DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DT DECON DOCUMENTO DE TRABAJO THE ROLE OF TECHNOLOGY EXTENSION AND TRANSFER IN FIRMS’ INNOVATION AND PRODUCTIVITY IN PERU Nº 543 Miguel Ortiz and Juan Palomino DOCUMENTO DE TRABAJO N° 543 The Role of Technology Extension and Transfer in Firms’ Innovation and Productivity in Peru Miguel Ortiz and Juan Palomino Marzo, 2025 DOCUMENTO DE TRABAJO 543 http://doi.org/10.18800/2079-8474.0543 http://doi.org/10.18800/2079-8474.0543 The Role of Technology Extension and Transfer in Firms’ Innovation and Productivity in Peru Documento de Trabajo 543 @ Miguel Ortiz and Juan Palomino Editado: © Departamento de Economía – Pontificia Universidad Católica del Perú Av. Universitaria 1801, Lima 32 – Perú. Teléfono: (51-1) 626-2000 anexos 4950 - 4951 econo@pucp.edu.pe https://departamento-economia.pucp.edu.pe/publicaciones/documentos Encargado de la Serie: Gabriel Rodríguez Departamento de Economía – Pontificia Universidad Católica del Perú gabriel.rodriguez@pucp.edu.pe Primera edición – Marzo, 2025 ISSN 2079-8474 (En línea) mailto:econo@pucp.edu.pe file:///G:/Unidades%20compartidas/DECON%20-%20JEFATURA/Mirtha/MIS%20DOCUMENTOS/Documentos%20de%20Trabajo/DT´s%202024/gabriel.rodriguez@pucp.edu.pe 1 The Role of Technology Extension and Transfer in Firms’ Innovation and Productivity in Peru* Miguel Ortiz† Juan Palomino‡ Pontificia Universidad Católica del Perú Pontificia Universidad Católica del Perú This Version: March 17, 2025 Abstract This study examines how technology extension and transfer services (TETS) drive firm-level innovation and productivity. Since research and development (R&D) investments are subject to market failure, engaging with external agents enables firms to innovate at lower risk and cost. Using data from Peru’s National Innovation Survey (ENI), we apply the Crépon, Duguet, and Mairesse (CDM) model alongside propensity score matching (PSM) to enhance the reliability of our results. Additionally, we employ the generalized propensity score (GPS) method to analyze the sensitivity of innovation and sales outcomes to varying investment levels. The findings confirm that investment in training and external R&D significantly enhances innovation, thereby boosting labor productivity. However, this relationship is nonlinear, suggesting the presence of investment thresholds required to maximize impact. JEL Classification: L25, O32, O38 Keywords: Technology Transfer, Innovation, Productivity, R&D, CDM model * This document is drawn from the Master's thesis in Economics by Miguel Angel Ortiz Chavez, submitted to the Graduate School of the Pontificia Universidad Católica del Perú (PUCP), titled Technology Extension and Transfer Services as Drivers of Innovation and Labor Productivity in Peruvian Firms. http://hdl.handle.net/20.500.12404/26628. We are grateful for the comments provided by Juan Manuel García (PUCP) and Juana Kuramoto (PUCP). Any remaining errors are the authors' sole responsibility. † Pontificia Universidad Católica del Perú. E-mail: maortiz@pucp.edu.pe, ORCID ID: https://orcid.org/0000- 0002-9172-1414. ‡ Address for Correspondence: Juan Palomino, Department of Economics, Pontificia Universidad Católica del Perú, 1801 Universitaria Avenue, Lima 32, Perú, Phone: +511-986456469, E-mail: juan.palominoh@pucp.pe. ORCID ID: https://orcid.org/0000-0003-2828-8424. http://hdl.handle.net/20.500.12404/26628 mailto:maortiz@pucp.edu.pe https://orcid.org/0000-0002-9172-1414 https://orcid.org/0000-0002-9172-1414 mailto:juan.palominoh@pucp.pe https://orcid.org/0000-0003-2828-8424 2 El Rol de la Extensión y la Transferencia Tecnológica en la Innovación y la Productividad de las Empresas en Perú* Miguel Ortiz† Juan Palomino‡ Pontificia Universidad Católica del Perú Pontificia Universidad Católica del Perú Version: Marzo 17, 2025 Resumen Este estudio examina cómo los servicios de extensión y transferencia tecnológica (SETT) impulsan la innovación y la productividad de las empresas. Dado que las inversiones en investigación y desarrollo (I+D) están sujetas a fallos de mercado, la colaboración con agentes externos permite a las empresas innovar con menor riesgo y coste. Utilizando datos de la Encuesta Nacional de Innovación (ENI) de Perú, aplicamos el modelo de Crépon, Duguet y Mairesse (CDM) junto con el emparejamiento por puntaje de propensión (PSM) para mejorar la fiabilidad de nuestros resultados. Además, empleamos el método de puntuación de propensión generalizada (GPS) para analizar la sensibilidad de los resultados de innovación y ventas a los distintos niveles de inversión. Los resultados confirman que la inversión en capacitación e I+D externa mejora significativamente la innovación, impulsando así la productividad laboral. Sin embargo, esta relación no es lineal, lo que sugiere la presencia de umbrales de inversión necesarios para maximizar el impacto. Clasificación JEL: L25, O32, O38 Palabras Clave: Transferencia Tecnológica, Innovación, Productividad, I+D, Modelo CDM * Este documento está basado en la Tesis de Maestría en Economía de Miguel Ángel Ortiz Chavez de la Escuela de Posgrado de la Pontificia Universidad Católica del Perú, titulado Los servicios de extensionismo y transferencia tecnológica como impulsores de la innovación y productividad laboral de las empresas peruanas. http://hdl.handle.net/20.500.12404/26628 Apreciamos los comentarios de Juan Manuel García (Pontificia Universidad Católica del Perú) y Juana Kuramoto (Pontificia Universidad Católica del Perú). Los errores subsistentes son responsabilidad de los autores. † Pontificia Universidad Católica del Perú. E-mail: maortiz@pucp.edu.pe, ORCID ID: https://orcid.org/0000- 0002-9172-1414. ‡ Address for Correspondence: Juan Palomino, Department of Economics, Pontificia Universidad Católica del Perú, 1801 Universitaria Avenue, Lima 32, Perú, Phone: +511-986456469, E-mail: juan.palominoh@pucp.pe. ORCID ID: https://orcid.org/0000-0003-2828-8424. http://hdl.handle.net/20.500.12404/26628 mailto:maortiz@pucp.edu.pe https://orcid.org/0000-0002-9172-1414 https://orcid.org/0000-0002-9172-1414 mailto:juan.palominoh@pucp.pe https://orcid.org/0000-0003-2828-8424 1 1. Introduction Firms that invest in innovation follow one of four strategies (Lipczynski et al., 2017). An offensive strategy seeks to lead the market by introducing recent technology, requiring substantial investment in equipment and human capital. A defensive strategy is adopted to counteract a competitor’s technological shift, ensuring survival in a changing market. An imitative strategy focuses on replicating existing innovations, typically through licensing agreements or by leveraging publicly available knowledge. A dependent strategy positions firms in a subordinate role, such as serving as suppliers or subcontractors, adopting technology provided by the contracting firm. Research and development (R&D) plays a vital role among key innovation activities. Firms typically establish dedicated R&D units with specialized laboratories, qualified personnel, and structured research programs. However, a sizable proportion of firms—particularly small and medium-sized enterprises (SMEs)—lack the financial and organizational capacity to undertake such investments. High initial costs, risk exposure, and uncertainty regarding results create significant barriers. In addition, smaller firms face structural challenges in accessing financing, developing industry linkages, and attracting specialized human capital (Shapira and Youtie, 2016). These market failures constrain the emergence of potentially innovative firms and slow the pace of industrial innovation. Innovation is a key driver of productivity growth (Syverson, 2011). One of the most widely used empirical frameworks for analyzing the relationship between R&D, innovation, and productivity is the Crépon, Duguet, and Mairesse (CDM) model (Crépon et al., 1998). This model has been extensively applied in demonstrating the positive link between innovation and productivity in advanced economies (Lööf et al., 2017). However, findings in emerging market economies (EMEs) have been mixed. While studies consistently confirm a direct relationship between innovation and productivity, the link between R&D investment and innovation is less robust (Fedyunina and Radosevic, 2022). This suggests that EME firms often engage in innovative processes through alternative investment pathways, such as collaborations with other firms or research institutions, rather than relying solely on in-house R&D. These collaborative dynamics contribute to the formation of an innovation ecosystem, where firms leverage external knowledge while developing internal competencies—an approach known as open innovation. In the era of Industry 4.0, open innovation has become one of the most viable mechanisms for fostering both knowledge generation and technology commercialization. Within this framework, technology transfer—the process by which scientific and technological discoveries are transmitted from one organization to another for further development and commercialization—plays a crucial role in enhancing firms’ absorptive capacity and strengthening innovation capabilities in EMEs (Alkhazaleh et al., 2022). Similarly, technology extension services provide direct support to firms in technological upgrading and innovation adoption, with a particular focus on SMEs (Shapira et al., 2015). These services aim to enhance firms’ competitiveness by improving business management practices, production processes, and access to specialized technological knowledge. They also provide targeted consulting services, technical assistance, workforce training, and support for the development of innovation-driven projects (De Groote, 2016). Kolodny et al. (2001) differentiate between technology transfer, defined as the commercialization of complex technologies by research laboratories and R&D centers for large industrial firms, and technology extension, which focuses on providing applied technological support to SMEs. Two core components of these mechanisms are external R&D acquisition and training services for technology adoption. Firms that engage with these services benefit from greater access to innovative technology, the introduction of 2 new products and services, and improvements in overall performance in the short and medium term (Shapira and Youtie, 2016). As a developing economy, Peru faces structural constraints that hinder technological progress and productivity growth. Its total factor productivity (TFP) represents less than 30% of that of the U.S. (Loayza, 2016) and R&D investment accounts for just 0.11% of GDP, making it one of the lowest in Latin America (CONCYTEC, 2017). These low investment levels are reflected in Peru’s performance in the Global Innovation Index (WIPO, 2021), where it ranked 70th out of 132 economies, with particularly weak results in knowledge and technology outputs (87th place). This study quantifies the impact of technology extension and transfer services (TETS)—proxied by expenditures on innovation-related training and external R&D investment—on firms’ innovation strategies and productivity performance, and seeks to answer the following hypotheses: • H1: Does the decision to invest in TETS, and the amount invested, affect firms' innovation and productivity? • H2: What are the differences and complementarities between TETS investment and internal R&D investment? By addressing these questions, this study contributes to the expanding body of research on the role of TETS in fostering innovation and enhancing productivity in EME firms. From a methodological standpoint, the study employs an adjusted CDM model, conducting additional regressions on samples obtained and weighted using Propensity Score Matching (PSM) and Generalized Propensity Score (GPS). The paper is structured as follows: Section 2 presents a review of the relevant literature; Section 3 describes the dataset and variables; Section 4 details the econometric methodology; Section 5 discusses the results and hypothesis validation; and finally, the study presents its main findings. 2. Literature Review 2.1. Relationship Between Innovation Activities, Innovation, and Productivity Investing in innovation activities aims to create or expand knowledge within an organization. Pakes and Griliches (1984) highlight that in the transition between R&D investment and its effects on innovation and productivity, an unobservable variable—knowledge capital—plays a crucial role. This variable is defined as the economic value of technological knowledge. The accumulation of knowledge depends on R&D expenditures, underlying trends, and variations in research productivity. Investment in R&D affects knowledge capital with a time lag, accumulating over previous years. In turn, knowledge capital drives innovation outcomes. Crépon et al. (1998) model the relationship between R&D, innovation, and productivity using a structural framework with three key linkages: (i) the relationship between R&D and its drivers, (ii) an innovation equation linked to R&D, and (iii) a productivity equation linked to innovation. Empirically, this is evaluated using a four-stage regression model, where the first two components relate to firms’ decisions to invest in R&D and the scale of investment. Their findings for French firms indicate that both the decision to invest in R&D and the amount invested are influenced by firm size, market diversification, market share, demand conditions, and technological opportunities. Regarding innovation, they find positive effects of R&D investment, as well as a positive relationship between innovation and productivity. 3 Lööf and Heshmati (2006) extend the CDM model in a study on Swedish firms, evaluating the robustness of the results across different estimation techniques, testing for consistency in both services and manufacturing, verifying results across alternative data sources, and addressing potential distortions from outliers. Their results confirm that all findings remain statistically significant across model specifications. Research on EMEs has yielded mixed results. Ramadani et al. (2019) apply the CDM model to Eastern European economies and find that external knowledge acquisition—used as a proxy for R&D— positively influences product innovation, which in turn enhances labor productivity. However, in a separate study on Eastern Europe, Fedyunina and Radosevic (2022) find no statistically significant relationship between R&D investment, innovation, and productivity. They argue that in EMEs, production capabilities are more critical than R&D investment and test this hypothesis using an alternative model in which the former replaces the latter. Their findings indicate that production capabilities do not significantly impact innovation but do influence productivity. They conclude that R&D investment and production capability development follow distinct processes and that in EMEs, the latter is more relevant for productivity growth. Edeh and Acevedo (2021) identify access to financing as the most significant barrier for firms in EMEs such as Nigeria. Using the CDM model, they find that R&D investment positively affects product and marketing innovation but has no significant impact on process innovation. Their results also indicate that while government funding does not significantly influence R&D investment, it does have a positive effect on productivity. In Peru, Tello (2020) examines the role of information and communication technologies (ICTs) adoption and differentiates between internal and external R&D investment. His findings indicate that decisions to invest in both types of R&D depend on firm size and whether the firm is part of a business group. The scale of investment is found to be significant for innovation outcomes, along with factors such as competitive pressure and mechanisms for protecting innovation. While productivity is positively influenced by innovation, its primary drivers are the capital-labor ratio and human capital accumulation. Internet use is also found to marginally increase the likelihood of innovation. The study concludes that firms are not fully capitalizing on the benefits of R&D and innovation activities. García (2022) analyzes manufacturing firms to assess the impact of innovation and ICT adoption on productivity, measured using the Levinsohn and Petrin approach. Fixed-effects regression results indicate a positive relationship between investment in science, technology, and innovation (STI) and TFP. However, the effects of ICT adoption remain inconclusive, with significant results observed only for certain variables, such as local network usage, software acquisition, and the implementation of computerized management systems. 2.2. Relationship Between TETS and Productivity Bell (1984) argues that technological change in firms is also driven by learning processes, which can occur internally or externally. He identifies three mechanisms through which this occurs: (i) learning-by- training, where firms acquire knowledge through employee training programs; (ii) learning-by-hiring, where firms gain knowledge by recruiting skilled personnel; and (iii) learning-by-searching, where firms engage in research activities. These mechanisms involve firms establishing linkages with other entities, including suppliers, research centers, universities, and specialized institutions. Acquiring external knowledge enhances cost efficiency and revitalizes firms’ knowledge bases (Chen et al., 2015). Jarmin (1999) evaluates a technology extension program in the U.S. using a two-stage model. In the first stage, he finds that firms with high prior growth, but low productivity are more likely to use these services. 4 The final results show positive effects on labor productivity, ranging from 3.4% to 16%. Ordowich et al. (2012) assess the same program using difference-in-differences and a lagged dependent variable model, also incorporating a firm survival model. Their results are inconclusive for the full sample, but a firm- size breakdown reveals positive effects. Finally, Lipscomb et al. (2018) conduct another evaluation of the program, incorporating updated datasets and methodological refinements. Their findings indicate that firms using these services see greater increases in sales and employment and are 18% less likely to exit the market. Chen, Vanhaverbeke, and Du (2015) examine four distinct types of external knowledge acquisition and their impact on innovation in Chinese firms. When analyzed individually, each type shows positive effects, though horizontal linkages have a weaker impact. When assessed jointly, all remain statistically significant, except for horizontal linkages. Additionally, when the model includes an interaction term for internal R&D, the impact is stronger for firms collaborating with supply chain partners or technology service providers. However, for horizontal linkages, the only significant effect appears in the interaction term, whereas the opposite is observed for science-based partnerships. Hottenrott and Lopes-Bento (2016) find a positive but nonlinear relationship between external R&D and innovation, with diminishing returns as firms increase their reliance on kind of investment. Their findings suggest that the optimal share of external R&D participation in projects is around 60%. Carboni and Medda (2021) report comparable results for Germany, France, Spain, and Italy, arguing that excessive reliance on external services can discourage firms from investing in in-house research and developing internal capabilities. However, when external R&D is sourced within a business group, it has the strongest impact on product innovation performance compared to acquiring it from universities or other firms. Workforce training programs also contribute positively to innovation. Na (2021) finds that on-the-job training and employees’ education levels enhance all types of innovation across Eastern Europe and Central Asia. This relationship is demonstrated using a two-stage Heckman regression model. 3. Research Design and Methodology 3.1. Dataset This study uses data from the National Innovation Survey (ENI) for manufacturing and knowledge- intensive services, conducted by Peru’s National Statistics Institute (INEI) in 2018. The survey covers Peruvian firms with annual sales exceeding USD 189,405 and includes 2,084 firms, selected based on two criteria: (i) a mandatory sample covering firms that account for 81% of net annual sales in their respective industries, and (ii) a randomly selected, non-mandatory sample drawn from the remaining firms. The survey collects data on firm characteristics, innovation activities, human resources, financing, obstacles, and outcomes for 2015, 2016, and 2017. 3.2. Variable Definitions TETS adoption (𝑔𝑠𝑡𝑖) is measured based on whether a firm invested in external R&D and innovation- related training in 2015 and 2016. Investment intensity (𝑟𝑠𝑡𝑖) is calculated as the logarithm of total investment over both years, divided by the number of employees. Innovation is measured using two variables. The first is a binary (dummy) variable (𝑝𝑠𝑡𝑖) that equals 1 if the firm introduced an innovation and 0 otherwise. The second is an innovation intensity variable (𝑛𝑠𝑡𝑖), defined as the share of total sales generated by innovative products. Labor productivity is captured as net sales per worker (𝑌 𝐿⁄ ), expressed in logarithms for 2017. 5 The independent variables (𝑥) follow the original CDM model and its extensions (Edeh and Acedo, 2021; Lööf et al., 2017; Lööf and Heshmati, 2006; Ramadani et al., 2019). To incorporate a localized perspective, this study also draws on Peruvian research, including Tello (2020) and García (2022). Firm size is measured by the number of employees, while human capital is defined as the percentage of employees with higher education. Market concentration is assessed using the Herfindahl-Hirschman Index (HHI). Once calculated, a categorical variable is assigned, classifying firms as unconcentrated (HHI < 0.15), moderately concentrated (0.15 ≤ HHI ≤ 0.25), or highly concentrated (HHI > 0.25) (Nguyen et al., 2020). These variables are used to test Schumpeterian hypotheses, which propose that larger firms and those operating in concentrated markets are better positioned to engage in innovation activities. The analysis also accounts for market constraints, incorporating variables related to financing constraints, risk aversion, and technological and demand-side barriers. Other firm characteristics include firm age, as well as external linkages and international market participation, measured through foreign capital ownership and export activity. For the innovation equation, additional controls include intellectual property protection, such as patents and copyrights, and participation in government programs. In the final stage, the model incorporates physical capital per worker, a key driver of productivity, alongside labor and human capital (Lööf and Heshmati, 2006; Jarmin, 1999). Following Lööf and Heshmati (2006), all monetary variables are expressed on a per-worker basis. A consumer price index (CPI) deflator is applied to adjust values to real terms over the three-year period. Across all model specifications, additional controls include firm location, particularly whether the firm operates in Lima, given the city’s concentration of TETS providers and access to transportation and communication networks. The analysis distinguishes between services and manufacturing, with further subdivisions based on technology intensity (UNIDO, 2019). All the variables mentioned in this section and used in the CDM model are described in detail in the Annex. 3.3. Methodology To test H1, the analysis applies the CDM model. The explanatory variables include the decision to use TETS and the intensity of investment, both measured as cumulative values for 2015 and 2016. By 2017, firms are expected to exhibit results in terms of innovation and productivity. Figure 1, constructed from the literature, summarizes the causal chain of how different innovation activities, both R&D and TETS, increase knowledge capital, which in turn will have an effect on innovation and business performance of firms. A key challenge of the CDM model is the potential presence of endogeneity and selection bias (Crépon et al., 1998). This issue arises when the firms using TETS are not randomly selected but rather self-select into the program. As a result, the intensity of investment is only observed for firms that actively choose to engage with TETS, leading to a latent variable problem. To address this issue, the first stage of the analysis applies a Heckman selection model, where the decision to invest is treated as a latent variable (𝑔𝑠𝑡𝑖 ∗). The observed binary outcome (𝑔𝑠𝑡𝑖) takes a value of 1 if the firm chooses to invest and 0 otherwise: 𝑔𝑠𝑡𝑖 = { 1 𝑠𝑖 𝑔𝑠𝑡𝑖 ∗ > 0 0 𝑠𝑖 𝑔𝑠𝑡𝑖 ∗ ≤ 0 (1𝑎) 6 The intensity of investment in TETS (𝑟𝑠𝑡𝑖 ∗) is also a latent variable. The observed investment level (𝑟𝑠𝑡𝑖) is only recorded if the firm has chosen to use these services: 𝑟𝑠𝑡𝑖 = { 𝑟𝑖 ∗ 𝑠𝑖 𝑔𝑠𝑡𝑖 ∗ > 0 − 𝑠𝑖 𝑔𝑠𝑡𝑖 ∗ ≤ 0 (1𝑏) Thus, both equations are related. In the classical version of the model, the latent variables are derived from the following equations: 𝑔𝑠𝑡𝑖 ∗ = 𝑥0𝑖𝑏0 + 𝑢0𝑖 (2) 𝑟𝑠𝑡𝑖 ∗ = 𝑥1𝑖𝑏1 + 𝑢1𝑖 (3) the error terms (𝑢0𝑖 and 𝑢1𝑖) are potentially correlated (𝜌) and assumed to follow a joint normal distribution. The correlation coefficient (𝜌) is crucial for determining the interdependence between Equations (2) and (3). If 𝜌 = 0, then the error terms are uncorrelated, and Ordinary Least Squares (OLS) would be a more efficient alternative. To correct for heteroskedasticity, robust standard errors are applied. As a result, the first stage produces a predicted value for investment intensity, which is used in the second stage to examine the relationship between innovation and TETS investment. product innovation (𝑝𝑠𝑡𝑖) is modeled as a binary variable that equals 1 if the firm introduced an innovation and 0 otherwise. The estimation applies a probit model with robust standard errors: 𝑝𝑠𝑡𝑖 = 𝛼𝑘𝑟𝑠𝑡𝑖 ∗ + 𝑥2𝑖𝑏2 + 𝑢2𝑖 (4𝑎) A second innovation variable, innovation intensity, is defined as the share of sales from innovative products (𝑛𝑠𝑡𝑖) and estimated using OLS regression with robust standard errors. 𝑛𝑠𝑡𝑖 = 𝛼𝑘𝑛𝑠𝑡𝑖 ∗ + 𝑥2𝑖𝑏2 + 𝑣2𝑖 (4𝑏) Both models consider innovation at all levels (internal, market, and international), following De Groote (2016), who argues that technology services, particularly TETS, primarily drive internal innovation rather than market-oriented innovation. In the third stage, the analysis examines the relationship between predicted innovation outcomes (𝑝𝑠𝑡𝑗𝑖 ∗ ) and labor productivity (𝑞). The productivity measure is based on first differences between 2015 and 2017, following Pakes and Griliches (1984), who model R&D investments as lagged and cumulative inputs. This approach is also consistent with Jarmin (1999), assuming a Cobb-Douglas production function. Δ ln 𝑞𝑖 = Δ ln 𝑌𝑖 𝐿𝑖 = ln 𝑌𝑖,2017 𝐿𝑖,2017 − ln 𝑌𝑖,2015 𝐿𝑖,2015 (5) where 𝑌𝑖 represents the net sales of firm 𝑖 and 𝐿𝑖 denotes the number of employees. First differences are also applied to physical capital per worker (𝐾𝑖), employment (𝐿𝑖), and human capital (𝐻𝑖). following Lööf and Heshmati (2006): Δ ln 𝑞𝑖 = Δ ln 𝑌𝑖 𝐿𝑖 = 𝛼𝑝𝑠𝑡𝑖 ∗ + 𝛿Δ ln ( 𝐾𝑖 𝐿𝑖 ) + 𝜃Δ ln(𝐿𝑖) + 𝜋Δ(𝐻𝑖) + 𝑥3𝑖𝑏3 + 𝑢3𝑖 (6𝑎) Δ ln 𝑞𝑖 = Δ ln 𝑌𝑖 𝐿𝑖 = 𝛼𝑛𝑠𝑡𝑖 ∗ + 𝛿Δ ln ( 𝐾𝑖 𝐿𝑖 ) + 𝜃Δ ln(𝐿𝑖) + 𝜋Δ(𝐻𝑖) + 𝑥3𝑖𝑏3 + 𝑢3𝑖 (6𝑏) To assess the robustness of the results, the model is replicated on a subsample consisting of firms that used TETS, compared to a control group of non-users with similar observable characteristics. The matching procedure applies PSM, which estimates the probability of using TETS based on observable firm attributes. This method is commonly used to approximate causal inference, as applied in Gallego 7 and Gutiérrez (2017) to evaluate the impact of quality certification on labor productivity, and the results are «as good as randomization» (Heinrich et al., 2010). The propensity score (𝑃(𝑥0)) is estimated as the probability of using TETS (𝑑), conditional on observable characteristics (𝑥0) (Becker and Ichino, 2002). Next, a balance test is performed to ensure that variable differences are not statistically significant within each distribution block. 𝑃(𝑥0) = Pr(𝑑 = 1|𝑥0) (6) Firms are matched using Nearest-Neighbor Matching (NNM), selecting a set of control firms (𝑐(𝑖)) with the closest propensity scores within a pre-defined threshold (𝑘). Each treated firm is matched with four control firms, using weighting 𝜔1(𝑖, 𝑗) to construct the comparison group: 𝑐(𝑖) = {𝑗 ∈ 𝐷 = 0 | ‖𝑃𝑖(𝑥0) − 𝑃𝑗(𝑥0)‖ ≤ 𝑘} (7) Additionally, Kernel Matching is applied to assign weighted averages to both treated and control firms, minimizing sample loss. The weighting function follows: 𝜔2(𝑖, 𝑗) = 𝐾 ( 𝑃𝑗(𝑋) − 𝑃𝑖(𝑋) 𝑎𝑛 ) ∑ 𝐾 ( 𝑃𝑗(𝑋) − 𝑃𝑖(𝑋) 𝑎𝑛 )𝑘𝜖𝐶 (8) where 𝜔2(𝑖, 𝑗) represents the weight and K(.) is the Kernel function. Once the subsample is defined, the CDM model is replicated, applying the associated weights (𝜔2(𝑖, 𝑗)) to each firm identified as a counterfactual, and the results are compared. To test H2, the CDM model is compared to an alternative specification incorporating internal R&D (𝑔𝑖𝑑𝑖) in Equation (2). Subsequently, Equations (3) to (6) are replicated. Internal R&D expenditure is used to measure investment intensity (𝑟𝑖𝑑). In the second and third stages, the predicted variables for expenditure intensity (𝑝𝑖𝑑𝑖) and innovation (𝐼𝑁𝑁𝑗𝑖 ∗) are included. To assess the cost-effectiveness of investing in R&D versus ETTS, the dose-response function is estimated using the GPS method. This technique evaluates the impact of a treatment when it is continuous rather than binary or discrete, allowing for an analysis of treatment intensity (Bia and Mattei, 2008). The core idea is to obtain a potential outcome (𝑌) for each potential treatment level (𝑡). Thus, the set of potential outcomes for a range of treatments (𝜏) is defined as {𝑌𝑖(𝑡)}𝑡∈𝜏. This methodology consists of three steps: • In the first stage, the GPS score 𝑅 = 𝑟(𝑇, 𝑋) is estimated as the conditional density of the treatment level 𝑇 given the covariates 𝑋. • In the second stage, the expected conditional outcome parameter is obtained: 𝛽(𝑡, 𝑥) = 𝐸(𝑌|𝑇 = 𝑡, 𝑅 = 𝑟). • In the third stage, the dose-response function is estimated as 𝜇(𝑡) = 𝐸[𝛽(𝑡, 𝑟(𝑡, 𝑋))], 𝑡 ∈ 𝜏 The goal is to estimate the percentage change in sales for each percentage change in R&D or ETTS expenditure. Confidence intervals are constructed using Bootstrap. Dai and Cheng (2015) apply this methodology to investigate the impact of public subsidies on R&D investment in Chinese manufacturing firms. 8 4. Results 4.1. Descriptive Statistics Table 1 shows the main descriptive statistics of the variables used in the models. Between 2015 and 2016, 12.7% of firms used TETS. A total of 30% introduced a product innovation, with sales from innovative products averaging 14.3% of total sales. Sales per worker—in Peruvian soles (PEN)—declined, from PEN 481,000 in 2015 to PEN 444,000 in 2017. On average, firms had 18.2 years of operation and employed 227 workers. Innovation-related linkages were weak, with the highest being firm-to-firm collaborations (4%). Most firms operated in diversified markets (34% in highly competitive industries and 53% in unconcentrated industries) with low market shares. Physical capital per worker increased by 56%. 4.2. Decision to Invest in TETS and Investment Intensity The first stage of the CDM model examines firms’ decision to use TETS and investment intensity. The estimated rho (�̂�) is statistically significant, indicating selection bias, which has been addressed. The results are shown in Table 2, the third column corresponds to the results of the decision to invest in TETS, while the second one refers to the intensity of spending, both cases referring to the total sample. Columns 4 to 7 repeat both regressions for the samples using the Kernel and Nearest Neighbor matching methods. The number of employees has a positive effect on the decision to invest but a negative effect on investment intensity, consistent with findings for Peru (García, 2022; Tello, 2020). Market concentration is not significant in either stage, except for moderate concentration, which is significant for investment decisions. Firms may pursue these investments for various reasons (Lipczynski et al., 2017): while large firms have greater resources, they may also face bureaucratic barriers or exhibit a complacent attitude. In contrast, firms in competitive markets may invest in TETS to enhance their competitiveness. The share of qualified workers has a positive effect, in line with Lööf and Heshmati (2006) and Ramadani et al. (2019). Technological barriers influence investment decisions, as firms rely on external sources to acquire unavailable technologies. Market constraints, particularly demand-side challenges, may also encourage firms to innovate—especially those following a defensive R&D strategy (Lipczynski et al., 2017). For investment intensity, firm age exhibits a quadratic relationship: positive in early years but declining over time. TETS programs typically target smaller firms and focus on transferring existing technology. Initially, these services are essential, but as firms establish themselves and develop internal expertise, their reliance on TETS declines. Linkages with other entities are not significant, although belonging to a business group has a positive effect. This supports Díaz and Kuramoto (2010), who note that Peru’s National Innovation System remains underdeveloped, with weak coordination between industry and scientific institutions. For the matched sample, all variables in the first stage of the model are not significant, as expected, since they were used for matching. However, �̂� still indicates selection bias. For investment intensity, coefficients for firm age, employment, and share of qualified workers are similar in sign and magnitude to those in the full sample, making them the only statistically significant variables. Physical capital per worker is not significant in this case. 4.3. Innovation Outcomes 9 Table 3 shows the results of the innovation equation (Eq. 4). The analysis considers two innovation variables: product innovation and the share of total sales from innovative products. In both cases, the predicted TETS investment intensity variable is positive and significant. Similar findings are reported by Ramadani et al. (2019) and Martin and Nguyen-Thi (2015), who use external knowledge acquisition as a proxy for innovation activities, as well as by Na (2021) in the context of workforce training. Some variables are also positively significant, including the number of employees, consistent with other research (Edeh and Acedo, 2021; Ramadani et al., 2019); software development/acquisition, a proxy for ICT use, which plays a key role in innovation processes, (Martin and Nguyen-Thi, 2015) intellectual property protection (Ramadani et al., 2019); and SME status. Regarding the latter, smaller firms may find it easier to introduce new products more quickly due to fewer bureaucratic barriers (Lipczynski et al., 2017). Additionally, in Peru, SMEs benefit from more flexible labor and tax regulations and are prioritized in public R&D&I subsidy programs. For product innovation, demand-side constraints prove relevant, suggesting that firms innovate as a defensive strategy in response to market restrictions. Access to government R&D&I programs also has a positive effect, aligning with Edeh and Acedo (2021). The coefficient for the main product’s sales share is negative, indicating that more diversified firms are more likely to engage in innovation. However, this variable is not significant for the second innovation measure, implying that while it affects innovation achievement, it does not necessarily translate into commercial success. For the matched sample, product innovation coefficients remain significant and similar in magnitude. Other variables show consistent patterns, except for software development, access to R&D&I programs, and the main product’s sales ratio. Regarding the share of sales from innovative products, TETS investment intensity remains significant, but some discrepancies arise across matching methods. Under NNM, intellectual property protection, foreign capital participation, and software acquisition are not significant. In contrast, under Kernel Matching, risk perception is not significant, whereas software development and foreign capital participation are. 4.4. Productivity Outcomes The primary finding is the positive significance of predicted innovation, a result consistent with Lööf and Heshmati (2006), who also measure differences in sales per worker. Jarmin (1999) and Lipscomb et al. (2018) directly assess the impact of technology extension programs on productivity and find positive effects. As shown in columns 2 and 3 of Table 4 Regarding production factors, physical capital exhibits a positive and significant effect, aligning with previous literature. However, labor is significant but negatively correlated—a finding similar to Edeh and Acedo (2021) for process and marketing innovation. Crépon et al (1998), Lööf and Heshmati (2006), and Tello (2020) do not report significance for this variable. The share of employees with higher education is only significant at the 10% level. Financial constraints also emerge as a relevant factor. For the matched sample (columns 3 to 6 of Table 4), innovation variables remain significant only for the sales share measure, with coefficients similar to those found in the full sample. Among production factors, capital and the number of employees remain significant, whereas skilled labor is not. The country's high occupational mismatch may explain these results. Compared to the previous stage, the matched sample shows a better model fit, particularly under NNM. 4.5. Comparison Between Models Using TETS and Internal R&D 10 An additional regression was conducted for firms investing in internal R&D. The decision to invest in internal R&D aligns with the decision to invest in TETS in terms of the relevance of the number of employees, skilled labor, export status, physical capital investment, and market-related constraints regarding technology access and demand, consistent with the literature. Notably, membership in a business group and firm diversification play a significant role, suggesting that investment is driven by market expansion, aligning with an offensive strategy. Regarding investment intensity, some differences emerge. Firm age, the number of employees, and market concentration lose significance. Meanwhile, skilled labor and capital remain significant and positive. However, firm linkages become significant but negative, indicating that inter-firm collaboration primarily involves services for ready-to-use technologies, where knowledge transfer follows a one-way flow from provider to client (Carboni and Medda, 2021), reducing the need for an internal R&D process. In the second stage, as with TETS, predicted expenditure remains significant for innovation, though with smaller marginal effects. Most variables maintain their sign and significance, except for employment in the sales ratio model and foreign capital participation in product innovation. Regarding the impact on performance, the coefficients for predicted innovation remain significant for both internal R&D and TETS. The coefficients for other variables are also similar, suggesting that both serve as strong predictors of innovation, in line with Ramadani et al. (2019). However, on average, firms invest more than twice as much per worker in internal R&D compared to TETS. The dose-response function, estimated using the GPS method, quantifies the marginal effect of both types of investment on innovation and labor productivity outcomes (See Figure 2). Examining the relationship between the probability of product innovation and investment in TETS and internal R&D, the findings first highlight a rapid increase in innovation likelihood at low levels of TETS investment, followed by a gradual decline, plateauing around PEN 8,000 per worker, after which it begins to decrease. This pattern reflects the quadratic relationship in external knowledge acquisition documented by previous studies (Carboni and Medda, 2021; Hottenrott and and Lopes-Bento, 2016). Excessive investment in TETS may become counterproductive by increasing coordination, management, and oversight costs while potentially constraining a firm's ability to develop internal knowledge. In contrast, internal R&D— although showing a lower slope in certain segments—maintains a consistently upward trend. The effect of investment on sales growth also reveals a positive relationship between TETS spending and increased sales, but only at moderate investment levels. Similar to the innovation probability curve, the effect diminishes around PEN 8,000 per worker. For internal R&D, however, once this investment threshold is exceeded, the sales impact accelerates significantly. 5. Conclusions Innovation is a key driver of firm productivity. However, investing in internal R&D can be prohibitively expensive due to the substantial financial commitment and associated risks. As a result, many firms choose to acquire knowledge externally, which allows them to share risk with other actors, foster technology transfer, and enhance their commercial and organizational capabilities. In EMEs, external knowledge acquisition also facilitates access to proven technologies in a shorter timeframe. The decision to invest in TETS is influenced by firm size, human capital, and market constraints. This finding suggests that firms adopt a defensive innovation strategy to maintain competitiveness. In contrast, investing in internal R&D is associated with factors such as market structure, business linkages, and product diversification—suggesting an offensive innovation strategy aimed at market expansion. 11 Regarding TETS investment intensity, firm age plays a relevant role, though the relationship is nonlinear. Initially, firms that invest in TETS benefit from greater experience, improved internal capabilities, and enhanced access to external knowledge providers (Chen et al., 2015). However, as firms gain expertise, they may transition away from TETS, opting instead to develop in-house knowledge. Firm linkages were not found to be significant in explaining TETS investment intensity, possibly due to the weak integration and coordination within the National Innovation System (Díaz and Kuramoto, 2010). This underutilization of collaborative innovation opportunities could be a missed opportunity, particularly in the context of Industry 4.0, where rapidly evolving market needs demand open innovation strategies (Alkhazaleh et al., 2022). The results confirm that TETS investment significantly contributes to innovation, even when controlling for selection bias. Other variables positively associated with innovation include firm size, intellectual property expenditure, and R&D investment. Additionally, the inverse relationship between risk perception and export status with innovation suggests that firms with lower risk tolerance may be more cautious in pursuing innovation. Innovation was also found to have a significant impact on sales performance. However, in the matched sample, the only significant effect was observed for the share of sales from innovative products. This implies that while the direct relationship between innovation and labor productivity remains unclear, successful innovations—measured through their contribution to sales—are clearly linked to better firm performance. In this regard, the sales share of innovative products is widely recognized as a key indicator of innovation success (Carboni and Medda, 2021). The dose-response function indicates that TETS investments yield positive effects at moderate levels; however, as spending increases, the growth rate turns negative. This suggests a quadratic relationship between external knowledge acquisition and firm performance. This pattern does not hold for internal R&D, where the trend remains consistently positive. TETS investment represents a viable strategy for firms facing multiple constraints in accumulating and transforming knowledge into innovation. This is particularly relevant for younger firms seeking to invest cautiously and at lower risk. More broadly, investment in innovation activities emerges as a viable strategy for enhancing firm performance. In the short term, both TETS and internal R&D yield similar results, with both serving as strong predictors of innovation. This allows firms to pursue innovation strategies without bearing the full risks associated with in-house knowledge development. Finally, this study has certain limitations. The analysis is constrained to a three-year period due to the scope of available data in the ENI, preventing an assessment of long-term effects. 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Descriptive statistics of the variables Variable Mean Standard Deviation Minimun Maximun Dependent variables Invested in TETS 2015-2016 0.13 0.333 0 1 Invested in internal R&D 2015-2016 0.12 0.323 0 1 Product innovation 0.3 0.46 0 1 Percentage of sales from innovative products 2017 14.28 30.394 0 100 Difference in sales per worker -0.09 0.568 -5 3.3 Independent variables Firm age 18.23 14.761 1 108 Foreign capital 0.16 0.363 0 1 Employees in 2015 227.65 740.773 1 16578 Exporter in 2015 0.37 0.482 0 1 Qualified workers in 2015 0.26 0.257 0 1 Financial constraints 0.46 0.498 0 1 Linkage (companies) 0.04 0.191 0 1 Linkage (consulting firms) 0.02 0.135 0 1 Linkage (institutes and universities) 0.02 0.123 0 1 Market share 0.02 0.087 0 1 Technology obstacles 0.32 0.466 0 1 Demand obstacles 0.59 0.492 0 1 Risk perception 0.43 0.495 0 1 Accessed R&D&I program 0.03 0.169 0 1 Concentration (highly competitive) 0.34 0.475 0 1 Concentration (not concentrated) 0.53 0.499 0 1 Concentration (moderate) 0.11 0.308 0 1 Manufacturing/low technology 0.44 0.496 0 1 Manufacturing/medium technology 0.14 0.346 0 1 Manufacturing/medium-high technology 0.13 0.337 0 1 Knowledge-intensive services (KIS) 0.29 0.454 0 1 Business group 0.18 0.386 0 1 Main product ratio 2015 80.38 25.894 0 100 Acquired or developed software 0.13 0.334 0 1 Invested in R&D in 2015 0.1 0.304 0 1 SME in 2015 0.36 0.479 0 1 Intellectual property 0.04 0.201 0 1 Difference in capital per worker 0.56 4.446 -16.2 17.3 Difference in workers 0.04 0.378 -4.1 3.4 Difference in % of skills 0.01 0.068 -0.4 0.8 Note: Statistics based on the sample of 2,032 companies Source: National Innovation Survey (NIS) 2018. 16 Table 2. Estimation of the first stage, decision and intensity of spending on technology extension and transfer services Variables Total sample Matched sample (NNM) Matched sample (Kernel) log(spending on TETS) decision to invest in TETS log(spending on TETS) decision to invest in TETS log(spending on TETS) decision to invest in TETS log(firm age) 7.729*** (2.723) 0.065 (0.051) 7.416*** (2.753) 0.042 (0.075) 7.087** (2.808) 0.0154 (0.683) log(firm age)2 -3.913*** (1.416) -3.787*** (1.434) -3.623** (1.461) log(employees 2015) -0.365*** (0.107) 0.096*** (0.028) -0.471*** (0.105) 0.053 (0.044) -0.457*** (0.104) 0.062 (0.041) linkage (companies) -0.014 (0.290) -0.018 (0.296) -0.021 (0.295) linkage (consulting firms) 0.375 (0.458) 0.384 (0.453) 0.337 (0.454) linkage (institutes and universities) 0.448 (0.549) 0.447 (0.529) 0.434 (0.530) linkage (innovation centers and government) 0.419 (0.573) 0.538 (0.599) 0.548 (0.598) qualified workers 2.710*** (0.702) 0.477*** (0.173) 1.792*** (0.657) -0.134 (0.267) 1.952*** (0.673) 0.009 (0.261) exporting company 2015 0.197 (0.307) 0.205** (0.085) -0.318 (0.285) -0.142 (0.126) -0.333 (0.278) -0.151 (0.115) foreign capital 0.418 (0.358) 0.116 (0.101) 0.261 (0.340) 0.0541 (0.149) 0.228 (0.333) 0.0383 (0.139) not concentrated -0.515* (0.291) -0.112 (0.104) -0.314 (0.286) -0.009 (0.142) -0.376 (0.274) -0.058 (0.128) moderate concentration 0.797** (0.406) 0.032 (0.139) 0.811** (0.401) 0.021 (0.188) 0.826** (0.390) 0.038 (0.171) high concentration -0.393 (0.722) -0.127 (0.249) -0.218 (0.739) -0.158 (0.341) -0.205 (0.717) -0.105 (0.322) main product ratio 2015 -0.004 (0.005) -0.002 (0.001) -0.002 (0.005) -0.001 (0.002) -0.002 (0.004) -0.001 (0.002) government funding 0.152 (0.560) 0.095 (0.563) 0.099 (0.565) financial constraints 0.195 (0.260) 0.064 (0.086) 0.085 (0.257) 0.023 (0.118) 0.071 (0.253) -0.013 (0.110) risk perception 0.231 (0.291) 0.0458 (0.085) 0.065 (0.286) -0.087 (0.121) 0.117 (0.281) -0.062 (0.112) log(capital 2015) 0.063* (0.033) 0.029*** (0.009) 0.023 (0.032) 0.010 (0.014) 0.020 (0.031) 0.007 (0.012) technology obstacles 0.258 (0.300) 0.208** (0.086) 0.098 (0.293) 0.164 (0.124) 0.064 (0.285) 0.117 (0.115) demand obstacles 0.244 (0.310) 0.185** (0.090) -0.112 (0.312) -0.018 (0.133) -0.074 (0.304) 0.026 (0.122) business group 0.773*** (0.278) 0.118 (0.093) 0.454 (0.279) -0.083 (0.134) 0.466* (0.274) -0.109 (0.125) �̂� 1.358*** (0.216) 1.178*** (0.230) 1.165*** (0.228) Constant 2.702** (1.306) -2.017*** (0.248) 6.207*** (0.964) -0.198 (0.372) 6.143*** (0.952) -0.162 (0.345) Observations 2,032 2,032 781 781 1,856 1,856 Note: Controlled for industry type and geographic factors. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: National Innovation Survey (NIS) 2018 17 Table 3. Estimation of the second stage, innovation outcomes Variables Total sample Matched sample (NNM) Matched sample (Kernel) log(spending on TETS) decision to invest in TETS log(spending on TETS) decision to invest in TETS log(spending on TETS) decision to invest in TETS predicted spending on SETT 0.036*** (0.012) 2.208*** (0.781) 0.123*** (0.039) 7.384** (3.905) 0.101*** (0.034) 6.880** (2.854) log(employees 2015) 0.033*** (0.009) 1.624** (0.648) 0.086*** (0.026) 6.162*** (2.110) 0.071*** (0.023) 4.999*** (1.894) technology obstacles 0.004 (0.022) 2.005 (1.560) 0.042 (0.044) 6.142* (3.634) 0.035 (0.040) 6.225* (3.429) demand obstacles 0.069*** (0.022) 1.930 (1.545) 0.127*** (0.046) 2.558 (3.781) 0.115*** (0.041) 2.265 (3.540) foreign capital -0.043 (0.029) -4.027** (1.971) -0.068 (0.057) -6.252 (4.350) -0.064 (0.051) -6.649* (3.994) risk perception -0.040* (0.021) -2.748* (1.480) -0.082* (0.043) -6.161* (3.574) -0.067* (0.039) -5.185 (3.359) not concentrated 0.003 (0.026) 0.071 (1.824) -0.011 (0.054) -0.363 (4.818) -0.009 (0.049) 0.541 (4.426) moderate concentration 0.004 (0.033) 0.875 (0.646) -0.082 (0.077) -4.664 (6.835) -0.041 (0.067) -3.147 (6.288) high concentration 0.074 (0.071) 4.700 (5.431) 0.086 (0.110) 0.581 (11.190) 0.139 (0.091) 4.876 (10.730) acquired or developed software 0.173*** (0.027) 14.660*** (2.657) 0.040 (0.044) 5.541 (3.728) 0.033 (0.040) 6.520* (3.569) accessed R&D&I program 0.216*** (0.064) 6.903 (4.950) 0.057 (0.088) -4.574 (6.447) 0.049 (0.082) -4.151 (6.618) invested in R&D in 2015 0.284*** (0.031) 13.74*** (3.025) 0.167*** (0.045) 6.010 (3.843) 0.163*** (0.043) 6.121 (3.755) main product ratio 2015 -0.001*** (0.000) -0.043 (0.028) -0.001 (0.001) -0.030 (0.063) -0.001 (0.001) -0.024 (0.062) SME 0.079*** (0.027) 5.021** (2.049) 0.171*** (0.058) 17.740*** (5.331) 0.126** (0.051) 12.830*** (4.951) intellectual property 0.245*** (0.057) 7.759* (4.468) 0.124* (0.071) -2.576 (4.875) 0.180*** (0.064) 1.037 (4.810) Constant 2.575 (5.336) -29.150 (23.790) -21.170 (21.940) Observations 2,032 2,032 781 781 1,856 1,856 𝑅2 0.161 0.091 0.103 0.070 0.102 0.058 Notes: Controlled for industry type and geographic factors. The probit model shows marginal effects and MacFadden 𝑅2. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: National Innovation Survey (NIS) 2018 18 Table 4. Estimation of Firm Performance Variables Total sample Matched sample (NNM) Matched sample (Kernel) log(spending on TETS) decision to invest in TETS log(spending on TETS) decision to invest in TETS log(spending on TETS) decision to invest in TETS Predicted innovation 0.222*** (0.052) 0.005*** (0.001) 0.086 (0.116) 0.004* (0.002) 0.088 (0.105) 0.004** (0.002) Difference in log capital per worker 0.015*** (0.003) 0.015*** (0.004) 0.016*** (0.006) 0.016*** (0.006) 0.016*** (0.005) 0.016*** (0.005) Difference in log of labor -0.427*** (0.092) -0.428*** (0.091) -0.538*** (0.072) -0.537*** (0.071) -0.525*** (0.070) -0.527*** (0.068) Difference in % of qualified workers 0.437* (0.264) 0.444* (0.264) 0.179 (0.422) 0.190 (0.418) 0.359 (0.363) 0.369 (0.360) Log seniority -0.026 (0.018) -0.026 (0.018) 0.002 (0.025) 0.005 (0.025) -0.015 (0.025) -0.013 (0.025) Financial constraints -0.042* (0.024) -0.044* (0.024) -0.085** (0.036) -0.097*** (0.036) -0.064* (0.036) -0.075** (0.036) Constant -0.090 (0.069) -0.129* (0.071) -0.067 (0.129) -0.197 (0.162) -0.033 (0.115) -0.163 (0.145) Observations 2,032 2,032 781 781 1,856 1,856 𝑅2 0.104 0.104 0.164 0.169 0.153 0.157 Notes: Controlled by industry type and geographic factors. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: NIS 2018 19 Figure 1. Process of R&D, TETS and innovation Own elaboration based on Bell (1984) and Crépon et al. (1998) 20 (a) (b) (c) (d) Figure 2. Dose-Response Function of Expenditure on TETS and Internal R&D Source: NIS 2018 21 Annex: Variables Used in the CDM Models Variable Name Type Definition Dependent Variables Decision to invest in TETS st1516 Qualitative (Dummy) 1 if the company invested in TETS, 0 if it did not. For the years 2015 and 2016. Investment in TETS per worker lstrab_1516 Quantitative Logarithm of the amount of investment in TETS per worker. Product innovation results inn_prod Qualitative (Dummy) 1 if the company carried out product innovations, 0 if it did not. Percentage of innovations on sales pvent_inn Quantitative Percentage of sales corresponding to innovations in 2017. Sales per worker lventrab_17 Quantitative Logarithm of net sales per worker in the year 2017. Difference in sales per worker d_lventast Quantitative Difference in the logarithm of net sales between 2015 and 2017. Explanatory Variables Market Participation Market share cuota Quantitative Ratio between the company's total sales and the total sales of the sector by macro-region. Market concentration nivhhi Qualitative 1 for diversified industry; 2 for moderate concentration; 3 for concentrated industry. Based on Herfindahl-Hirschman Index (HHI) values. Diversification princ Quantitative Percentage of sales from the main product relative to total sales in 2015. Experience lantig Quantitative Years of company operation from its start until the survey date. Firm Size Firm size sme_15 Qualitative 1 for small and medium-sized enterprises; 0 for large enterprises. Workers lemp_15 Quantitative Logarithm of the number of workers in 2015. Sectoral Effects Economic sector act_econ Qualitative 1 for low-tech industry; 2 for medium-tech industry; 3 for medium-high-tech industry; 4 for knowledge-intensive services (According to UNIDO classification). Region lima Qualitative 1 for Lima and Callao; 0 for other regions. Demand Pull Demand pull dempull Qualitative (Dummy) 1 for medium to high obstacles due to uncertainty regarding demand for innovative goods and services, markets dominated by established firms, and small market size; 0 otherwise. 22 Variable Name Type Definition Risk aversion riesgo Qualitative (Dummy) 1 for perception of economic risk; 0 otherwise. Participation in international markets emp_exp15 Qualitative (Dummy) 1 if the company exported during the analysis period; 0 otherwise. Sectoral Technological Level Intellectual property propint Qualitative 1 if the company invested in intellectual property; 0 otherwise. Participation in STI support programs programa Qualitative (Dummy) 1 if the company participated in any program; 0 otherwise. Financing obstacles rfin Qualitative (Dummy) 1 if the company reported obstacles to financing innovation activities; 0 otherwise. Technology push tecpush Qualitative (Dummy) 1 if the company faced limited technological opportunities; 0 otherwise. Foreign capital kext_15 Qualitative (Dummy) 1 if the company received foreign capital in 2015; 0 otherwise. Active linkages vinc* Qualitative 1 with related companies; 2 with specialized firms; 3 with research centers and universities; 4 with innovation centers and government. Physical Capital Intensity Capital investment lcaptrab_15 Quantitative Logarithm of capital investment in 2015. Variation in capital investment dlcap_1517 Quantitative Difference in the logarithm of capital investment between 2015 and 2017. Workforce Quality Skilled personnel skill_15 Quantitative Percentage of workers with higher education in 2015. ÚLTIMAS PUBLICACIONES DE LOS PROFESORES DEL DEPARTAMENTO DE ECONOMÍA  Libros Jorge Rojas 2024 Lecciones de economía internacional: teoría pura. Lima, Fondo Editorial PUCP Gonzalo Ruiz Díaz y Sergio Sifuentes Castañeda 2024 Análisis de impacto regulatorio, ensayos reunidos. Lima, Fondo Editorial PUCP Alan Fairlie Reinoso y Ariana Figueroa 2024 Programas de posgrado en crecimiento verde y desarrollo sostenible en América Latina: una aproximación comparativa. Lima, INTE PUCP. Félix Jiménez 2024 La economía peruana del periodo 1950-2020. Lima, Fondo Editorial PUCP. Roxana Barrantes y José I. Távara (editores) 2023 Perspectivas sobre desarrollo y territorio en el nuevo contexto. Homenaje a Efraín Gonzales de Olarte. Lima, Fondo Editorial PUCP. Efraín Gonzales de Olarte 2023 La descentralización pasmada. Desconcentración y desarrollo regional en el Perú 2003-2020. Lima, Fondo Editorial PUCP. Adolfo Figueroa 2023 The Quality of Society, Volume III. Essays on the Unified Theory of Capitalism. New York, Palgrave Macmillan Efraín Gonzales de Olarte 2023 El modelo de Washington, el neoliberalismo y el desarrollo económico. El caso peruano 1990-2020. Lima, Fondo Editorial PUCP. Máximo Vega Centeno. 2023 Perú: desarrollo, naturaleza y urgencias Una mirada desde la economía y el desarrollo humano. Lima, Fondo Editorial PUCP. Waldo Mendoza 2023 Constitución y crecimiento económico: Perú 1993-2021. Lima, Fondo Editorial PUCP. Oscar Dancourt y Waldo Mendoza (Eds.) 2023 Ensayos macroeconómicos en honor a Félix Jiménez. Lima, Fondo Editorial PUCP. Carlos Contreras Carranza (ed.) 2022 Historia económica del Perú central. Ventajas y desafíos de estar cerca de la capital. Lima, Banco Central de Reserva del Perú e Instituto de Estudios Peruanos. Alejandro Lugon 2022 Equilibrio, eficiencia e imperfecciones del mercado. Lima, Fondo Editorial PUCP. https://departamento.pucp.edu.pe/economia/libro/11996/ https://departamento.pucp.edu.pe/economia/libro/modelo-washington-neoliberalismo-desarrollo-economico-caso-peruano-1990-2020/ https://departamento.pucp.edu.pe/economia/libro/modelo-washington-neoliberalismo-desarrollo-economico-caso-peruano-1990-2020/ https://departamento.pucp.edu.pe/economia/libro/peru-desarrollo-naturaleza-urgencias-una-mirada-desde-la-economia-desarrollo-humano/ https://departamento.pucp.edu.pe/economia/libro/peru-desarrollo-naturaleza-urgencias-una-mirada-desde-la-economia-desarrollo-humano/ https://departamento.pucp.edu.pe/economia/libro/constitucion-crecimiento-economico-peru-1993-2021/ https://departamento.pucp.edu.pe/economia/libro/ensayos-macroeconomicos-honor-felix-jimenez/ Waldo Mendoza Bellido 2022 Cómo investigan los economistas. Guía para elaborar y desarrollar un proyecto de investigación. Segunda edición aumentada. Lima, Fondo Editorial PUCP. Elena Álvarez (Editor) 2022 Agricultura y desarrollo rural en el Perú: homenaje a José María Caballero. Lima, Departamento de Economía PUCP. Aleida Azamar Alonso, José Carlos Silva Macher y Federico Zuberman (Editores) 2022 Economía ecológica latinoamericana. Buenos Aires, México. CLACSO, Siglo XXI Editores. Efraín Gonzales de Olarte 2021 Economía regional y urbana. El espacio importa. Lima, Fondo Editorial PUCP. Alfredo Dammert Lira 2021 Economía minera. Lima, Fondo Editorial PUCP. Adolfo Figueroa 2021 The Quality of Society, Volume II – Essays on the Unified Theory of Capitalism. New York, Palgrave Macmillan. Carlos Contreras Carranza (Editor) 2021 La Economía como Ciencia Social en el Perú. Cincuenta años de estudios económicos en la Pontificia Universidad Católica del Perú. Lima, Departamento de Economía PUCP. José Carlos Orihuela y César Contreras 2021 Amazonía en cifras: Recursos naturales, cambio climático y desigualdades. Lima, OXFAM. Alan Fairlie 2021 Hacia una estrategia de desarrollo sostenible para el Perú del Bicentenario. Arequipa, Editorial UNSA. Waldo Mendoza e Yuliño Anastacio 2021 La historia fiscal del Perú: 1980-2020. Colapso, estabilización, consolidación y el golpe de la COVID-19. Lima, Fondo Editorial PUCP. Cecilia Garavito 2020 Microeconomía: Consumidores, productores y estructuras de mercado. Segunda edición. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. Adolfo Figueroa 2019 The Quality of Society Essays on the Unified Theory of Capitalism. New York. Palgrave MacMillan. Carlos Contreras y Stephan Gruber (Eds.) 2019 Historia del Pensamiento Económico en el Perú. Antología y selección de textos. Lima, Facultad de Ciencias Sociales PUCP. https://departamento.pucp.edu.pe/economia/libro/agricultura-desarrollo-rural-peru-homenaje-jose-maria-caballero/ https://departamento.pucp.edu.pe/economia/libro/10743/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-historia-fiscal-del-peru-1980-2020-colapso-estabilizacion-consolidacion-golpe-la-covid-19/ https://departamento.pucp.edu.pe/economia/libro/la-historia-fiscal-del-peru-1980-2020-colapso-estabilizacion-consolidacion-golpe-la-covid-19/ http://departamento.pucp.edu.pe/economia/libro/the-quality-of-society-essays-on-the-unified-theory-of-capitalism/ Barreix, Alberto Daniel; Corrales, Luis Fernando; Benitez, Juan Carlos; Garcimartín, Carlos; Ardanaz, Martín; Díaz, Santiago; Cerda, Rodrigo; Larraín B., Felipe; Revilla, Ernesto; Acevedo, Carlos; Peña, Santiago; Agüero, Emmanuel; Mendoza Bellido, Waldo; Escobar Arango y Andrés. 2019 Reglas fiscales resilientes en América Latina. Washington, BID. José D. Gallardo Ku 2019 Notas de teoría para para la incertidumbre. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. Úrsula Aldana, Jhonatan Clausen, Angelo Cozzubo, Carolina Trivelli, Carlos Urrutia y Johanna Yancari 2018 Desigualdad y pobreza en un contexto de crecimiento económico. Lima, Instituto de Estudios Peruanos. Séverine Deneulin, Jhonatan Clausen y Arelí Valencia (Eds.) 2018 Introducción al enfoque de las capacidades: Aportes para el Desarrollo Humano en América Latina. Flacso Argentina y Editorial Manantial. Fondo Editorial de la Pontificia Universidad Católica del Perú. Mario Dammil, Oscar Dancourt y Roberto Frenkel (Eds.) 2018 Dilemas de las políticas cambiarias y monetarias en América Latina. 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Diciembre, 2021 No. 503 “La no linealidad en la relación entre la competencia y la sostenibilidad financiera y alcance social de las instituciones microfinancieras reguladas en el Perú”. Giovanna Aguilar y Jhonatan Portilla. Noviembre, 2021. No. 502 “Approximate Bayesian Estimation of Stochastic Volatility in Mean Models using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets”. Carlos A. Abanto-Valle, Gabriel Rodríguez, Luis M. Castro Cepero and Hernán B. Garrafa-Aragón. Noviembre, 2021. No. 501 “El impacto de políticas diferenciadas de cuarentena sobre la mortalidad por COVID-19: el caso de Brasil y Perú”. Angelo Cozzubo, Javier Herrera, Mireille Razafindrakoto y François Roubaud. Octubre, 2021. No. 500 “Determinantes del gasto de bolsillo en salud en el Perú”. Luis García y Crissy Rojas. Julio, 2021. No. 499 “Cadenas Globales de Valor de Exportación de los Países de la Comunidad Andina 2000-2015”. Mario Tello. Junio, 2021. No. 498 “¿Cómo afecta el desempleo regional a los salarios en el área urbana? Una curva de salarios para Perú (2012-2019)”. Sergio Quispe. Mayo, 2021. No. 497 “¿Qué tan rígidos son los precios en línea? Evidencia para Perú usando Big Data”. Hilary Coronado, Erick Lahura y Marco Vega. Mayo, 2021. No. 496 “Reformando el sistema de pensiones en Perú: costo fiscal, nivel de pensiones, brecha de género y desigualdad”. Javier Olivera. Diciembre, 2020. No. 495 “Crónica de la economía peruana en tiempos de pandemia”. Jorge Vega Castro. Diciembre, 2020. No. 494 “Epidemia y nivel de actividad económica: un modelo”. Waldo Mendoza e Isaías Chalco. Setiembre, 2020. No. 493 “Competencia, alcance social y sostenibilidad financiera en las microfinanzas reguladas peruanas”. Giovanna Aguilar Andía y Jhonatan Portilla Goicochea. Setiembre, 2020. No. 492 “Empoderamiento de la mujer y demanda por servicios de salud preventivos y de salud reproductiva en el Perú 2015-2018”. Pedro Francke y Diego Quispe O. Julio, 2020. No. 491 “Inversión en infraestructura y demanda turística: una aplicación del enfoque de control sintético para el caso Kuéalp, Perú”. Erick Lahura y Rosario Sabrera. Julio, 2020. No. 490 “La dinámica de inversión privada. El modelo del acelerador flexible en una economía abierta”. Waldo Mendoza Bellido. Mayo, 2020. No. 489 “Time-Varying Impact of Fiscal Shocks over GDP Growth in Peru: An Empirical Application using Hybrid TVP-VAR-SV Models”. Álvaro Jiménez and Gabriel Rodríguez. Abril, 2020. No. 488 “Experimentos clásicos de economía. Evidencia de laboratorio de Perú”. Kristian López Vargas y Alejandro Lugon. Marzo, 2020. No. 487 “Investigación y desarrollo, tecnologías de información y comunicación e impactos sobre el proceso de innovación y la productividad”. Mario D. Tello. Marzo, 2020. No. 486 “The Political Economy Approach of Trade Barriers: The Case of Peruvian’s Trade Liberalization”. Mario D. Tello. Marzo, 2020. No. 485 “Evolution of Monetary Policy in Peru. An Empirical Application Using a Mixture Innovation TVP-VAR-SV Model”. Jhonatan Portilla Goicochea and Gabriel Rodríguez. Febrero, 2020. No. 484 “Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models”. Jean Pierre Fernández Prada Saucedo and Gabriel Rodríguez. Febrero, 2020. No. 483 “Macroeconomic Effects of Loan Supply Shocks: Empirical Evidence”. Jefferson Martínez amd Gabriel Rodríguez. Febrero, 2020. No. 482 “Acerca de la relación entre el gasto público por alumno y los retornos a la educación en el Perú: un análisis por cohortes”. Luis García y Sara Sánchez. Febrero, 2020. No. 481 “Stochastic Volatility in Mean. Empirical Evidence from Stock Latin American Markets”. Carlos A. Abanto-Valle, Gabriel Rodríguez and Hernán B. Garrafa- Aragón. Febrero, 2020. No. 480 “Presidential Approval in Peru: An Empirical Analysis Using a Fractionally Cointegrated VAR2”. Alexander Boca Saravia and Gabriel Rodríguez. Diciembre, 2019. No. 479 “La Ley de Okun en el Perú: Lima Metropolitana 1971 – 2016.” Cecilia Garavito. Agosto, 2019. No. 478 “Peru´s Regional Growth and Convergence in 1979-2017: An Empirical Spatial Panel Data Analysis”. Juan Palomino and Gabriel Rodríguez. Marzo, 2019.  Materiales de Enseñanza No. 14 “Programación de Experimentos en Ciencias Sociales con oTree”. Ricardo Huamán- Aguilar y Joan Miranda. Marzo, 2025. No. 13 “Fundamentos de Econometría”. Juan León Jara Almonte y Marcelo Manuel Gallardo Burga. Febrero, 2025. No. 12 “La teoría clásica de las ventajas comparativas en el comercio internacional”. Jorge Vega Castro. Junio, 2024. No. 11 “La teoría de protección efectiva: conceptos básicos”. Jorge Vega Castro. Mayo, 2023. No. 10 “Boleta o factura: el impuesto general a las ventas (IGV) en el Perú”. Jorge Vega Castro. Abril, 2023. No. 9 “Economía Pública. Segunda edición”. Roxana Barrantes Cáceres, Silvana Manrique Romero y Carla Glave Barrantes. Febrero, 2023. No. 8 “Economía Experimental Aplicada. Programación de experimentos con oTree”. Ricardo Huamán-Aguilar. Febrero, 2023 No. 7 “Modelos de Ecuaciones Simultáneas (MES): Aplicación al mercado monetario”. Luis Mancilla, Tania Paredes y Juan León. Agosto, 2022 No. 6 “Apuntes de Macroeconomía Intermedia”. Felix Jiménez. Diciembre, 2020 No. 5 “Matemáticas para Economistas 1”. Tessy Váquez Baos. Abril, 2019. No. 4 “Teoría de la Regulación”. Roxana Barrantes. Marzo, 2019. No. 3 “Economía Pública”. Roxana Barrantes, Silvana Manrique y Carla Glave. Marzo, 2018. No. 2 “Macroeconomía: Enfoques y modelos. Ejercicios resueltos”. Felix Jiménez. Marzo, 2016. No. 1 “Introducción a la teoría del Equilibrio General”. Alejandro Lugon. Octubre, 2015. Departamento de Economía - Pontificia Universidad Católica del Perú Av. 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