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on Efficiency and Productivity |
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Issue of 2023‒03‒27
eleven papers chosen by |
| By: | Emir Malikov; Shunan Zhao |
| Abstract: | We develop a novel methodology for the proxy variable identification of firm productivity in the presence of productivity-modifying learning and spillovers which facilitates a unified "internally consistent" analysis of the spillover effects between firms. Contrary to the popular two-step empirical approach, ours does not postulate contradictory assumptions about firm productivity across the estimation steps. Instead, we explicitly accommodate cross-sectional dependence in productivity induced by spillovers which facilitates identification of both the productivity and spillover effects therein simultaneously. We apply our model to study cross-firm spillovers in China's electric machinery manufacturing, with a particular focus on productivity effects of inbound FDI. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.14602&r=eff |
| By: | Anderton, Robert; Botelho, Vasco; Reimers, Paul |
| Abstract: | Is digitalisation a massive gamechanger which will deliver huge gains in productivity, or is it more of a sideshow with only limited impacts? We use a large balance sheet panel dataset comprising more than 19 million European firm-level observations to empirically investigate the impact of digitalisation on productivity growth via various previously unexplored chan-nels and mechanisms. Our results suggest that for two otherwise identical firms, the firm that exhibits on average a higher share of investment in digital technologies will exhibit a faster rate of TFP growth, but not all firms and sectors experience significant productivity gains from digitalisation. Digitalisation does not seem to have relatively stronger impacts on the productivity of frontier firms compared to laggards, nor does it help to turn laggards into frontier firms. Overall, firms should not regard digital investment as a ‘one-size-fits-all’ strategy to improve their productivity. Digital technologies are a gamechanger for some firms. But they seem more like a sideshow for most firms, who attempt to be increasingly digital but are not able to adequately reap its productivity gains. JEL Classification: D22, D24, D25, O33 |
| Keywords: | digital technology/transition, productivity growth, technology adoption/diffusion |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20232794&r=eff |
| By: | Diego A. Comin; Javier Quintana; Tom G. Schmitz; Antonella Trigari |
| Abstract: | We compute new estimates for Total Factor Productivity (TFP) growth in five European countries and in the United States. Departing from standard methods, we account for positive profits and use firm surveys to proxy for unobserved changes in factor utilization. These novelties have a major impact in Europe, where our estimated TFP growth series are less volatile and less cyclical than the ones obtained with standard methods. Based on our approach, we provide annual industry-level and aggregate TFP series, as well as the first estimates of utilization-adjusted quarterly TFP growth in Europe. |
| JEL: | E01 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31006&r=eff |
| By: | CHANG Pao-Li; MAKIOKA Ryo; NG Bo Lin; YANG Zhenlin |
| Abstract: | This paper proposes a three-stage GMM estimation procedure for estimating firm-level productivity in the presence of potential spatial dependence across firms via the product market, the input market, and the supply chain. The procedure builds on Ackerberg, Caves and Frazer (2015) and Wooldridge (2009), but in addition, allows the productivity process to depend on the lagged output levels and input usages of related firms, and to accommodate spatially correlated productivity shocks across firms. The procedure provides the estimates of the production function parameters (the capital and labor shares in value-added, and the degree of serial correlation in the productivity process), and the spatial dependence parameters (of productivity on related firms’ past outputs and inputs, and current innovation shocks), where the set of related firms can differ across the three dimensions of spatial dependence. We establish the asymptotic properties of the proposed estimator, and conduct Monte Carlo simulations to validate these properties. In particular, our proposed estimator is consistent under DGPs with or without spatial dependence. In contrast, the conventional estimators are biased when the true DGPs are indeed characterized by spatial dependence. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:23016&r=eff |
| By: | Emir Malikov; Shunan Zhao; Jingfang Zhang |
| Abstract: | There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This paper extends Gandhi et al.'s (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, our 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, we achieve point identification by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. We also show how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.13429&r=eff |
| By: | Mons Chan; Amil Petrin; Frederic Warzynski |
| Abstract: | In this paper we provide a methodology that jointly studies production and demand for multi-product firms using detailed firm-product level data from Denmark. We estimate marginal cost by combining production function estimation with a cost function that allows for quasi-fixed inputs. We use a discrete choice demand model that extends insights from Berry, Levinsohn and Pakes (1995) to obtain a measure of the demand shock (quality). We estimate the relationship between product (process) R&D and quality (efficiency), and find strong evidence that process innovation is related to higher efficiency, while product innovation is associated with higher product quality. We discuss the welfare implications of these two distinct innovation activities. |
| JEL: | L1 |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30950&r=eff |
| By: | Dominique Desbois (UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | This presentation uses symbolic data classification to explore the similarities between distributions of conditional quantile estimates, applying it to the problem of specific cost allocation in agriculture. After recalling the conceptual framework of agricultural production cost estimation, the first part presents the empirical model, the quantile regression approach and the interval data classification technique used. The second part presents the comparative analysis between twelve European Member States of the results of the hierarchical divisive classification of estimation intervals, applied to the estimation of fertiliser costs. |
| Abstract: | Cette communication utilise la classification des données symboliques pour explorer les similitudes entre distributions d'estimations quantiles conditionnelles, en l'appliquant au problème de l'allocation des coûts spécifiques en agriculture. Après avoir rappelé le cadre conceptuel de l'estimation des coûts de production agricole, la première partie présente le modèle empirique, l'approche de régression quantile et la technique de classification des données d'intervalle utilisée. La seconde partie présente l'analyse comparative entre douze États membres européens des résultats issus de la classification hiérarchique divisive des intervalles d'estimation, appliquée à l'estimation du coût des fertilisants. |
| Keywords: | Clustering Methods, agricultural production costs : distribution of estimates |
| Date: | 2022–09–14 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03959713&r=eff |
| By: | ISHIKAWA Takayuki |
| Abstract: | Physical investment in Japan has weakened since the bubble burst. The U.S. and other advanced countries have also experienced a slump in physical investment since the global financial crisis. Following Crouzet and Everly (2018), we examine whether the slump in physical investment in Japan shifted to investment in intangible assets, as in other advanced countries. Using firm-level data, this study estimates a Tobin’s Q-type investment function for tangible assets. Then, using these estimation results, we examine the extent to which the shift to investment in intangible assets has been a factor in Japan’s weak investment in tangible assets. Our estimation results confirm that Research and Development (R&D) investment significantly explains weak physical investments. However, R&D can explain only part of the slump in physical investment. The results suggest that, unlike in other countries, not only intangible investment but also aggressive physical investment is essential for productivity improvement in Japan. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:eti:dpaper:23008&r=eff |
| By: | Daron Acemoglu; Hans R. A. Koster; Ceren Ozgen |
| Abstract: | We estimate the effects of robot adoption on firm-level and worker-level outcomes in the Netherlands using a large employer-employee panel dataset spanning 2009-2020. Our firm-level results confirm previous findings, with positive effects on value added and hours worked for robot-adopting firms and negative outcomes on competitors in the same industry. Our worker-level results show that directly-affected workers (e.g., blue-collar workers performing routine or replaceable tasks) face lower earnings and employment rates, while other workers indirectly gain from robot adoption. We also find that the negative effects from competitors' robot adoption load on directly-affected workers, while other workers benefit from this industry-level robot adoption. Overall, our results highlight the uneven effects of automation on the workforce. |
| JEL: | D63 E22 E23 E24 J24 O33 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31009&r=eff |
| By: | Manh-Hung Nguyen (TSE-R - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Chon van Le (VNU-HCM - Vietnam National University - Ho Chi Minh City); Scott Atkinson (University of Georgia [USA]) |
| Abstract: | The paper investigates the production inefficiency of the US electricity industry in the wake of restructuring and emission reduction regulations. |
| Keywords: | Technical inefficiency, Electricity industry, Restructuring, Emissions |
| Date: | 2022–11–24 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03980818&r=eff |
| By: | Ghulam Mustafa (Pakistan Institute of Development Economics); Bashir Khan (Deputy Director, HEDR, Higher Education Commission (HEC), Islamabad) |
| Abstract: | The available literature establishes that the incentive theory of motivation[2] has significantly influenced the desired outcomes in public sector organizations, and institutes. The achievement of the desired outcomes chiefly depends on the way incentives are being offered (e, g.; Killeen, 1985; Burgess and Ratto, 2003; Fehr et al., 2013; Fall and Roussel, 2014; Cassar and Meier, 2018; Itri et al., 2019). Believing on the outcomes of the incentive theory of motivation, the Higher Education Commission (HEC) introduced a monetary incentive-based mode of appointment against the Basic Pay Scale (BPS) to hire the teaching faculty by public sector universities in Pakistan. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:pid:kbrief:2022:86&r=eff |