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on Efficiency and Productivity |
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Issue of 2022‒02‒07
nine papers chosen by |
| By: | Nigel Driffield (Warwick Business School, University of Warwick, The Productivity Institute) |
| Keywords: | Productivity, East Midlands, West Midlands |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:anj:ppaper:010&r= |
| By: | Owen Garling (Bennett Institute for Public Policy, University of Cambridge, The Productivity Institute) |
| Keywords: | Productivity, East Anglia |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:anj:ppaper:009&r= |
| By: | Matteo Lanzafame (Asian Development Bank) |
| Abstract: | This paper provides estimates of the impact of demographic change on labor productivity growth, relying on annual data over 1961-2018 for a panel f 90 advanced and emerging economies. We find that increases in both the young and old population shares have significantly negative effects on labor productivity growth, working via various channels – including physical and human capital accumulation. Splitting the analysis for advanced and emerging economies shows that population ageing has a greater effect on emerging economies than on advanced economies. Extending the benchmark model to include a proxy for the robotization of production, we find evidence indicating that automation reduces the negative effects unfavorable demographic change – in particular, population aging-on labor productivity. |
| Keywords: | Demographic Change, Labor Productivity, Robots |
| JEL: | C33 J11 O40 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2021.30&r= |
| By: | Yegbemey, Rosaine Nérice; Bensch, Gunther; Vance, Colin |
| Abstract: | Weather conditions are an important determinant of agricultural factor input, particularly labor allocation. The availability of weather forecasts can therefore lead to efficiency gains in the form of cost decreases and productivity increases. We test the practical feasibility, the uptake, and the effect of providing basic weather forecasts in the rainy season on the labor productivity of smallholder farmers. For this purpose, we conducted a Randomized Controlled Trial as a pilot with monthly data collections involving 331 farmers across six villages in north Benin. We find that most farmers subscribe to the intervention and report satisfaction with the service. The impact estimates indicate positive and economically significant intention-to-treat and local average treatment effects on labor productivity for maize and cotton cultivation. These findings suggest that weather-related information and mobile phone outreach help smallholder farmers to better adapt to changing weather. |
| Keywords: | Pilot field experiment,climate and weather information,labor productivity,smallholder farming,information technology,impact evaluation |
| JEL: | D13 O12 Q12 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:930&r= |
| By: | Schain, Jan Philip |
| Abstract: | This article analyzes the impact of institutional investors on firm productivity duringthe financial crisis 2008/09 across European manufacturing industries. Using propen-sity score matching combined with a difference in differences estimator I find a positivesignificant effect of 2% of foreign institutional ownership. Employing a variety of prox-ies for financial constraints, the article shows that the effect is driven by industries,countries, and firms that are more financially constrained indicating that foreign insti-tutional ownership prevents the known productivity slowdown during the financial crisisby alleviating financial constraints. |
| Keywords: | Institutional Investors,Financial Crisis,Productivity,Financial Constraints |
| JEL: | F61 G23 G32 G01 L25 D22 D24 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:dicedp:379&r= |
| By: | Victor Ajai (Energy Policy Research Group, Judge Business School, University of Cambridge); Karim Anaya (Energy Policy Research Group, Judge Business School, University of Cambridge); Geoffroy Dolphin (Energy Policy Research Group, Judge Business School, University of Cambridge); Michael Pollit (Energy Policy Research Group, Judge Business School, University of Cambridge) |
| Keywords: | Total factor productivity, growth accounting, regulation, energy networks, climate policy |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:anj:wpaper:016&r= |
| By: | Alessandri, Enrico (Bocconi University, Milan, and University of Urbino Carlo Bo, Urbino) |
| Abstract: | This paper uses patent citation networks to study technological change in the mining industry between 1970 and 2015. The analysis is undertaken at both the aggregate level by jointly considering all mining-related technological fields, and at the micro-level of patents in nine sub-fields, representing specific technological "sub-trajectories". Consistent with previous literature focused on other technological domains, we find that innovation patterns in the mining sector are "technology bounded", i.e. largely shaped by patenting activities carried out in a very limited range of mining technological fields, even though we detect a shift from exploration to environmental mining technologies (emergence of a new technological paradigm). In addition, we examine two aspects of technical change that have been largely disregarded in extant research: the geographical patterns of inventive activities and the role of key applicants in such patterns. We show that core mining patents and leading inventors involved originate almost exclusively from the US, so that trajectories appear to be heavily "geographically bounded", revealing that developing resource-abundant countries lag behind the technological frontier in mining. Moreover, only a few applicant firms are responsible for most inventive activities reflecting a highly concentrated oligopolistic structure, hence characterising trajectories as "applicant bounded". Similar results are observed at the level of sub-trajectories, although with some relevant exceptions, hence suggesting that a substantial heterogeneity exists within the industry and across mining-related technologies. |
| Keywords: | Technological trajectories, Technological sub-trajectories, Mining technologies, Geography of innovation, Patents, International technological frontier |
| JEL: | O31 L72 F23 R11 |
| Date: | 2021–12–08 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2021048&r= |
| By: | Pierre-Philippe Combes (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR); Gilles Duranton (University of Pennsylvania [Philadelphia]); Laurent Gobillon (PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | We propose a new nonparametric approach to estimate the production function for housing. Our estimation treats output as a latent variable and relies on a first-order condition for profit maximization combined with a zero-profit condition. More desirable locations command higher land prices and, in turn, more capital to build houses. For parcels of a given size, we compute housing production by summing across the marginal products of capital. For newly built single-family homes in France, the production function for housing is close to constant returns and is well, though not perfectly, approximated by a Cobb-Douglas function with a capital elasticity of 0.65. |
| Keywords: | Housing,Production function |
| Date: | 2021–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:pseptp:halshs-03342578&r= |
| By: | Paulo Rotella Junior (Department of Production Engineering, Federal University of Paraiba, Brazil & Department of Management, Federal Institute of Education, Science and Technology - North of Minas Gerais, Brazil & Faculty of Finance and Accounting, Prague University of Economics and Business, Czech Republic & Faculty of Social Sciences, Charles University, Czech Republic); Luiz Celio Souza Rocha (Department of Management, Federal Institute of Education, Science and Technology - North of Minas Gerais, Brazil); Rogerio Santana Peruchi (Department of Production Engineering, Federal University of Paraiba, Brazil); Giancarlo Aquila (IEPG, Federal University of Itajuba, Brazil); Karel Janda (Faculty of Finance and Accounting, Prague University of Economics and Business, Czech Republic & Faculty of Social Sciences, Charles University, Czech Republic); Edson de Oliveira Pamplona (Institute of Production and Management Engineering, Federal University of Itajuba, Brazil) |
| Abstract: | This article presents a new approach for building robust portfolios based on stochastic efficiency analysis and periods of market downturn. The empirical analysis is done on assets traded on the Brazil Stock Exchange, B3 (Brasil, Bolsa, Balcao). We start with information on the assets from periods of market downturn (worst-case) and we group them using hierarchical clustering. Then we do stochastic efficiency analysis on these data using the Chance Constrained Data Envelopment Analysis (CCDEA) model. Finally, we use a classical model of capital allocation to obtain the optimal share of each asset. Our model is able to accommodate investors who exhibit different risk behaviors (from conservatives to risky investors) by varying the level of probability in fulfilling the constraints (1-αi) of the CCDEA model. We show that the optimal portfolios constructed with the use of information from periods of market downturns perform better for the Sharpe ratio (SR) in the validation period. The combined use of these approaches, using also fundamentalist variables and information on market downturns, allows us to build robust portfolios, with higher cumulative returns in the validation period, and portfolios with lower beta values. |
| Keywords: | Robust optimization, Stochastic evaluation, Chance Constrained DEA, Worst-case markets, Portfolios |
| JEL: | G11 G14 C38 C61 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2022_03&r= |