nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2022‒07‒25
fifteen papers chosen by
Angelo Zago
Università degli Studi di Verona

  1. The Determinants of Total Factor Productivity Growth in Pakistan: An Exploration By Omer Siddique
  2. U.S. agricultural productivity growth: measurement, trends, and drivers (1948-2019) By Wang, Sun Ling; Mosheim, Roberto; Njuki, Eric; Nehring, Richard
  3. The contribution of industrial robots to labor productivity growth and economic convergence: A production frontier approach By Andreas Eder; Wolfgang Koller; Bernhard Mahlberg
  4. Productive Efficiency Analysis with Incomplete Output Information By Laurens Cherchye; Bram De Rock; Dieter Saelens; Marijn Verschelde
  5. A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application By Juan Jos´e Price; Arne Henningsen
  6. Foreign direct investment, information technology and total factor productivity dynamics in Sub-Saharan Africa By Asongu, Simplice A; Odhiambo, Nicholas M
  7. The Outlook for U.S. Agriculture – 2022: New Paths to Sustainability and Productivity Growth By Meyer, Seth
  8. Genetically Modified Organisms and Agricultural Productivity By Robert G Chambers; Yu Sheng
  9. Impacts of (individual and aggregate) productivity and credit shocks on equilibrium aggregate production By Ngoc-Sang Pham
  10. Gender and age diversity. Does it matter for firms’ productivity By Laetitia Challe; Fabrice Gilles; Yannick L'Horty; Ferhat Mihoubi
  11. Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a crosssectionally dependent panel framework By Antonio Musolesi; Giada Andrea Prete; Michel Simioni
  12. Privatization's Influence on Agglomeration and Selection Effects: Evidence from China's Manufacturing Industry By Yikai Zhao; Jun Nagayasu
  13. What drove the profitability of colonial firms?: Labour coercion and trade preferences on the Sena Sugar Estates (1920-74) By Sam Jones; Peter Gibbon
  14. Hedge Fund Performance: A Quantitative Survey By Fan Yang; Tomas Havranek; Zuzana Irsova; Jiri Novak
  15. Revisiting the Porter Hypothesis: A Nonparametric Analysis on the impact of Pollution Abatement Technologies on firms' performances By Davide Golinelli

  1. By: Omer Siddique (Pakistan Institute of Development Economics)
    Abstract: Total factor productivity (TFP), also known as multifactor productivity, is that part of the GDP that cannot be attributed to factor inputs, including labour, capital, human capital, and materials. TFP essentially tells us how productively economies use factor inputs. Some economies produce more output with the same inputs, while others produce less. Therefore, an increase in TFP growth is essential for long-run sustained growth. Economic theory and evidence also point in the same direction.
    Keywords: Productivity, Growth, Pakistan, Exploration,
    Date: 2022
  2. By: Wang, Sun Ling; Mosheim, Roberto; Njuki, Eric; Nehring, Richard
    Keywords: Productivity Analysis, Production Economics
    Date: 2022–02
  3. By: Andreas Eder (Institute for Industrial Research, Mittersteig 10/4, 1050 Vienna, Austria; University of Natural Resources and Life Sciences, Department of Economics and Social Sciences, Institute for Sustainable Economic Development, Feistmantelstrasse 4, 1180, Vienna, Austria); Wolfgang Koller (Institute for Industrial Research, Mittersteig 10/4, 1050 Vienna, Austria); Bernhard Mahlberg (Institute for Industrial Research, Mittersteig 10/4, 1050 Vienna, Austria; Vienna University of Economics and Business, Welthandelsplatz 1, 1020 Vienna, Austria)
    Abstract: This paper investigates the contribution of industrial robots to labor productivity growth and the process of economic convergence in 19 developed and 17 emerging countries in the period 1999 to 2019. To answer our research questions, we extend the non-parametric production frontier framework by considering industrial robots as a separate production factor. Production frontiers and distances to the frontiers are estimated by Data Envelopment Analysis, a method based on linear programming models. Considerable contributions of robotization to labor productivity growth are mainly found in emerging countries and are rather modest in most developed countries. In the period 2009 to 2019 robot capital deepening as a source of productivity growth has gained in importance in emerging countries but not in developed countries. Within the period 1999 to 2019 we find some evidence of i) unconditional β-convergence, ii) a reduction in the dispersion of productivity levels across economies (σ- convergence) and iii) a depolarization (shift from bimodal to unimodal distribution) of the labor productivity distribution. Non-robot physical capital deepening and robotization are the most important drivers of β-convergence. Robot capital deepening contributed to the depolarizationof the labor productivity distribution and to σ-convergence. Though, the effect of robot capital deepening on the entire shift of the labor productivity distribution between 1999 and 2019 is modest and dominated by other growth factors such as technological change and non-robot physical capital deepening.
    Keywords: automation, robotization, decomposition, data envelopment analysis, emerging countries, developed countries
    JEL: E24 O33 O47
  4. By: Laurens Cherchye; Bram De Rock; Dieter Saelens; Marijn Verschelde
    Abstract: We present a novel DEA-type method to evaluate the productive efficiency of DMUs when the empirical analyst has incomplete output information. Our method builds on the Afriat Theorem that was originally proposed in the context of consumer analysis. We translate this result to a production setting and show that it provides a productive basis for cost efficiency analysis in the absence of output information. Our method is versatile in that it can accommodate a continuum of instances characterized by incomplete information on output quantities. We illustrate its practical usefulnessthrough an empirical application that evaluates the productive efficiency performance of countries.
    Keywords: efficiency measurement, nonparametric production analysis, incomplete output information, Afriat Theorem
    Date: 2022–06
  5. By: Juan Jos´e Price (Business School, Universidad Adolfo Ib´a˜nez); Arne Henningsen (Dept. of Food and Resource Economics, University of Copenhagen)
    Abstract: We derive and empirically apply an input-oriented distance function based on the stochastic ray production function suggested by L¨othgren (1997, 2000). We show that the derived ray-based input distance function is suitable for modeling production technologies based on logarithmic functional forms (e.g., Cobb-Douglas and Translog) when control over inputs is greater than control over outputs and when some productive entities do not produce the entire set of outputs — two situations that are jointly present in various economic sectors. We also address a weakness of the stochastic ray function,namely its sensitivity to the outputs’ ordering, by using a model-selection approach and a model-averaging approach. We estimate a ray-based Translog input distance function with a data set of Danish museums. These museums have more control over their inputs than over their outputs, and many of them do not produce the entire set of outputs that is considered in our analysis. Given the importance of monotonicity conditions in efficiency analysis, we demonstrate how to impose monotonicity on ray-based input distance functions. As part of the empirical analysis, we estimate technical efficiencies,distance elasticities of the inputs and outputs, and scale elasticities and establish how the production frontier is affected by some environmental variables that are of interest to the museum sector.
    Keywords: Stochastic ray production frontier, distance function, input-oriented efficiency, zero output quantities, model averaging, monotonicity, museums.
    JEL: C51 D22 D24
  6. By: Asongu, Simplice A; Odhiambo, Nicholas M
    Abstract: Compared to other regions of the world, the potential for information technology penetration in sub-Saharan Africa (SSA) is very high. Unfortunately, productivity levels in the region are also very low. This study investigates the importance of information technology in influencing the effect of foreign direct investment (FDI) on total factor productivity (TFP) dynamics. The focus is on 25 countries in SSA. Information technology is measured with mobile phone penetration and internet penetration, while the engaged TFP productivity dynamics are TFP, real TFP, welfare TFP, and real welfare TFP. The empirical evidence is based on the Generalised Method of Moments. The findings show that, with the exception of regressions pertaining to real TFP growth for which the estimations do not pass post-estimation diagnostic tests, it is apparent that information technology (i.e. mobile phone penetration and internet penetration) modulate FDI to positively influence TFP dynamics (i.e. TFP, welfare TFP, and welfare real TFP). Policy and theoretical implications are discussed.
    Keywords: Productivity; Foreign Investment; Information Technology; Sub-Saharan Africa
    Date: 2022–02
  7. By: Meyer, Seth
    Keywords: Crop Production/Industries, Environmental Economics and Policy
    Date: 2022–02
  8. By: Robert G Chambers (University of Maryland); Yu Sheng (Peking University)
  9. By: Ngoc-Sang Pham (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie, EM Normandie - École de Management de Normandie)
    Abstract: In a market economy, the aggregate production level depends not only on the aggregate variables but also on the distribution of individual characteristics (e.g., productivity, credit limit, ...). We point out that, due to financial frictions the equilibrium aggregate production may be non-monotonic in both individual productivity and credit limit. By consequence, the emergence of some firms (for example, improving productivity or relaxing credit limit) may not necessarily be beneficial to economic development.
    Keywords: Productivity shock,Financial shock,Credit constraint,Heterogeneity,Productivity dispersion,Distributional effects,Efficiency,General equilibrium
    Date: 2022–06–02
  10. By: Laetitia Challe; Fabrice Gilles; Yannick L'Horty; Ferhat Mihoubi
    Date: 2022
  11. By: Antonio Musolesi (University of Ferrara, SEEDS - Sustainability Environmental Economics and Dynamics Studies (Università degli Studi di Ferrara)); Giada Andrea Prete (University of Ferrara); Michel Simioni (UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement, TSE - 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)
    Abstract: This paper provides a broad replication of Calderón et al. (2015). We address some complex and relevant issues, namely functional form, non-stationary variables and cross-sectional dependence. In particular, by adopting the CCE framework, we consider both parametric-static and dynamic-and non-parametric specications, thus allowing for dierent degrees of exibility. Contrary to Calderón et al. (2015), we nd a lack of signicance of the infrastructure index, with an estimated elasticity very close to zero for all estimates. Moreover, by employing the data-driven model selection procedure proposed by Gioldasis et al. (2021), it is found that non-parametric specications provide the best predictive performance and that CCE models always overperform with respect to traditional panel data methods that employ cross-sectional demeaning to account for cross-sectional dependence.
    Keywords: Cross-sectional dependence,Factor models,Moving block bootstrap,Non-parametric regression,Spline functions,Public capital hypothesis.
    Date: 2022–06–02
  12. By: Yikai Zhao; Jun Nagayasu
    Abstract: We study the impact of state-owned enterprises'(SOE) privatization on how firm productivity responds to agglomeration and selection effects, and investigate whether and how policymakers can utilize agglomeration and selection to benefit from privatization. As SOEs enjoy privileged treatment because of their government ties, we argue that the agglomeration advantages of SOEs are rooted in their connection with local governments who regulate them, who share local information with surrounding SOEs, such as labor markets, resources, and tacit knowledge. Overall, we attempt to answer the following questions: 1) Will the SOEs f reform negatively (positively) influence enterprises' agglomeration (selection) effects? 2) To what extent is this influence affected by the local government? 3) Is this adverse or favorable impact heterogeneous?
    Date: 2022–07
  13. By: Sam Jones; Peter Gibbon
    Abstract: The magnitude of returns to colonial-era investments in Africa has been addressed in an extensive literature, as have the nature and legacies of extractive colonial institutions. However, the link between these institutions and the profitability of firms remains unclear. We reconstruct the annual financial records of Sena Sugar Estates in Portuguese East Africa (today's Mozambique) over the period 1920-74 to probe the contributions of forced labour and preferential trade arrangements to the performance of the firm.
    Keywords: Mozambique, Sugarcane, Forced labour, Trade preferences, Colonialism, Profitability
    Date: 2022
  14. By: Fan Yang (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic); Tomas Havranek (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic & CEPR); Zuzana Irsova (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic); Jiri Novak (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)
    Abstract: We provide the first quantitative survey of the empirical literature on hedge fund per- formance. We examine the impact of potential biases on the reported results. Empirical analysis in prior studies has been plagued by fragmentation of underlying data and by lim- ited consensus on how hedge fund performance should be measured. Using a sample of 1,019 intercept terms from regressions of hedge fund returns on risk factors (the "alphas") collected from 74 studies published between 2001 and 2021 we show that inferences about hedge fund returns are not significantly contaminated by publication selection bias. Most of our monthly alpha estimates adjusted for the (small) bias fall within a relatively narrow range of 30 to 40 basis points. Considering several partitions of our sample, we document a modest publication bias only for estimates based on instrumental variables (IV), for which relatively large standard errors are common and that tend to be less precise. In contrast, studies that explicitly control for the potential biases in the underlying data (e.g. the back- filling bias and the survivorship bias) report lower alphas. Our results demonstrate that despite the prevalence of the publication selection bias in numerous other research settings, publication may not be selective when there is no strong a priori theoretical prediction about the sign of estimated coefficients, which may induce greater readiness to publish statistically insignificant results.
    Keywords: Hedge funds, meta-analysis, publication bias
    JEL: J23 J24 J31
    Date: 2022–06
  15. By: Davide Golinelli (University of Ferrara – Department of Economics and Management (Ferrara, Italy);)
    Abstract: Nonparametric regression models are designed to relax the Gauss-Markov assumptions needed to obtain an unbiased and consistent estimator from the traditional parametric regression. The rationale is to let the function be defined by the data locally, without imposing a linear relationship or higher orders polynomials to fit possible non-linearity, at global level. This paper has the aim to investigate the Porter Hypothesis with the use of nonparametric analysis using kernel regression, in particular the local constant estimator developed by Nadaraya and Watson (1955; 1956) and the linear extension proposed by Stone (1977) and Cleveland (1979). The use of kernel to deal with discrete variables is extremely useful to study the effect of the introduction of pollution abatement technologies, used as a proxy assessing for policy stringency, over the value added and hence to test the effect of regulations on firm’s performances: in doing so, starting from the estimator designed by Aitchinson and Aitkens (1976), the extension proposed by Li and Racine (2007) is used. The nonparametric analysis provides a model with a better goodness-of-fit, furthermore the value of the bandwidth referred to the introduction of pollution abatement technology obtained through Kullback-Leibler cross-validation, underlines heterogeneity between groups, and suggesting the positive effect of the introduction of environmental regulation on the performance of firms, leading to the so called Porter hypothesis.
    Date: 2022–07

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