduced unemployment rates (for example the publication by Ferro and Romero (2021)). The main economic factors are labour market needs, innovation and entrepreneurship, economic inequality and industry-academia collaboration.…”