nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2022‒05‒23
seven papers chosen by

  1. Russia's growth potential post-COVID-19 By Korhonen, Iikka
  2. JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality By Luca Coraggio; Marco Pagano; Annalisa Scognamiglio; Joacim Tåg
  3. Are fiscal consolidation episodes helpful for public sector efficiency? By António Afonso; José Alves
  4. Approximations and Inference for Nonparametric Production Frontiers By Cinzia Daraio; Leopold Simar
  5. Accounting for the slowdown in output growth after the Great Recession: A wealth preference approach By Kazuma Inagaki,; Yoshiyasu Ono; Takayuki Tsuruga
  6. Nonparametric Multiple-Output Center-Outward Quantile Regression By Eustasio del Barrio; Alberto González-Sanz; Marc Hallin
  7. Nobody's gonna slow me down? The effects of a transportation cost shock on firm performance and behavior By Branco, Catarina; Dohse, Dirk; dos Santos, João Pereira; Tavares, José

  1. By: Korhonen, Iikka
    Abstract: This paper updates my earlier calculations on Russia's long-run growth potential using a standard growth accounting framework in which GDP growth depends on available labor, capital and efficiency in combining them, i.e. total factor productivity. Russia's economy has grown relatively slowly during the past decade, partly because of declining labor force. In my revised framework, growth recovers after the negative COVID-19 shock, but remains subdued as the working-age population continues to dwindle. Productivity growth remains lower than in the early 2000s, while average GDP growth settles at approximately 1.5% p.a.
    Date: 2021
  2. By: Luca Coraggio (University of Naples Federico II); Marco Pagano (University of Naples Federico II and EIEF); Annalisa Scognamiglio (University of Naples Federico II); Joacim Tåg (Research Institute of Industrial Economics (IFN))
    Abstract: Does the matching between workers and jobs help explain productivity differentials across firms? To address this question we develop a job-worker allocation quality measure (JAQ) by combining employer-employee administrative data with machine learning techniques. The proposed measure is positively and significantly associated with labor earnings over workers’ careers. At firm level, it features a robust positive correlation with firm productivity, and with managerial turnover leading to an improvement in the quality and experience of management. JAQ can be constructed for any employer-employee data including workers’ occupations, and used to explore the effect of corporate restructuring on workers’ allocation and careers.
    Date: 2022
  3. By: António Afonso; José Alves
    Abstract: We assess the consequences of fiscal consolidation episodes on public sector efficiency (scores) for 35 OECD countries for the 2007-2020 period. We find that fiscal consolidations improve public sector efficiency and results are robust across efficiency models. Moreover, peripheral euro-area economies and economies with debt-to-GDP ratios between 60% and 90% are those whose public sector efficiency scores improve more when fiscal consolidation episodes occur. The evidence that fiscal consolidations enhance spending efficiency is an additional argument for fiscal consolidations, from a policy perspective.
    Keywords: fiscal consolidation episodes; government spending efficiency; panel data; OECD
    JEL: C23 D61 H21 E62 H63
    Date: 2022–05
  4. By: Cinzia Daraio; Leopold Simar
    Abstract: Nonparametric methods have been widely used for assessing the performance of organizations in the private and public sector. The most popular ones are based on envelopment estimators, like the FDH or DEA estimators, that estimate the attainable sets and its efficient boundary by enveloping the cloud of observed units in the appropriate input-output space. The statistical properties of these flexible estimators have been established. However these nonparametric techniques do not allow to make sensitivity analysis of the production outputs to some particular inputs, or to infer about marginal products and other coefficients of economic interest. On the contrary, parametric models for production frontiers allow richer and easier economic interpretation but at a cost of restrictive assumptions on the data generating process. In addition, the latter rely mostly on regression methods fitting the center of the cloud of observed points. In this paper we offer a way to avoid these drawbacks and provide approximations of these coefficients of economic interest by “smoothing†the popular nonparametric estimators of the frontiers. Our approach allows to handle fully multivariate cases. We describe the statistical properties for both the full and the partial (robust) frontiers. We consider parametric but also flexible approximations based on local linear tools providing local estimates of all the desired partial derivatives and we show how to deal with environmental factors. An illustration on real data from European Higher Education Institutions (HEI) shows the usefulness of the proposed approach.
    Keywords: Nonparametric production frontiers; DEA; FDH; partial frontiers; directional distances; linear approximations; local linear approximations.
    Date: 2022–05–10
  5. By: Kazuma Inagaki,; Yoshiyasu Ono; Takayuki Tsuruga
    Abstract: Previous studies have argued that output growth in advanced economies declined during the Great Recession and remained low afterward. This paper proposes a model to explain this slowdown in output growth. We incorporate wealth preferences and downward nominal wage rigidity into a standard monetary growth model. Our model demonstrates that output initially grows at the same rate as productivity and slows endogenously in the transition path to the stagnation steady state. This stagnation is persistent even if productivity continues to grow at a steady rate. Applying our model to US data, we show that it successfully explains the declines observed in the real interest rate, inflation, and the velocity of money, along with the slowdown in output growth.
    Date: 2022–05
  6. By: Eustasio del Barrio; Alberto González-Sanz; Marc Hallin
    Abstract: Based on the novel concept of multivariate center-outward quantiles introduced recently in Chernozhukov et al. (2017) and Hallin et al. (2021), we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested conditional center-outward quantile regression contours and regions with given conditional probability content irrespective of the underlying distribution; their graphs constitute nested center-outward quantile regression tubes. Empirical counterparts of these concepts are constructed, yielding interpretable empirical regions andcontours which are shown to consistently reconstruct their population versions in the Pompeiu-Hausdorff topology. Our method is entirely non-parametric and performs well in simulations including heteroskedasticity and nonlinear trends; its power as a data-analytic tool is illustrated on some real datasets.
    Keywords: Multiple-output regression, Center-outward quantiles, Optimal transport
    Date: 2022–05
  7. By: Branco, Catarina; Dohse, Dirk; dos Santos, João Pereira; Tavares, José
    Abstract: This paper takes a deep and comprehensive look into the firm-level behavioral reactions to a massive transportation cost shock. Exploiting rich data encompassing the universe of Portuguese private firms and a natural experiment we find that the introduction of tolls on previously toll-free highways caused a substantial decrease of turnover and firm profits. In response to the tolls, firms reduced expenses, cutting employment-related expenses and purchases of other inputs in a similar magnitude. Labor costs were reduced by employment cuts rather than by wage cuts. We find evidence for increased firm exit in treated municipalities, but not for increased re-location.
    Keywords: road tolls,infrastructure,firm performance,firm behavior,location,Portugal
    JEL: R48 L25 R12
    Date: 2022

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