nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2018‒02‒26
eleven papers chosen by

  1. Does market structure trigger efficiency? Evidence for the USA before and after the financial crisis By Halkos, George; Polemis, Michael
  2. Internationalisation, innovation and productivity in services: Evidence from Germany, Ireland and the United Kingdom By Peters, Bettina; Riley, Rebecca R.; Siedschlag, Iulia; Vahter, Priit; McQuinni, John
  3. Varying-coefficient panel data models with partially observed factor structure By Chaohua Dong; Jiti Gao; Bin Peng
  4. Cohesion Policy Meets Heterogeneous Firms By Lorendana Fattorini; Mahdi Ghodsi; Armando Rungi
  5. Will pollution taxes improve joint ecological and economic efficiency of thermal power industry in China? A DEA based materials balance approach By Ke Wang; Zhifu Mi; Yi-Ming Wei
  6. Productivity growth in Italy: a tale of a slow-motion change By Matteo Bugamelli; Francesca Lotti; Monica Amici; Emanuela Ciapanna; Fabrizio Colonna; Francesco D’Amuri; Silvia Giacomelli; Andrea Linarello; Francesco Manaresi; Giuliana Palumbo; Filippo Scoccianti; Enrico Sette
  7. AI, Labor, Productivity and the Need for Firm-Level Data By Robert Seamans; Manav Raj
  8. Disentangling Occupation- and Sector-specific Technological Change By Barany, Zsofia; Siegel, Christian
  9. Robustified expected maximum production frontiers By Daouia, Abdelaati; Florens, Jean-Pierre; Simar, Léopold
  10. Nonparametric estimation of international R&D spillovers By Georgios Gioldasis; Antonio Musolesi; Michel Simioni
  11. Multimarket Competition and Profitability: Evidence from Ukrainian banking By Tho Pham; Oleksandr Talavera; Junhong Yang

  1. By: Halkos, George; Polemis, Michael
    Abstract: This paper investigates the relationship between efficiency and market structure for a sample of industrial facilities dispersed among the USA states. In order to measure the relevant efficiency scores, we use a Data Development Analysis (DEA) allowing for the inclusion of desirable and undesirable (toxic chemical releases) outputs in the production function. In the next stage, we utilise the bootstrapped quantile regression methodology to uncover possible non-linear relationships between efficiency and competition at the mean and at various quantiles before and after the global financial crisis (2002 and 2012). In this way, we impose no functional form constraints on parameter values over the conditional distribution of the dependent variable (efficiency). At the same time, we estimate at which part of its conditional distribution function, the efficiency is located and draw substantial conclusions about the range of policy measures obtained. The empirical findings, indicate that the relationship between efficiency and market concentration did not remain unchanged in the aftermath of the economic crisis. The empirical results survived robustness checks under the inclusion of an alternative market concentration indicator (CR8).
    Keywords: Market concentration; Industrial Toxic Releases; Efficiency, Financial crisis; Bootstrapped quantile regression.
    JEL: C14 L1 Q52
    Date: 2018–02–09
  2. By: Peters, Bettina; Riley, Rebecca R.; Siedschlag, Iulia; Vahter, Priit; McQuinni, John
    Abstract: This paper examines the links between internationalisation, innovation and productivity in service enterprises. For this purpose, we use micro data from the Community Innovation Survey 2008 in Germany, Ireland and the United Kingdom, and estimate an augmented structural model. Our empirical evidence highlights the importance of internationalisation in the context of innovation outputs in all three countries. Our results indicate that innovation in service enterprises is linked to higher productivity. Among the innovation types that we consider, the largest productivity returns were found for marketing innovations.
    Keywords: internationalisation of services,innovation,productivity
    JEL: L25 O31
    Date: 2018
  3. By: Chaohua Dong; Jiti Gao; Bin Peng
    Abstract: In this paper, we study a varying–coefficient panel data model with nonstationarity, wherein a factor structure is adopted to capture different effects of time invariant variables over time. The methodology employed in this paper fills a gap of dealing with the mixed I(1)/I(0) regressors and factors in the literature. For comparison purposes, we consider the scenarios where the factors are either observable or unobservable, respectively. We propose an estimation method for both the unknown coefficient functions involved and the unknown factors before we establish the corresponding theory. We then evaluate the finite–sample performance of the proposed estimation theory through extensive Monte Carlo simulations. In an empirical study, we use our newly proposed model and method to study the returns to scale of large commercial banks in the U.S.. Some overlooked modelling issues in the literature of production econometrics are addressed.
    Keywords: Asymptotic theory, orthogonal series method, translog cost function, return to scale.
    JEL: C14 C23 D24
    Date: 2018
  4. By: Lorendana Fattorini (IMT School for advanced studies); Mahdi Ghodsi (The Vienna Institute for International Economic Studies); Armando Rungi (IMT School for advanced studies)
    Abstract: In this paper, we empirically test the effects of the EU ‘cohesion policy’ on the performance of about 500,000 European manufacturing firms after combining regional policy data at NUTS- 2 level with firm-level data. In a framework of heterogeneous firms and different absorptive capacity of regions, we show that financing of ‘cohesion policy’ by European Regional Development Fund (ERDF) aimed at direct investments in R&D correlates with improvement of firms’ productivity in a region. Conversely, funding designed at overall Business Support correlates with negative productivity growth rates. In both cases, we registered an asymmetric impact along the firms’ productivity distribution, where a stronger impact can be detected in the first quartile, i.e. less efficient firms in a region. We finally argue that considering the heterogeneity of firms allows a better assessment of the impact of ‘cohesion policy’ measures.
    Keywords: firm performance, total factor productivity, cross-country analysis, convergence, regional policy
    JEL: D22 D24 E23 F15 L25
    Date: 2018–02
  5. By: Ke Wang; Zhifu Mi; Yi-Ming Wei
    Abstract: Previous studies of the efficiency of Chinese electricity industry have been limited in providing insights regarding policy implications of inherent trade-offs of economic and environmental outcomes. This study proposes a modified data envelopment analysis method combined with materials balance principle to estimate ecological and cost efficiency in the Chinese electricity industry. The economic cost and ecological impact of energy input reallocation strategies for improving efficiency are identified. The possible impacts of pollution taxes upon the levels of sulfur dioxide (SO2) emissions are assessed. Estimation results show that (i) both energy input costs and SO2 could be reduced through increasing technical efficiency. (ii) It is possible to adjust energy input mix to attain ecological efficient, and correspondingly, SO2 would reduce by 15%. (iii) The Chinese electricity industry would reduce its unit cost by 9% if optimal ecological efficiency is attained and reduce its unit pollution by 13% if optimal cost efficiency is attained, implying that there are positive ecological synergy effects associated with energy cost savings and positive economic synergy effects associated with SO2 pollution reductions. (iv) Estimated shadow costs of SO2 reduction are very high, suggesting that, in the short term, the Chinese electricity industry should pursue cost efficient point instead of ecological efficient point, since alternative abatement activities are less costly and some of the abatement cost could be further offset by energy input cost savings. (v) There would be no significant difference between the impacts of pollution discharge fees and pollution taxes on SO2 emissions levels because of the relatively low pollution tax rate.
    Keywords: Data envelopment analysis (DEA); Emission reduction; Energy efficiency; Environmental economics; Material balance; Sulfur dioxide (SO2)
    JEL: Q54 Q40
    Date: 2018–02–21
  6. By: Matteo Bugamelli (Bank of Italy); Francesca Lotti (Bank of Italy); Monica Amici (Bank of Italy); Emanuela Ciapanna (Bank of Italy); Fabrizio Colonna (Bank of Italy); Francesco D’Amuri (Bank of Italy); Silvia Giacomelli (Bank of Italy); Andrea Linarello (Bank of Italy); Francesco Manaresi (Bank of Italy); Giuliana Palumbo (Bank of Italy); Filippo Scoccianti (Bank of Italy); Enrico Sette (Bank of Italy)
    Abstract: Productivity is the main factor holding back long-term economic growth in Italy. Since the second half of the 1990s, productivity growth has been feeble both by historical standards and compared with the other main euro area countries. Understanding the reasons for such a performance and finding the most effective policy levers is crucial to increase Italy’s potential growth rate. Against this background, we provide a detailed analysis of the data and a critical review of the available empirical evidence to identify both the structural weaknesses limiting productivity growth and the strengths of the Italian productive system that may support it looking forward. Since the end of the 1990s and more intensively since the second half of 2011, the reform effort has been particularly effective in the regulation of product and labor markets and industrial policy. On other factors which are very relevant for productivity dynamics, the reform action has been less effective so far.
    Keywords: productivity, growth, business dynamics, innovation, human capital, labor, finance, regulation, policies
    JEL: D0 E0 F0 G0 H0 J08 K0 L0
    Date: 2018–01
  7. By: Robert Seamans; Manav Raj
    Abstract: We summarize existing empirical findings regarding the adoption of robotics and AI and its effects on aggregated labor and productivity, and argue for more systematic collection of the use of these technologies at the firm level. Existing empirical work primarily uses statistics aggregated by industry or country, which precludes in-depth studies regarding the conditions under which robotics and AI complement or are substituting for labor. Further, firm-level data would also allow for studies of effects on firms of different sizes, the role of market structure in technology adoption, the impact on entrepreneurs and innovators, and the effect on regional economies amongst others. We highlight several ways that such firm-level data could be collected and used by academics, policymakers and other researchers.
    JEL: B4 O3 O4
    Date: 2018–01
  8. By: Barany, Zsofia; Siegel, Christian
    Abstract: To study the drivers of the employment reallocation across sectors and occupations between 1960 and 2010 in the US we propose a model where technology evolves at the sector-occupation cell level. Since the framework does not a priori impose a specific form of technological change, it allows us to quantify the respective role of sector-specific and of occupation-specific technological change. We implement a novel method to extract changes in sector-occupation cell productivities from the data. Using a factor model we find that occupation and sector factors jointly explain 74-87 percent of cell productivity changes, with the occupation component being by far the most important. While in our general equilibrium model both factors imply similar reallocations of labor across sectors and occupations, quantitatively the bias in technological change across occupations is much more important than the bias across sectors.
    Keywords: biased technological change; employment polarization; structural change
    JEL: J24 O33 O41
    Date: 2018–01
  9. By: Daouia, Abdelaati; Florens, Jean-Pierre; Simar, Léopold
    Abstract: The aim of this paper is to construct a robust nonparametric estimator for the production frontier. We study this problem under a regression model with one-sided errors where the regression function defines the achievable maximum output, for a given level of inputs-usage, and the regression error defines the inefficiency term. The main tool is a concept of partial regression boundary defined as a special probability-weighted moment. This concept motivates a robustified unconditional alternative to the pioneering class of nonparametric conditional expected maximum production functions. We prove that both the resulting benchmark partial frontier and its estimator share the desirable monotonicity of the true full frontier. We derive the asymptotic properties of the partial and full frontier estimators, and unravel their behavior from a robustness theory point of view. We provide numerical illustrations and Monte Carlo evidence that the presented concept of unconditional expected maximum production functions is more efficient and reliable in filtering out noise than the original conditional version. The methodology is very easy and fast to implement. Its usefulness is discussed through two concrete datasets from the sector of Delivery Services, where outliers are likely to affect the traditional conditional approach.
    Keywords: Boundary regression; Expected maximum; Nonparametric estimation; Production function; Robustnes
    Date: 2018–02
  10. By: Georgios Gioldasis (University of Ferrara); Antonio Musolesi (University of Ferrara); Michel Simioni (Institut National de la Recherche Agronomique (INRA))
    Abstract: In a recent paper, Ertur and Musolesi (Journal of Applied Econometrics 2017; 32: 477-503) employ the Common Correlated Effects (CCE) approach to address the issue of strong cross-sectional dependence while studying international technology diffusion. We carefully revisit this issue by adopting Su and Jin's (Journal of Econometrics 2012; 169: 34-47) method, which extends the CCE approach to nonparametric specifications. Our results indicate that the adoption of a nonparametric approach provides significant benefits in terms of predictive ability. This work also refines previous results by showing threshold effects, nonlinearities and interactions, which are obscured in parametric specifications and which have relevant policy implications.
    Date: 2018–03
  11. By: Tho Pham (School of Management, Swansea University); Oleksandr Talavera (School of Management, Swansea University); Junhong Yang
    Abstract: This paper examines the impact of non-price competition, indicated by multimarket contacts, on bank performance. Using a unique data set of Ukrainian banks’ branch locations, we construct three measures of multimarket linkages. We find that banks with a higher level of multimarket contacts are more likely to have higher financial performance. The findings support the mutual forbearance hypothesis: when banks compete in multiple markets, they have incentives to cooperate instead of competing aggressively. This cooperative incentive is induced by the familiarity and the similarity among multimarket competitors. The positive effect of multimarket competition on bank profitability is stronger when banks interact in more competitive markets. However, the anti-competitive effect of multimarket contacts is lessened following an exogenous shock to banks' branch networks. Banks that were more exposed to the shock experience worsened competitive positions and no longer benefited from multimarket contacts.
    Keywords: Banking, Multimarket competition, Multimarket contact, Mutual forbearance hypothesis, Profitability, Difference-in-differences, Political conflict.
    JEL: G21 L11 L25 L40
    Date: 2018–01–25

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