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The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

This study investigates the motivations and consequences of classification shifting from cost of sales to research and development (R&D) in high-technology industries.

This study conducts a multivariate analysis using logistic and ordinary least squares regression methods on panel data of high-technology firms for the period 1988–2012 to examine the effect of R&D classification shifting on gross margin benchmarks and future performance.

The results show that R&D classification shifting increases the likelihood of meeting or beating gross margin benchmarks. They also show that firms engaged in R&D classification shifting exhibit lower future R&D productivity, stock returns, and operating performance. The findings indicate that the short-term benefits of achieving gross margin benchmarks are offset by the long-term negative impact of R&D misclassification.

This paper provides insights that can help regulators develop clearer guidelines for the appropriate classification of R&D costs.

Moving beyond the core earnings management paradigm, this study demonstrates the use of R&D classification shifting as a tool to manipulate gross profits and R&D in high-technology industries. Most prior studies focused on the determinants of R&D classification shifting, while few investigated the impact of the practice. The findings in this study provide initial evidence of the consequences of R&D classification shifting for future R&D productivity and firm performance in high-tech industries. Using five methods, this study also validates R&D classification shifting and addresses the alternative explanation of R&D overinvestment.

This paper aims to examine the interrelation between two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. The authors first explore whether ex ante more productive firms are those that introduce innovations (the self-selection hypothesis) and if the introduction of innovations boosts TFP growth (the returns-to-innovation hypothesis). Second, the authors study the firm’s joint dynamic decision to implement process and/or product innovations. The authors use Colombian manufacturing data from the Annual Manufacturing and the Technological Development and Innovation Surveys.

This study uses a four-stage procedure. First, the authors estimate TFP using a modified version of Olley and Pakes (1996) and Levinsohn and Petrin (2003), proposed by De Loecker (2010), that implements an endogenous Markov process where past firm innovations are endogenized. This TFP would be estimated by GMM, Wooldridge (2009). Second, the authors use multivariate discrete choice models to test the self-selection hypothesis. Third, the authors explore, using multi-value treatment evaluation techniques, the life span of the impact of innovations on productivity growth (returns to innovation hypothesis). Fourth, the authors analyse the joint likelihood of implementing process and product innovations using dynamic panel data bivariate probit models.

The investigation reveals that the self-selection effect is notably more pronounced in the adoption of process innovations only, as opposed to the adoption of product innovations only or the simultaneous adoption of both process and product innovations. Moreover, our results uncover distinct temporal patterns concerning innovation returns. Specifically, process innovations yield immediate benefits, whereas implementing both product innovations only and jointly process and product innovations exhibit significant, albeit delayed, advantages. Finally, the analysis confirms the existence of dynamic interconnections between the adoption of process and product innovations.

The contribution of this work to the literature is manifold. First, the authors thoroughly investigate the relationship between the implementation of process and product innovations and productivity for Colombian manufacturing explicitly recognising that firms’ decisions of adopting product and process innovations are very likely interrelated. Therefore, the authors start exploring the self-selection and the returns to innovation hypotheses accounting for the fact that firms might implement process innovations only, product innovations only and both process and product innovations. In the analysis of the returns of innovation, the fact that firms may choose among a menu of three innovation strategies implies the use of evaluation methods for multi-value treatments. Second, the authors study the dynamic inter-linkages between the decisions to implement process and/or product innovations, that remains under studied, at least for emerging economies. Third, the estimation of TFP is performed using an endogenous Markov process, where past firms’ innovations are endogenized.

The aim of this study is to investigate the contribution of tangible and intangible investments in driving labour productivity growth in the European Union over the period 2000–2017 and their role in the short and medium run. Additionally, heterogeneity across countries is accounted for by performing estimates separately for Eastern and Western European countries.

The methodology used to conduct the analysis of the determinants of productivity is the two-way fixed-effect and the system generalised method of moments. We also include country-specific dummies in place of our variable on national innovative capacity as a means to further reduce the number of instruments.

The results reveal a long-term relationship of investment in intangible assets with labour productivity growth, more specifically of investment in R&D. This relationship holds both when considering the whole set of European countries and for Western European countries, demonstrating that R&D is key to enhancing labour productivity growth. On the contrary, the effect for Eastern countries is negative, probably due to the lack of capacity to turn this investment into an efficient and effective way to foster productivity.

Besides confirming the well-known role of tangible and intangible assets in productivity, the heterogeneity shown in our analysis highlights the need for improving capabilities in Eastern countries. Diversifying the decisions on the investments in European countries, depending on the specific needs and their heterogeneity, could help bridge the productivity gap and enhance specific capabilities of the country systems.

The purpose of this study is to investigate the impact of pervasive immigrant inflows on GDP productivity growth in selected OECD countries, including Australia, Canada, Germany, Italy, New Zealand and the USA. The study aims to consider patent filing residence and non-residence as well as R&D expenditure to see if large immigrant destination countries can accept many immigrants to generate knowledge and creativity and stimulate economic development.

The study uses OECD and WDI data sets from 2000 to 2019 and employs a fundamental correlation matrix and static panel model to analyze the data. The study examines the impact of residential and non-residential patent applications and R&D expenditure on GDP productivity growth in the selected OECD countries.

The study found an adverse effect for residential patent applications, while non-residential patent application and R&D expenditure variables were strongly linked to GDP productivity. This indicates that to reap the benefits of skilled immigration inflows, the selected OECD countries must devote more resources to research and development and build a knowledge-based economy. This will improve economic efficiency and overall growth.

This paper assists policymakers in comprehending how to effectively utilize immigration inflows in developed and emerging economies in order to construct a future knowledge-based economic system.

This study attempts to uncover the nonlinear relationship between public procurement and corporate total factor productivity (CTFP), and investigates the mediating roles of R&D investment and rent-seeking cost. Additionally, it conducts a heterogeneity analysis for firms with varying levels of political connections and corporate social responsibility (CSR).

Employing Ordinary Least Squares (OLS) and Olley-Pakes (OP) methods, the authors gauge CTFP and manually identify government customers to quantify public procurement. Leveraging panel data from Chinese listed companies, this study explores the relationship between public procurement and CTFP.

This study unveils a U-shaped relationship between public procurement and CTFP, highlighting R&D investment and rent-seeking costs as potential mechanisms. Furthermore, it identifies heterogeneous effects among companies with varying levels of political connections and CSR on the relationship between public procurement and CTFP, including their mediating effects.

This research enhances understanding of demand-side policies and provides crucial insights for the government to further improve public procurement policies.

By offering empirical evidence of how public procurement impacts CTFP, this paper enriches the literature on the behavioral repercussions of public procurement and the determinants of CTFP. It also overcomes the “black box” of the mechanism between public procurement and CTFP, based on the government’s dual role as a pathfinder and customer of enterprises. It broadens the application scenarios of institutional theory and principal-agent theory. Additionally, the heterogeneity analysis of firms with varying political connections and CSR extends the frontiers of related research.

Emerging economies have been highlighted as an important growth source of the global economy. However, this group of countries has not received enough academic attention yet. Therefore, this study aims to identify the impact of research and development (R&D) expenditure on economic growth in emerging economies.

The theoretical framework of the production function is applied to quantitatively analyse the impact of R&D expenditure on economic growth with a sample of 29 emerging economies in the period between 1996 and 2019.

The panel cointegration test confirms the existence of long-run cointegration relationships between economic growth and independent variables in these emerging economies. Besides, the estimated results show that the national R&D expenditure has positive effects on economic growth from both direct and interaction dimensions. This evidence has filled the empirical research gap in the R&D-growth nexus in the case of emerging economies. Finally, while gross capital and education have positive impacts on growth, corruption has a harmful effect on economic growth in these countries.

The results highlight that policymakers should enhance R&D expenditure and R&D activities as the key national development strategy. The investment in R&D not only helps emerging economies avoid the middle-income trap but also pushes these countries to successfully join the group of developed countries.

To the best of the authors’ knowledge, this research is among the first to examine the impact of R&D expenditure on economic growth with a homogeneous sample of emerging economies. The results are obviously helpful for policymakers to use R&D as the key development strategy for supporting economic growth in emerging economies in the future.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.