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on Efficiency and Productivity |
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Issue of 2024‒04‒15
six papers chosen by |
| By: | Cândida Ferreira |
| Abstract: | This paper contributes to the literature using first a Data Envelopment Analysis (DEA) approach to measure bank efficiency and the results provided by the Malmquist indices to analyse the evolution of the technical, technological, and scale efficiency changes, in a panel including 784 relevant banks of all the 27 European Union (EU) countries, between 2006 and 2021. In the second stage, the study uses panel dynamic Generalised Method of Moments (GMM) estimations to analyse the impact on the total productivity changes of bank market competition (measured with the estimated Boone indicator) and bank stability (proxied with the estimated Z-score), while controlling for some relevant bank activities, economic growth and the influence of the relevant crises that affected the EU banking sector during the considered period. The main findings reveal that while bank market competition looks like promoting the banks’ total factor productivity change, bank loans, bank deposits and short-term funding, as well as bank market stability and economic growth do not contribute to the banks’ total factor productivity changes. |
| Keywords: | European Union banking sector; Malmquist indices; bank total factor productivity changes; Z-score; Boone indicator. |
| JEL: | C33 D53 F36 G21 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:ise:remwps:wp03152024&r=eff |
| By: | Daraio, Cinzia (Sapienza Univer- sity of Rome); Di Leo, Simone (Sapienza Univer- sity of Rome); Simar, Léopold (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | In productivity and efficiency analysis, directional distances are very popular, due to their flexibility for choosing the direction to evaluate the distance of Decision Making Units (DMUs) to the efficient frontier of the production set. The theory and the statistical properties of these measures are today well known in various situations. But so far, the way to measure directional distances to the cone spanned by the attainable set has not been analyzed. In this paper we fill this gap and describe how to define and estimate directional distances to this cone, for general technologies, i.e. without imposing convexity. Their statistical properties are also developed. This allows us to measure distances to non-convex attainable set under Constant Returns to Scale (CRS) but also to measure and estimate Luenberger productivity indices and their decompositions for general technologies. The way to make inference on these indices is also described in details. We propose illustrations with some simulated data, as well as, a practical example of inference on Luenberger productivity indices and their decompositions with a well-known real data set. |
| Keywords: | Nonparametric production frontiers ; Cone ; DEA ; FDH ; Directional Distances ; Luenberger productivity indices |
| JEL: | C1 C14 C13 |
| Date: | 2024–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:aiz:louvad:2024009&r=eff |
| By: | Brantly Callaway; Tong Li; Joel Rodrigue; Yuya Sasaki; Yong Tan |
| Abstract: | Leveraging the sharp changes in environmental regulation embedded in China’s 11th Five-Year Plan (FYP), which covered the period from 2006 to 2010, we characterize the degree to which the plan softens trade-offs between emissions and output. We document that the 11th FYP is associated with modest changes in average or total sulphur dioxide (SO2) emissions among manufacturers, but a sharp decline in the variance in the distribution of emissions intensity. Extending well-known distributional estimators to characterize dynamic firm-level responses to policy change, we find large causal declines in emissions intensity in the upper quantiles of the distribution, modest evidence of increases in the lower quantiles and no change in the middle quantiles. Differential changes in firm-level emissions intensity are consistent with the differential investment in emissions-mitigating technology, energy switching and productivity improvements. Interpreted through the lens of a resource misallocation framework, China’s 11th FYP increased aggregate productivity and output by 1.8% and 10.2%, respectively, through improved resource allocation. Our model suggests efficient regulation could have further increased aggregate productivity by 3.5% and output by 4.7% without any increase in aggregate emissions. |
| Keywords: | Climate change; Productivity |
| JEL: | C21 D24 Q53 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:24-7&r=eff |
| By: | Naveen Kumar (Department of Economics, Delhi School of Economics); Dibyendu Maiti (Department of Economics, Delhi School of Economics) |
| Abstract: | This paper investigates the long-term impact of climate change on Indian economic growth, both at aggregate and dis-aggregated levels across regions and sectors. A simple Ramsey model is built to show that the resource abundance, climatic exposure, and state capacity affecting the rate of resource mobilisation for productivity and efficiency improvement determine regional growth. A crosssectional augmented auto-regressive distributed lag model (CS-ARDL), addressing endogeneity, heterogeneity, and cross-sectional dependence with stochastic trends, employed in 29 major states from 1980 to 2019, confirms a significant and negative impact of temperature rise on total factor productivity and the resultant economic growth. On average, one Celcius degree of temperature rise has depressed economic growth by approximately 3.89%, with substantial variations across states, sectors, and income groups. The variation in labour relations, industrialisation level, forest cover, and debts across the states affecting the ecological damage and efficiency changes in labour and capital differentially has been found responsible for the variation in TFP and the resultant growth. Our estimated coefficients combined with the projected temperature reveal that poorer and less developed states are expected to be more vulnerable than others because of their dependence on agriculture and ecological resources. The GSDP growth is projected to decrease by a range of 5.25% to 24.51% during 2020 to 2100 from the Stringent Mitigation scenario (SSP1-2.6) to the Business-as-Usual scenario (SSP5-8.5). JEL Code: O44, Q54, Q51 |
| Keywords: | climate change, economic growth, India, panel data, adaptation |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:cde:cdewps:345&r=eff |
| By: | Alice Fan; Bingjie Hu; Sadhna Naik; Neree C.G.M. Noumon; Keyra Primus |
| Abstract: | This paper identifies and quantifies the drivers of inflation dynamics in the three Baltic economies and assesses the effectiveness of fiscal policy in fighting inflation. It also analyzes the macroeconomic impact of inflation on competitiveness by focusing on the relationship between wages and productivity in the tradeable sector. The results reveal that inflation in the Baltics is largely driven by global factors, but domestic demand matters as well, suggesting that fiscal policy can play a role in containing inflation. Also, there is robust evidence of a long-run (cointegration) relationship between (real) wages in the tradeable (manufacturing) sector and productivity in the Baltics with short-term deviations self-correcting in Estonia and Lithuania only. |
| Keywords: | Inflation Dynamics; Competitiveness; Baltics |
| Date: | 2024–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/061&r=eff |
| By: | Mariana Carmelia Balanica-Dragomir; Gabriel Murariu; Lucian Puiu Georgescu |
| Abstract: | Carbon emissions have become a specific alarming indicators and intricate challenges that lead an extended argue about climate change. The growing trend in the utilization of fossil fuels for the economic progress and simultaneously reducing the carbon quantity has turn into a substantial and global challenge. The aim of this paper is to examine the driving factors of CO$_2$ emissions from energy sector in Romania during the period 2008-2022 emissions using the log mean Divisia index (LMDI) method and takes into account five items: CO$_2$ emissions, primary energy resources, energy consumption, gross domestic product and population, the driving forces of CO$_2$ emissions, based on which it was calculated the contribution of carbon intensity, energy mixes, generating efficiency, economy, and population. The results indicate that generating efficiency effect -90968.57 is the largest inhibiting index while economic effect is the largest positive index 69084.04 having the role of increasing CO$_2$ emissions. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.04354&r=eff |