Total Factor Productivity Change Research Articles

Measuring efficiency of commercial banks and other deposit institutions: DEA-Malmquist approach

Research background: Commercial banks play a vital role in the global financial system and are critical components of it. Hence, the efficient performance of commercial banks could lead to more robust economic stability, enhanced financial resilience, and sustainable growth. The pandemic/post-pandemic period forces the expansion of digitalisation in the economy, including the banking sector. The Malmquist Index helps to assess productivity change. This research fills the gap in measuring the dynamics of efficiency and productivity change during the pandemic/post-pandemic period in a small open economy. Purpose of the article: The current study aims to assess and compare the dynamics of banking sector efficiency and productivity change in the pandemic/post-pandemic period. Methods: Data Envelopment Analysis (DEA) was used in the current research in order to measure the efficiency of deposit institutions operating in Lithuania. The study employed the input-oriented Constant Returns to Scale (CRS) model in order to assess how efficiently the banks utilise the inputs to achieve the outputs. The calculations of the efficiency scores were complemented using the Malmquist Index, which evaluates the productivity change over time using Total Factor Productivity Change (TFPCH), Technical Efficiency Change (TECH), and Technological Change (TCCH). Findings & value added: To the best of the authors’ knowledge, this is the first study to explore the banks’ efficiency and productivity change using DEA and MI for the Lithuanian banking sector. The research results have revealed varying efficiency among the Lithuanian commercial banks and other deposit institutions within the four investigated models. Depending on the model, some studied deposit institutions reached the highest scores, while others showed lower efficiency. However, the results of the Malmquist Index have showed overall productivity growth across all the models, underlining positive technological advancements despite challenges like the COVID-19 pandemic, i.e. the productivity change showed a positive dynamic over the analysed period. The present research provides valuable insights and contributes to efficiency-related knowledge, highlighting trends in productivity for strategic decision-making and policy formulation based on the case of deposit institutions operating in Lithuania. The results are valuable and could be practically implied by other EU banks operating in small open economies by adopting the practices of the banks that were considered efficient and showed positive productivity change. In other words, high-performing Lithuanian banks could be treated as a benchmark and set as a model for less efficient banks operating in other EU countries.

Locational and firm-level determinants of total factor productivity in Vietnamese manufacturing

Total factor productivity (TFP) has received much attention from both academics and policy makers in the past decade, as it promises an answer to the question of sustainable growth in the context of limited inputs. This article examines the TFP of manufacturing companies in Vietnam and the factors at the firm level, in addition to locational drivers, especially at the provincial level, that drive the change in TFP. We use a strongly balanced panel dataset of 1,130 manufacturing firms from 2014 to 2018 and the Wooldridge method to calculate the TFP and a general least squares estimator with a heteroskedastic and uncorrelated error structure to examine the determinants. The results show that the TFP of firms is driven by factors at both the firm and the province levels. Specifically, firm size and capital intensity have a positive impact on TFP, while differences in firm type also influence TFP outcomes. Furthermore, six locational factors significantly impact firms' TFP, with a particular emphasis on Information and Communications Technology (ICT) readiness, transparency index, and corruption index. Most importantly, we found that transparency and corruption have a nonlinear inverse U-shaped association with the TFP of firms, implying that there is an optimal midpoint of governance quality that maximizes the TFP of firms. The article then provides certain implications for both policymakers and firms in improving their TFP.

Productivity growth of Spanish tourist wineries using a directional distance approach

This paper analyses, in a two-step procedure, the productivity growth of 69 Spanish wineries (period 1998-2022) which offer tourism activities at their installations. To do so, we firstly calculate the total factor productivity (TFP) change using a directional distance function (DDF) and the Luenberger-Hicks-Moorsteen (LHM) TFP indicator introduced by Ang and Kerstens (2017), which allows us to correctly distinguish between technological change (TC), technical inefficiency change (TINC) and scale inefficiency change (SINC). Although a high efficiency is found in median terms, productivity decreased during the period in cumulative terms. This is explained by two contrasting forces: a positive TINC and SINC, and a negative TC. In a second step, we analyse the effect of some factors on TFP change and its components using a dynamic panel data model. Several practical implications are proposed based on these outcomes.

Evaluating the Efficiency of Turkish Banking Sector Using Data Envelopment Analysis and Malmquist Productivity Index: Covid-19 Pandemic and After

Covid-19 pandemic has created a deep crisis with social, cultural, economic, and political consequences all over the world. In this study, we used the Malmquist Total Factor Productivity Change Index (TFPCH) and Data Envelopment Analysis (DEA) methods to assess the efficiency and performance of banks during the Covid-19 pandemic and in the post-pandemic era. We performed the application for 17 banks in Türkiye of which data structure is suitable for analysis. For the analysis, data from 2019, the beginning of the Covid-19 epidemic, to 2022, when the epidemic almost ended, were used. The highest value of the general efficiency average of banks is 0.943 in 2020. 2020 is also the year with the highest number of effective banks with a total of 9 effective banks. The highest value of the TFPCH general average is 1.543 in the 2021-2022 period. It was determined that the state banks had the highest average efficiency percentage and the highest TFPCH average for the entire period of 2019-2022.

Unveiling Productivity Dynamics Among Regional Rural Banks in India

This study employs the non-parametric Malmquist productivity index to evaluate the total factor productivity (TFP) change among the regional rural banks (RRBs) in India from 2016 to 2023. The analysis of productivity change among the RRBs holds intrinsic significance due to its potential impact on overall performance and to the delivery of quality services among the rural population. To evaluate the productivity change under varying combinations of input and output selections, two distinct approaches were used: the intermediation approach and the production approach. The empirical results reveal substantial variation in average TEP estimates across the RRBs under consideration throughout the sample period. Furthermore, the observed productivity changes predominantly emanated from technological change (TC) rather than efficiency improvements. Additionally, we found that the smaller RRBs were performing better than their larger counterparts. These empirical findings bear noteworthy policy implications, offering valuable insights for policymakers and regulators to formulate effective strategies to ensure the competitiveness and sustainability of RRBs in India.

Revisiting the Productivity Paradox: What is Next for the BRICS and European Banking Systems?

Information technology is a critical driver of productivity growth in modern economies. However, there has been no convincing explanation for the observed discrepancy in the literature, increasing suspicion on whether IT can improve institutional performance in contemporary banking markets. The fallacy of productivity adds credence to Robert Solow’s dictum, “You can see the computer age everywhere except in productivity statistics”. We employ two extensive bank-level datasets of 5,794 institutions across 37 nations to estimate the total factor productivity (TFP) payoffs from IT in BRICS and European markets. A DEA-based, Malmquist productivity index quantifies TFP change and its respective components. Findings provide evidence against the paradox as both regions experience IT-fueled productivity growth. Nevertheless, such associations vary across banking sector development, rationalizing how IT spending can explain productivity differences across nations. For BRICS banks, a significant proportion of TFP growth originates from frontier expansion instead of frontier progression, signaling a widening of technology gap. Contrastingly, IT has diminished the technology gap between European banks. Intra-country comparisons suggest that if IT-driven productivity growth is regarded as a nation’s long-term goal, industry characteristics should govern the distribution of knowledge capital.

Bank Productivity: A Meta-Regression Analysis

Purpose: This study aims at examining studies employing the Malmquist Productivity Index (MPI) in calculating banks’ productivity. It also seeks to determine the factors affecting the total factor productivity change of banks through meta-regression analysis. Methodology: On December, 2023, relevant works were systematically reviewed using Web of Science (WoS), Scopus, and Google Scholar. The literature review employed a comprehensive search involving all files with the keywords such as ‘‘productivity” and “bank’’. The research process adhered to the PRISMA guidelines. Findings: Key features of the 35 studies incorporated in the analysis are presented. The samples of 65.71% of the studies are Asian countries. The bank productivity of 45.71% was calculated through the DEA-MPI method. The studies under consideration were sourced from diverse populations. These studies share key similarities in terms of subject and methodology. Random Effects Model was used to test heterogeneity across studies. The common effect size is 19.361 (z= 4.23, 95% CI: [10.384, 28.338]). Inter-study heterogeneity was determined through Cochran Q test and I^2 index (I^2= % 100, df=32.000, Q=141163533.762, p

Productivity in the Insurance Company: A Meta-Regression Analysis

Purpose –This study attempts to examine the studies using Malmquist Productivity Index (MPI) in calculating the productivity of insurance sector and to identify the factors influencing their total factor productivity change through meta-regression analysis. Design/Methodology/Approach –This study employed 29 empirical articles which were published between 1977-2023 and which met the inclusion criteria. Findings –The results on the meta-regression analysis revealed that the publication year of the studies and the income groups of the countries did not have an impact on the total factor productivity. The number of observations, number of variables, and branch of theinsurance company were statistically significant on the estimation of total factor productivity change.Discussion –In this study, a meta-regression analysis systematically highlights the effects of methodological assumptions on the productivity of insurance companies and synthesize empirical findings. The study serves to make the literature more accessible to researchers who will use the MPI method for measuring the productivity of insurance companies. This study is expected to guide managers in enhancing the productivity of insurance companies, considering national requirements, making and implementing legal regulations, and identifying policies

Assessing Road Safety in Morocco’s Regions from 2014 to 2022: A DEA-MPI Benchmarking Analysis

Assessing road safety performance in various regions of a country is crucial for improving overall road safety conditions and reducing the global mortality rate. This study employs the data-envelopment-analysis-based Malmquist productivity index (DEA-MPI) to comprehensively assess the progress of road safety performance in different regions of Morocco over time. Using a dataset spanning from 2014 to 2022, which contains data on road accidents, fatalities, injuries, the number of vehicles, and road traffic, this article evaluates the efficiency evolution across Morocco’s twelve regions. The study results show that the improvement of Morocco’s road safety performance during the studied period is unsatisfying and far from reaching the objectives of the current road safety strategy, which aims to reduce the number of fatalities by 50% by 2026. Moreover, the Malmquist productivity index (MPI) approach, which decomposes total factor productivity change into efficiency and technical changes, revealed that neither component shows a consistent trend throughout the studied period. This indicates that performance progress over time is insufficient and falls short of expectations, underscoring the immediate need for both technical and managerial improvements to address the current road safety challenges effectively.

Equity and efficiency of health resource allocation in the Chengdu-Chongqing Economic Circle of China.

This study aimed to evaluate the fairness and efficiency of health resource allocation (HRAE) in Chengdu-Chongqing Economic Circle after the new healthcare reform. This study also aimed to identify existing problems, providing empirical evidence for the government to formulate regional health plans scientifically and reasonably. The fairness of health resource allocation was analyzed using the Gini coefficient, Theil index, and agglomeration degree from population and geographical area perspectives. The three-stage data envelopment analysis and the Malmquist productivity index were used to analyze HRAE from static and dynamic perspectives. The Gini coefficient for population allocation in Chengdu-Chongqing Economic Circle was 0.066-0.283, and the Gini coefficient for geographical area allocation was 0.297-0.469. The contribution rate within a region was greater than that between regions, and health resources were mainly concentrated in economically developed core areas. The overall fairness of Chengdu Economic Circle was relatively better than that of Chongqing Economic Circle. Moreover, the adjusted mean technical efficiency was 0.806, indicating room for HRAE improvement in Chengdu-Chongqing Economic Circle. Stochastic Frontier Analysis found that different environmental variables have varying degrees of impact on HRAE. The adjusted mean total factor productivity change (Tfpch) was 1.027, indicating an overall upward trend in HRAE since the new healthcare reform. However, scale efficiency change (Sech) (0.997) limited the improvement of Tfpch. The fairness of health resources allocated by population was better than that allocated by geographical area. The unfairness of health resources mainly stemmed from intra-regional differences, with considerable health resources concentrated in core areas. Over the past 13 years, HRAE has improved but exhibited spatial heterogeneity and Sech-hindered productivity improvement. The study recommends strengthening regional cooperation and sharing to promote the integrated and high-quality development of the health and well-being in Chengdu-Chongqing Economic Circle.

Efficiency evaluation, regional technological heterogeneity and determinant of total factor productivity change in China’s healthcare system

Enhancing efficiency and productivity in countries’ healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China’s healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region’s efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country’s attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal–Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system’s efficiency, TFPC, and TGR.

Impact of Islamicity on the productivity of conventional and Islamic banks in selected southeast Asian countries

This study provides a new understanding of how bank-specific characteristics, the macroeconomic environment, and Islamicity affect the productivity of conventional banks (CBs) and Islamic banks (IBs) in selected Southeast Asian countries. The data of 153 banks, collected between 2015 and 2020, were used, and the influence of the selected factors was investigated by using panel data approaches, as well as the data envelopment analysis-based Malmquist productivity index (DEA-MPI). In the first-stage analysis, bank productivity was measured by using total factor productivity changes (TFPC), and in the second-stage analysis, panel regression analysis was performed, specifically, multiple panel regression analysis, which employed pooled ordinary least squares, a fixed-effects model, and a random-effects model. Parametric and nonparametric (Mann–Whitney and Kruskall–Wallis) tests found that the IBs were more progressive than the CBs. The findings indicated that unlike the IBs, change in the productivity of the CBs was adversely affected by economic Islamicity, as well as legal and governance Islamicity, which indicated that macroeconomic and legal structures that leaned toward Islamic values adversely affected the CBs. Meanwhile, the IBs were likely to be structured in line with such values and thus can mitigate the adverse impact of various factors. Both groups were negatively affected by human and political rights Islamicity, which indicated the use of a setup that could not benefit from Islamic values. This finding lends credence to the arguments in the literature that IBs mirror CBs in practice to remain competitive in their pricing based on market principles. The IBs were found to benefit from international relations Islamicity, which bodes well for the competitiveness of new players against the ancient setup of CBs in the globalized world.

Which productivity can promote clean energy transition —total factor productivity or green total factor productivity?

Developing clean energy is a key pathway and an inevitable choice for achieving the goals of carbon peak and carbon neutrality. From a global perspective, technology is increasingly affecting the trajectory of energy transition, driving clean energy into a stage of rapid development. Therefore, this paper focuses on exploring the dynamic evolutionary characteristics of clean energy transitions driven by different productivity. Using panel data from 171 economies from 1990 to 2019, this paper systematically examines the impact of Total Factor Productivity (TFP) and Green Total Factor Productivity (GTFP) on clean energy transitions. The empirical results indicate that both TFP and GTFP positively impact clean energy transition. Specifically, clean energy consumption increases by 3.35% and 6.03%with a one standard deviation change in TFP and GTFP respectively. Upon decomposing TFP and GTFP, it was found that Green Efficiency Change (GECH) and Green Technical Change (GHCH), especially GECH, are the main factors driving the clean energy transition. Heterogeneity analysis shows that, in developed economies, GTFP has a larger positive impact on clean energy transition than TFP. Furthermore, GTFP demonstrates a significant positive impact on the clean energy transition both before and after the 2008 financial crisis, whereas TFP's positive impact is only evident before the crisis. Our findings emphasize the social benefits of further investments in GTFP.

The Effect of Technology Transfers on Productivity Increment in Japanese Overseas Manufacturing

Technological innovation and technology transfer play an important role in enhancing the productivity of MNEs, especially the Japanese overseas manufacturing subsidiaries. The purpose of this study is to examine the association between R&D cost, technology purchase, and total factor productivity change and regression models with the survey data of Japanese Ministry of Economy, Trade, and Industry on Overseas Business Activities conducted over 2021-2023. Basic and Trend Surveys were combined to create a sample of 1,798 affiliates across 38 countries with variables such as R&D spending, Technology Transfer payments, capital stock growth, and employment growth. The evidence presented suggests that both technology and R&D payments enhance productivity improvement for the company, and the estimated returns on R&D investments is 0.54 Yen (JPY) for every JPY spent and technology transfer payments 0.76 JPY. The regression results also show a synergistic relationship between technology and R&D payments indicating that affiliates involved in both processes report higher levels of efficiency. In addition, the study also focuses on geographical specialization of technology transfer and R&D in industries including chemicals, pharmaceuticals, and machinery, where major expenditure on R&D is done across the globe and particularly in United States and in Europe.

Impacts of Capital Formation and Investment Sources on Total Factor Productivity: The Example of Nigeria’s Agriculture

The study was designed to analyse the impact of capital formation and investment sources on Nigeria's agricultural productivity from the period 1980 - 2021. The time series data were analysed with Malmquist Data Envelopment Analysis (DEA) and the tobit regression models using DEA, the Total Factor Productivity Change, which quantifies the degree of productivity, was estimated at 1.022, which implies an average productivity progress of 2.2% annually. Again, since the value of technical efficiency change is less than technological change, it implies that the productivity gains are mainly attributable to technological progress instead of efficiency improvements in the periods under the study. The results of tobit regression indicate that domestic capital formation significantly influences agricultural TFP, highlighting the importance of government expenditure on agriculture (GEA) and gross fixed capital formation (GFCF) in enhancing productivity, although with minimal effects. Human capital formation (HCF) also showed positive impacts on Total Factor Productivity TFP, suggesting that improvements in workforce skills and knowledge are crucial for agricultural efficiency. However, FDI exhibited no significant correlation with TFP, implying that local investments are more beneficial than foreign aids in the context of Nigeria's agricultural sector. The findings emphasize the need for robust policy frameworks that align capital disbursements with agricultural production periods to optimize TFP. This study contributes to the discourse on agricultural productivity by providing empirical evidence on the critical role of capital investments in enhancing TFP within developing economies like Nigeria.

Rising Tides of Knowledge: Exploring China’s Higher Education Landscape and Human Capital Growth

AbstractThis study examines the status of human capital in China’s higher education system by analyzing the quantity of higher education institutions as a metric. The results show a general enhancement in human capital in all regions, along with an optimized structure. This is demonstrated by a decline in the primary education demographic, a consistent growth in secondary education, and a rapid surge in the higher education demographic. The percentage of junior college students has remained steady, whereas the ratios of undergraduate and graduate students have been consistently increasing. The Malmquist index method is used to analyze changes in total factor productivity (TFP) among provinces and regions by breaking it down into technical level and efficiency changes. From 2005 to 2010, there was a general increase in total factor productivity (TFP) and technical proficiency, although technical efficiency showed only slight enhancement. TFP and technical level growth decelerated from 2011 to 2020, while technical efficiency stayed above 1. These findings indicate that China’s total factor productivity (TFP) growth is mainly driven by technological advancements, with minimal improvements in technical efficiency.

An evaluation of the regional heterogeneity of museums' operational efficiency and productivity changes across Chinese provinces

Museums are critical in safeguarding cultural heritage and cultivating community educational opportunities. This research aims to evaluate operational efficiency (OE), the impact of technological change on total factor productivity change (TFPC), and the regional heterogeneity of museum performance in three regions and 31 provinces across China. To this end, the study employed DEA-SBM and the Malmquist Productivity Index to gauge OE, TFPC, and determinants of TFPC (efficiency change or emerging technologies change) across 31 provinces for 2012–2021. Results reveal that the average OE of the Chinese Museum is 0.8394. It shows a 16.06 % growth potential in the operational efficiency of Chinese Museums. Further, the OE of Chinese Museums declined over the study period from 0.8965 in 2012 to 0.8088 in 2021. Beijing, Fujian, and Hunan are ranked top with a Museum's OE Score of 1. The average MI score of Chinese Museums is 0.9744, and technology change is the main determinant of Decline in productivity change as EC = 0.9992 is greater than TC = 0.9846. The MI of Liaoning, Shanghai, Ningxia, Jiangxi, Chongqing, Sichuan, Guangdong, and Tianjin is over 1, indicating growth in total factor productivity over the study period. The eastern region of China shows higher operational efficiency and total factor productivity scores of museums than the central and western regions. The results of the Kruskal-Wallis test proved that a statistically significant difference exists among different regions of China for the OE, MI, EC, and TC of museums.

Can performance-based financing improve efficiency of health centers in Ethiopia? A Malmquist Productivity Index analysis

IntroductionThe Ethiopian government has introduced several healthcare financing reforms intending to improve efficiency. Piloting implementation of performance-based financing is one of these actions. The purpose of this research is to assess the efficiency of healthcare facilities that have implemented performance-based financing compared to those that have not.MethodsEfficiency was measured using a nonparametric data envelopment analysis and the Malmquist Productivity Index technique. Total factor productivity change, technical change, and technological change are compared across eight sampled healthcare facilities that are implementing performance-based financing and eight that are not in Ethiopia.ResultsHealth facilities implementing performance-based financing have a mean technical efficiency score of 64%, allowing for a potential 36% reduction in inputs without affecting outputs. Their scale efficiency is 88%, indicating a potential 12% increase in total outputs without expanding facilities. In contrast, facilities not implementing performance-based financing have a mean technical efficiency score of 62%, with a potential for 38% input reduction without affecting outputs. Their scale efficiency is 87%, suggesting a potential 13% increase in total outputs without scaling up facilities. Among the 16 healthcare facilities observed, seven experienced a decline in the mean total productivity, while one remained stagnant. The remaining eight facilities witnessed an increase in productivity. The healthcare facilities implementing performance-based financing showed a 1.3% decrease in mean total productivity during the observed period. Among them, five showed an increase and three showed a decrease in the total factor of productivity. The mean total factor of productivity of all healthcare facilities not implementing performance-based financing remained stagnant over the three-year period (2019–2021), with four showing an increase and four showing a decrease in total productivity.ConclusionsThe study concludes that implementing performance-based financing did not improve productivity levels among healthcare facilities over three years. In fact, productivity decreased among the facilities implementing performance-based financing, while those not implementing it remained stagnant. This shows health facilities that implement performance-based financing tend to utilize more resources for similar outputs, contradicting the anticipated efficiency improvement.

The effect of COVID-19 pandemic on the efficiency of training and research hospitals in Turkey

The Coronavirus disease 2019 (COVID-19), which emerged in December 2019, caused an increase in the demand for healthcare services globally. Hospitals’ operational and financial performances are thought to have been negatively affected by COVID-19 because they had to postpone providing non-urgent healthcare services to prevent infection. This study aimed to evaluate how the COVID-19 pandemic has affected the efficiency of training and research hospitals in Turkey. The study population comprised 51 training and research hospitals. The Malmquist Total Factor Productivity index was used to examine the changes in the efficiency of the training and research hospitals between 2017–2018, 2018–2019, 2019–2020, and 2020–2021. In addition, a repeated-measures ANOVA test was used to evaluate whether a significant difference existed in the efficiency levels of the hospitals in the pre-COVID-19 pandemic periods (2017–2018 and 2018–2019) and during them (2019–2020 and 2020–2021). No significant difference was found between hospitals' technical and scale efficiency levels before and during the pandemic, but the change in total factor productivity was significant (F = 34.18; p < 0.001). Our research findings provide clues about how hospitals' behaviours changed during the pandemic compared to before the pandemic. It was observed that efficiency decreases in hospitals at the onset of the pandemic disappeared with the adaptation process to the new normal. Technical efficiency decreased in two periods (2017–2018 and 2020–2021) when total factor productivity increased. Reverse changes in total factor productivity and technical efficiency indicate that technological change drives total factor productivity. This study identified differences in hospital efficiency during both ordinary and extraordinary periods. It would benefit managers and policymakers to recognise these differences and focus on managerial skills that ensure technical efficiency in extraordinary circumstances to mitigate possible efficiency losses.

Efficiency and productivity gains of robotic surgery: The case of the English National Health Service.

This paper examines the effect of new medical technology (robotic surgery) on efficiency gains and productivity changes for surgical treatment in patients with prostate cancer from the perspective of a public health sector organization. In particular, we consider three interrelated surgical technologies within the English National Health System: robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar clinical benefits. While the clinical benefits in adopting robotic surgery over laparoscopic intervention are unproven, it requires a high initial investment cost and carries high on-going maintenance costs. Using data from Hospital Episode Statistics for the period 2000-2018, we observe growing volumes of prostatectomies over time, mostly driven by an increase in robotic-assisted surgeries, and further analyze whether hospital providers that adopted a robot see improved measures of throughput. We then quantify changes in total factor and labor productivity arising from the use of this technology. We examine the impact of robotic adoption on efficiency gains employing a staggered difference-in-difference estimator and find evidence of a 50% reduction in length of stay (LoS), 49% decrease in post-LoS and 44% and 46% decrease in postoperative visits after 1year and 2years, respectively. Productivity analysis shows the growth in radical prostatectomy volume is sustained with a relatively stable number of urology surgeons. The robotic technique increases total production at the hospital level between 21% and 26%, coupled with a 29% improvement in labor productivity. These benefits lend some, but not overwhelming support for the large-scale hospital investments in such costly technology.