|
on Efficiency and Productivity |
| By: | Gagliardi Nicola (CEBRIG and DULBEA, Solvay Brussels School of Economics and Management, Université Libre de Bruxelles); Elena Grinza (Department of Economics, Social Studies, Applied Mathematics and Statistics, University of Turin, Torino, CEBRIG (Université Libre de Bruxelles), LABORatorio Riccardo Revelli); Rycx François (CEBRIG and DULBEA, Solvay Brussels School of Economics and Management, Université Libre de Bruxelles) |
| Abstract: | In this paper, we investigate the impact of rising temperatures on firm productivity using longitudinal firm-level balance-sheet data from private sector firms in 14 European countries, combined with detailed weather data, including temperature. We begin by estimating firms' total factor productivity (TFP) using control-function techniques. We then apply multiple-way fixed-effects regressions to assess how higher temperature anomalies affect firm productivity - measured via TFP, labor productivity, and capital productivity. Our findings reveal that global warming significantly and negatively impacts firms' TFP, with the most adverse effects occurring at higher anomaly levels. Labor productivity declines markedly as temperatures rise, while capital productivity remains unaffected - indicating that TFP is primarily affected through the labor input channel. Our moderating analyses show that firms involved in outdoor activities, such as agriculture and construction, are more adversely impacted by increased warming. Manufacturing, capital-intensive, and blue-collar-intensive firms, compatible with assembly-line production settings, also experience significant productivity declines. Geographically, the negative impact is most pronounced in temperate and mediterranean climate areas, calling for widespread adaptation solutions to climate change across Europe. |
| Keywords: | Climate change, Global warming, Firm productivity, Total factor productivity (TFP), Semiparametric methods to estimate production functions, Longitudinal firm-level data. |
| JEL: | D24 J24 Q54 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:tur:wpapnw:094 |
| By: | Eder, Andreas |
| Abstract: | Using a 2007-2014 panel of Austrian crop farms, we analyze the effect of multiple dimensions of land fragmentation on farms' production efficiency and risk performance. We use Data Envelopment Analysis (DEA), a non-parametric linear programming approach, to estimate efficiencies. Technical efficiency is decomposed into i) scale efficiency, ii) pure technical efficiency, and iii) input-mix efficiency. Risk efficiency, a concept borrowed from modern portfolio theory, measures the performance of a farm relative to a mean-variance frontier. A second-stage DEA analysis reveals that farms with fewer plots and a shorter average farmstead to plot distance tend to be more technically efficient. Larger plots allow for better exploitation of returns to scale. The scattering of plots has no statistically significant effect on technical efficiency but provides benefits in terms of higher risk efficiency. Land consolidation projects should carefully weigh the costs and benefits associated with different dimensions of land fragmentation. |
| Keywords: | Land consolidation, Farmland fragmentation, Economies of scale, Agriculturalproductivity, Risk management, Data Envelopment Analysis |
| JEL: | Q12 Q15 Q18 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:forlwp:304301 |
| By: | Claudiu Tiberiu Albulescu (Politehnica University of Timisoara) |
| Abstract: | This paper investigates the asymmetric relationship between corporate tax avoidance and total factor productivity (TFP) using firm-level data for 141 European oil and gas companies, covering the period 2007 to 2015. Firstly, we rely on the novel mechanism advanced by Rovigatti and Mollisi (2018) to compute firms’ TFP. Secondly, we resort to Canay’s (2011) panel data fixed-effect quantile approach to assess the nonlinear, asymmetric effect that tax avoidance has on a firm’s productivity. As novelty, we use two proxy variables to estimate tax avoidance, namely companies’ holding structures and tax haven location. We discover that the impact of tax avoidance on TFP is not straightforward. On the one hand, we report mixed empirical findings regarding the impact of firms’ organization in holding structures on TFP. On the other hand, tax haven location enhances the productivity of oil and gas companies from the extractive industry. Finally, we show that the impact of tax avoidance on TFP is stronger at higher quantiles, that is, for higher levels of productivity. Our findings show that offshore profit transfers represent a quite common practice for European oil and gas firms, in particular for the large companies, which helps them to increase their productivity level. In our analysis we control for the role of ownership structure, firm size, intangibles, indebtedness and energy price dynamics. To check the robustness we use different approaches to compute the TFP. |
| Keywords: | TFP, tax avoidance, oil and gas companies, tax haven, quantile regression |
| JEL: | O |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:inf:wpaper:2024.15 |
| By: | Fall, François Seck; Tchakoute Tchuigoua, Hubert; Vanhems, Anne; Simar, Léopold (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | This study analyzes the impact of unobserved heterogeneity on microfinance efficiency. Using panel data for 168 microfinance institutions (MFIs) over the period 20102015, we examine the persistence of the effect of unobserved heterogeneity on microfinance efficiency. Using recent nonparametric and robust techniques, we identify a latent heterogeneity factor related to the ability of MFI managers to promote efficiency, independent of MFI size, and analyze its impact on MFI inefficiency measures over time. We then assess the robustness of our results to several factors: the MFI status of the MFI (for-profit or nonprofit), the definition of the efficiency measure (social and financial) and an observed degree of heterogeneity captured by the percentage of women on the board. Finally, we analyze the relationship between our unobserved heterogeneity factor and institutional and socio-economic indicators. |
| Keywords: | OR in developing countries ; Microfinance ; Efficiency ; Unobserved heterogeneity ; Panel data ; Nonparametric robust frontier models |
| Date: | 2024–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024020 |
| By: | Simplice A. Asongu (Johannesburg, South Africa); Joseph Nnanna (Abuja, Nigeria) |
| Abstract: | This research investigates how enhancing remittances affects total factor productivity (TFP) dynamics in Sub-Saharan Africa. The Generalised Method of Moments (GMM) empirical strategy is adopted for the purpose of the study and the engaged TFP dynamics are: TFP, real TFP, welfare TFP and real welfare TFP. Significant net effects are not apparent from enhancing remittances for TFP, real TFP growth and welfare TFP while positive net effects are apparent on real welfare TFP. The unexpected findings are elucidated and policy implications are discussed. This study has complemented the attendant literature by assessing how growing remittances influence dynamics of TFP in Sub-Saharan Africa. |
| Keywords: | Economic Output; Remitances; Sub-Saharan Africa |
| JEL: | E23 F24 F30 O16 O55 |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:exs:wpaper:24/021 |
| By: | Simar, Léopold (Université catholique de Louvain, LIDAM/ISBA, Belgium); Wilson, Paul (Clemson University) |
| Abstract: | Kneip, Simar and Wilson (Journal of Business and Economic Statistics, 2016) and Daraio, Simar and Wilson (The Econometrics Journal, 2018) provide non-parametric tests of (i) convexity versus non-convexity of the production set, (ii) constant ver- sus non-constant returns-to-scale of the frontier, and (iii) separability versus non- separability of the frontier with respect to environmental variables. Among other uses, these tests are essential for deciding which non-parametric efficiency estimator should be used to estimate technical efficiency. Each test requires randomly splitting the sample. Although theory establishes that the tests are valid for any random split, results can vary with different splits. This paper provides a computationally efficient method to aggregate test outcomes across multiple sample-splits using ideas from the statistical literature on controlling false discovery rates in multiple testing situations. We provide tests using multiple sample-splits (to remove the ambiguity resulting from a single sample-split) and extensive Monte Carlo evidence on the size and power of our tests. The computational time required by the new tests is about 0.001 times the computational time required by the bootstrap method proposed by Simar and Wilson (Journal of Productivity Analysis, 2020). |
| Keywords: | Hypothesis testing ; inference ; multiple splits ; convexity ; returns to scale ; separability ; DEA ; FDH |
| JEL: | C12 C44 C63 |
| Date: | 2024–04–10 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024012 |
| By: | Jacques Bughin |
| Abstract: | Generative Artificial Intelligence (genAI) is the latest evidence of the transformative value of AI in organizations. One promising avenue lies in software engineering, where genAI can contribute to coding by pairing with developers. Based on a sample of global firms, two main insights emerge on analyzing the productivity implications of genAI-pair coding. Coding quality is negatively correlated with productivity throughput gains, while quality-adjusted productivity gains depend on the extent to which organizations have deployed AI capabilities in the form of data, skills upgrade, and AI governance. As observed with other digital technologies, the success of using genAI is closely tied to complementary technical skills and organizational resources. |
| Keywords: | Generative AI, productivity, enterprise RBV, capabilities, machine learning |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ict:wpaper:2013/378272 |
| By: | Nwaobi, Godwin |
| Abstract: | As the most populous nation in Africa, Nigeria is uniquely positioned to reap the benefits of the emerging digital economy. And by accelerating access to digital technologies spurs innovation, efficiency and productivity which brings about choice and opportunities for greater growth and inclusion. Therefore, this research project shall provide evidence with respect to some aspects of inter-firm and intra-firm diffusion digital technologies in Nigeria. In other words, the proposed study intends to provide new empirical evidence with respect to the factors determining inter-firm and intra-firm diffusion of digital technologies by Nigeria productive enterprises. Furthermore, this research paper shall ascertain the extent to which patterns of digital adoption are different for domestic and foreign-owned firms. Econometrically, we propose to use a novel firm level (micro) panel data from the Nigerian manufacturing firms for the period between 2020 and 2025 as applicable. |
| Keywords: | Firms, diffusion, intrafirm, interfirm, Nigeria, panel data, probit model, digital technology, adoption, technology, enterprise, artificial intelligence, productivity, micro panel, innovations, digitalization |
| JEL: | C50 C55 C8 D20 D22 L0 L50 L60 L86 L96 O1 O14 O3 O31 O32 O33 O38 |
| Date: | 2024–10–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122392 |
| By: | Mr. Bas B. Bakker; Sophia Chen; Dmitry Vasilyev; Olga Bespalova; Moya Chin; Daria Kolpakova; Archit Singhal; Yuanchen Yang |
| Abstract: | Since 1980, income levels in Latin America and the Caribbean (LAC) have shown no convergence with those in the US, in stark contrast to emerging Asia and emerging Europe, which have seen rapid convergence. A key factor contributing to this divergence has been sluggish productivity growth in LAC. Low productivity growth has been broad-based across industries and firms in the formal sector, with limited diffusion of technology being an important contributing factor. Digital technologies and artificial intelligence (AI) hold significant potential to enhance productivity in the formal sector, foster its expansion, reduce informality, and facilitate LAC’s convergence with advanced economies. However, there is a risk that the region will fall behind advanced countries and frontier emerging markets in AI adoption. To capitalize on the benefits of AI, policies should aim to facilitate technological diffusion and job transition. |
| Keywords: | Artificial Intelligence (AI); Productivity Stagnation; Technological Innovation; Latin America; Caribbean; Economic Growth; Labor Productivity; Automation; Macroeconomic Impact; Digital Transformation; Cross-Country Analysis; Regional Development; Technology Adoption; Emerging Economies; Economic Policy. |
| Date: | 2024–10–11 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/219 |
| By: | Norbert Pfeifer; Robert Hill; Miriam Steurer |
| Abstract: | Increasing the energy efficiency of housing needs to be a key part of strategies to reach Net Zero carbon emissions by 2050. In this paper we measure the market incentives of owners to improve the energy efficiency of residential properties and how these incentives differ by location and property type. By linking sales records for England and Wales with their Energy Performance Certificates (EPCs), we create a merged micro-level dataset providing transaction price, physical and locational characteristics, energy performance, recommended energy efficiency improvements, and associated costs at the level of individual properties. We also construct a proxy for plot size using the exact geographic location and distances to neighbouring properties. We then estimate a hedonic model to predict the property price increases if all EPC recommendations were implemented. Our results reveal significant differences in market incentives across regions and property types. On average, we find that 84.4% of the costs of EPC-recommended energy efficiency improvements are capitalised in property prices for flats, as compared with 59.4% for semis/terraces and 59.3% for detached houses, although significant differences exist across regions. Subsidies targeted to regions and property types where market incentives are weakest could help reduce the cost of reaching Net Zero. |
| Keywords: | Energy Efficiency Improvements; Energy Performance Certificate; housing market |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-064 |
| By: | Marina Di Giacomo; Massimiliano Piacenza; Luca Salmasi (Università Cattolica del Sacro Cuore; Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore); Gilberto Turati (Università Cattolica del Sacro Cuore; Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore) |
| Abstract: | This paper provides a causal estimate of labor productivity in maternity units. We consider an Italian law that defines the staffing requirements of maternity wards according to the annual number of births. We exploit these discontinuities in the availability of medical staff induced by the law to define both instrumental variables and an RDD framework that allows us to estimate the causal effect of different teams of professionals during delivery on the health status of newborns and mothers. The analysis is based on detailed patient-level data on deliveries in an Italian region. We find that maternity units with annual births above the thresholds are more likely to have a “full team” of professionals during delivery. In turn, the presence of a full team significantly affects outcomes. We find an improvement in both neonatal and maternal outcomes, coupled with more intense use of medical procedures, suggesting that larger hospitals are better able to manage deliveries with appropriate treatments to avoid complications than smaller units. In addition, we do not find substantial heterogeneous effects across days of the week, time of day, and nationality of mothers. |
| Keywords: | medical staff; maternity wards; productivity; instrumental variables. |
| JEL: | D24 H75 I10 I12 I18 J24 J45 J82 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie1:def134 |
| By: | Dragan Crnogorac (School of Economics and Business, University of Ljubljana) |
| Abstract: | This paper investigates the determinants of carbon intensity across various economic sectors in the European Union, focusing on the period that already considers transition policies under the Paris Agreement and the Fit-for-55 initiative. As sectors exhibit diverging emission levels and transition policy implications, understanding the factors influencing carbon intensity has become increasingly relevant. We employ a panel regression analysis using data from 2014 to 2022, examining variables such as brown energy consumption share, total factor productivity, gross value added, employment metrics, energy prices, and environmental taxes. Our findings reveal that carbon intensity is influenced by a complex interplay of factors, with significant variations across sectors. Notably, sectors with high reliance on brown energy show a stronger correlation with carbon intensity levels. The results underscore the necessity for tailored transition policies that consider sector-specific characteristics to effectively reduce carbon emissions within the EU. Furthermore, the study highlights the importance of integrating economic and environmental policies to foster a sustainable transition, providing valuable insights for policymakers aiming to achieve climate targets. |
| Keywords: | Carbon Intensity, Economic Sectors, Panel Regression Analysis, GMM, EU Climate Policies, Fit-for-55 Initiative, Brown Energy Consumption, Total Factor Productivity, Sector-Specific Transition Policies |
| JEL: | Q50 C23 Q54 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14516478 |
| By: | Sergio Destefanis (University of Salerno); Giulia Nunziante (Sapienza University of Rome) |
| Abstract: | Developing upon Golden and Picci (2005) measure of corruption, we construct a comprehensive dataset for 20 Italian NUTS2 regions. This dataset includes measures of infrastructure in physical terms throughout 1987-2019 for thirteen intermediate categories and six main classes of assets, monetary measures of public capital stock (based on the PIM approach), spanning the 1890-2019 period, for nine asset classes, and a time-varying index of sectoral public expenditure efficiency throughout 1987-2019. Relevant novelties of the new dataset are its wide time range, and the availability of information for six asset classes, as well as for core, noncore and total infrastructure aggregates. An exploratory exercise investigates the impact of the new index of public spending efficiency on the effectiveness of cohesion policies upon GDP per capita. Our findings indicate that this index significantly influences the impact of national capital-account expenditures (especially national public investments) on GDP per capita. |
| Keywords: | Golden -Picci corruption index, physical measures of infrastructure, perpetual inventory method, cohesion policies |
| JEL: | O11 O43 R53 R58 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:ahy:wpaper:wp52 |
| By: | Cimini, Francesco; Kalantzis, Fotios |
| Abstract: | This study examines the impact of green and digital investments on the investment inefficiency level of European firms. We define investment inefficiency as the deviation from the optimal investment level, which depends on both the net present value (NPV) of the projects and the marginal benefit and cost of investment. Leveraging matched data from the European Investment Survey (EIBIS) and ORBIS, which results in a sample of 4, 892 firmyear observations from 27 European countries surveyed over the period 2021-2023, we employed a panel data regression model to estimate the effect of green and digital investments on investment inefficiency. Our analysis shows that both types of investments reduce investment inefficiency, particularly for under-investing firms. We also find evidence of a statistically significant interaction effect between green and digital investments for over-investing firms, suggesting that digital technologies can enhance the efficiency gains from green investments. Our results have important implications for policy makers and business managers who aim to foster the twin digital and green transition in Europe and improve their investment efficiency and competitiveness. |
| Keywords: | European Investment Bank Investment Survey, Investment Inefficiency, Green investment, Digital investment, Twin transition |
| JEL: | M41 G31 Q53 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:eibwps:304395 |
| By: | Diego Vallarino |
| Abstract: | This paper introduces a novel approach to optimizing portfolio rebalancing by integrating Graph Neural Networks (GNNs) for predicting transaction costs and Dijkstra's algorithm for identifying cost-efficient rebalancing paths. Using historical stock data from prominent technology firms, the GNN is trained to forecast future transaction costs, which are then applied as edge weights in a financial asset graph. Dijkstra's algorithm is used to find the least costly path for reallocating capital between assets. Empirical results show that this hybrid approach significantly reduces transaction costs, offering a powerful tool for portfolio managers, especially in high-frequency trading environments. This methodology demonstrates the potential of combining advanced machine learning techniques with classical optimization algorithms to improve financial decision-making processes. Future research will explore expanding the asset universe and incorporating reinforcement learning for continuous portfolio optimization. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.01864 |